Articoli di riviste sul tema "Species distribution modelling (SDM)"
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1
Kamino, Luciana H. Y., João Renato Stehmann, Silvana Amaral, Paulo De Marco, Thiago F. Rangel, Marinez F. de Siqueira, Renato De Giovanni e Joaquín Hortal. "Challenges and perspectives for species distribution modelling in the neotropics". Biology Letters 8, n. 3 (26 ottobre 2011): 324–26. http://dx.doi.org/10.1098/rsbl.2011.0942.
Abstract (sommario):
The workshop ‘ Species distribution models: applications, challenges and perspectives ’ held at Belo Horizonte (Brazil), 29–30 August 2011, aimed to review the state-of-the-art in species distribution modelling (SDM) in the neotropical realm. It brought together researchers in ecology, evolution, biogeography and conservation, with different backgrounds and research interests. The application of SDM in the megadiverse neotropics—where data on species occurrences are scarce—presents several challenges, involving acknowledging the limitations imposed by data quality, including surveys as an integral part of SDM studies, and designing the analyses in accordance with the question investigated. Specific solutions were discussed, and a code of good practice in SDM studies and related field surveys was drafted.
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Beale, Colin M., e Jack J. Lennon. "Incorporating uncertainty in predictive species distribution modelling". Philosophical Transactions of the Royal Society B: Biological Sciences 367, n. 1586 (19 gennaio 2012): 247–58. http://dx.doi.org/10.1098/rstb.2011.0178.
Abstract (sommario):
Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.
3
Lowen, J. Benjamin, Devorah R. Hart, Ryan R. E. Stanley, Sarah J. Lehnert, Ian R. Bradbury e Claudio DiBacco. "Assessing effects of genetic, environmental, and biotic gradients in species distribution modelling". ICES Journal of Marine Science 76, n. 6 (8 aprile 2019): 1762–75. http://dx.doi.org/10.1093/icesjms/fsz049.
Abstract (sommario):
Abstract To develop more reliable marine species distribution models (SDMs), we examine how genetic, climatic, and biotic interaction gradients give rise to prediction error in marine SDM. Genetic lineages with distinct ecological requirements spanning genetic gradients have yet to be treated separately in marine SDM, which are often constrained to modeling the potential distribution of one biological unit (e.g. lineage or species) at a time. By comparing SDM performance for the whole species or where observation and predictions were partitioned among geographically discontinuous genetic lineages, we first identified the appropriate biological unit for modeling sea scallop. Prediction errors, in particular contiguous omissions at the northern range margins were effectively halved in genetic lineage SDM (Total error=15%) verses whole species SDM. Remaining SDM prediction error was strongly associated with: i) Sharp climatic gradients (abrupt and persistent spatial shifts in limiting temperatures) found within continental shelf breaks and bottom channels. ii) A biotic gradient in the predation of sea scallop juveniles by the sand star within the Hudson Shelf USA. Our findings highlight how the accuracy of marine SDM is dependent on capturing the appropriate biological unit for modeling (e.g. lineages rather than species) and adequately resolving limiting abiotic and biotic interaction gradients.
4
Naimi, Babak, e Miguel B. Araújo. "sdm: a reproducible and extensible R platform for species distribution modelling". Ecography 39, n. 4 (1 marzo 2016): 368–75. http://dx.doi.org/10.1111/ecog.01881.
5
Wunderlich, Rainer Ferdinand, Yu-Pin Lin, Johnathen Anthony e Joy R. Petway. "Two alternative evaluation metrics to replace the true skill statistic in the assessment of species distribution models". Nature Conservation 35 (20 giugno 2019): 97–116. http://dx.doi.org/10.3897/natureconservation.35.33918.
Abstract (sommario):
Model evaluation metrics play a critical role in the selection of adequate species distribution models for conservation and for any application of species distribution modelling (SDM) in general. The responses of these metrics to modelling conditions, however, are rarely taken into account. This leads to inadequate model selection, downstream analyses and uniformed decisions. To aid modellers in critically assessing modelling conditions when choosing and interpreting model evaluation metrics, we analysed the responses of the True Skill Statistic (TSS) under a variety of presence-background modelling conditions using purely theoretical scenarios. We then compared these responses with those of two evaluation metrics commonly applied in the field of meteorology which have potential for use in SDM: the Odds Ratio Skill Score (ORSS) and the Symmetric Extremal Dependence Index (SEDI). We demonstrate that (1) large cell number totals in the confusion matrix, which is strongly biased towards ‘true’ absences in presence-background SDM and (2) low prevalence both compromise model evaluation with TSS. This is since (1) TSS fails to differentiate useful from random models at extreme prevalence levels if the confusion matrix cell number total exceeds ~30,000 cells and (2) TSS converges to hit rate (sensitivity) when prevalence is lower than ~2.5%. We conclude that SEDI is optimal for most presence-background SDM initiatives. Further, ORSS may provide a better alternative if absence data are available or if equal error weighting is strictly required.
6
Felicísimo, Ángel M., Ignacio Armendáriz e Virginia Alberdi Nieves. "Modelling the potential effects of climate change in the distribution of Xylotrechus arvicola in Spain". Horticultural Science 48, No. 1 (31 marzo 2021): 38–46. http://dx.doi.org/10.17221/85/2019-hortsci.
Abstract (sommario):
Xylotrechus arvicola is an emerging grape pest that generates serious sanitary problems in vineyards and is currently expanding its range throughout Spain. The increasing prevalence of this pest in Spanish vineyards has been detected since 1990. In this study, the relationship between the climate and the actual distribution of the beetle was analysed, as well as how this distribution might change in the future according to several climate change models. The methodology was based on predictive models (SDM; species distribution modelling) using climate variables as explanatory factors, although the relationships were not necessarily causal. Maxent was used as the SDM method. The current climatic niche was calculated, and the actual potential distribution area was estimated. The relationships between the climate variables and the species probability of the presence were projected to various future climate change scenarios. The main conclusions reached were that climate change will favour the expansion of X. arvicola and that the potential infestation zones will be extended significantly. Although the results, because they were based on hypothetical climate frameworks that are under constant revision, were not conclusive, they should be taken into consideration when defining future strategies in the wine industry.
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De Marco, Paulo, José Alexandre Felizola Diniz-Filho e Luis Mauricio Bini. "Spatial analysis improves species distribution modelling during range expansion". Biology Letters 4, n. 5 (29 luglio 2008): 577–80. http://dx.doi.org/10.1098/rsbl.2008.0210.
Abstract (sommario):
Species distribution models (SDMs) assume equilibrium between species' distribution and the environment. However, this assumption can be violated under restricted dispersal and spatially autocorrelated environmental conditions. Here we used a model to simulate species' ranges expansion under two non-equilibrium scenarios, evaluating the performance of SDM coupled with spatial eigenvector mapping. The highest fit is for the models that include space, although the relative importance of spatial variables during the range expansion differs in the two scenarios. Incorporating space to the models was important only under colonization-lag non-equilibrium, under the expected scenario. Thus, mechanisms that generate range cohesion and determine species' distribution under climate changes can be captured by spatial modelling, with advantages compared with other techniques and in line with recent claims that SDMs have to account for more complex dynamic scenarios.
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Untalan, M. Z. G., D. F. M. Burgos e K. P. Martinez. "SPECIES DISTRIBUTION MODELLING OF TWO SPECIES ENDEMIC TO THE PHILIPPINES TO SHOW THE APPLICABILITY OF MAXENT". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (23 dicembre 2019): 449–54. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-449-2019.
Abstract (sommario):
Abstract. Maxent is a machine learning model used for species distribution modelling (SDM) that is rising in popularity. As with any species distribution model, it needs to be validated for certain species before being used to generate insights and trusted predictions. Using Maxent, SDM of two endemic species in the Philippines, Varanus palawanensis (Palawan monitor lizard) and Caprimulgus manillensis (Philippine nightjar), were created using presence-only data, with 14 V. palawanensis and 771 C. manillensis occurrences, and 19 bioclimatic variables from BIOCLIM. This study shows the consistency to historical facts of Maxent on two endemic species of the Philippines of varying nature. The applicability of Maxent on the two very different species show that Maxent has high likelihood to give good results for other species. Showing that Maxent is applicable to the species of the Philippines gives additional tools for ecologists and national administrators to lead the development of the Philippines in the direction that conserves the biodiversity of the Philippines and that increases the productivity and quality of life in the Philippines.
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Lobite, Neil Jun Sala. "Modelling Habitat Suitability and Distribution of the Endemic Mindanao Horned Frog (Pelobatrachus stejnegeri) and its Response to Changing Climate". Grassroots Journal of Natural Resources 7, n. 1 (30 aprile 2024): 123–37. http://dx.doi.org/10.33002/nr2581.6853.070107.
Abstract (sommario):
Climate change is already affecting biodiversity, with special concern to endemics whose range is restricted and limited. This study focuses on the Mindanao horned frog (Pelobatrachus stejnegeri), an endemic species to the Philippines, susceptible to climate-induced habitat changes. Using MaxEnt species distribution model (SDM), the current and future (year 2050 projections) habitat suitability and distribution of P. stejnegeri were modelled. Results showed that annual mean temperature, elevation, and annual precipitation were the environmental variables having the highest influence on P. stejnegeri's distribution. The model predicts a significant range contraction under representative concentration pathways (RCP) future scenarios (RCP 2.6 and RCP 8.5), with a more pronounced decrease in distribution (31.72%) under the high emission scenario (RCP 8.5). These findings emphasize the vulnerability of P. stejnegeri to climate change and highlight the importance of integrating SDM into conservation and management strategies to protect endemic species under changing climatic conditions.
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FREITAS, GUILHERME H. S., LÍLIAN M. COSTA, ANDERSON V. CHAVES, MARCELO F. VASCONCELOS, LEONARDO C. RIBEIRO, JULIANO C. SILVA, RONEY A. SOUZA, FABRÍCIO R. SANTOS e MARCOS RODRIGUES. "Geographic range and conservation of the Cipo Canastero Asthenes luizae, an endemic furnariid of Brazilian sky islands". Bird Conservation International 30, n. 3 (31 ottobre 2019): 365–80. http://dx.doi.org/10.1017/s0959270919000418.
Abstract (sommario):
SummaryCipo Canastero Asthenes luizae is a relict ovenbird restricted to rocky outcrops at high elevations within the campo rupestre vegetation of the Espinhaço Range in the state of Minas Gerais, south-eastern Brazil. This poorly known species is considered ‘Near Threatened’, but recent studies have suggested that it should be listed under a higher category of threat. To contribute to the knowledge of this species and its conservation assessment and related planning, we compiled all literature records of the species distribution (n = 16 locations), collected new data on its occurrence (n = 72 locations), and calculated its geographic range using four different approaches. First, we defined the sky islands where the species occurs (nine units) using the lowest elevation value recorded (1,100 m asl) as a cut-off. Second, we performed species distribution modelling (SDM) across the sky islands and identified an area of 2,225.21 km2. Third, we measured the species’ extent of occurrence (EOO = 24,555.85 km2) and used SDM to estimate its upper limit (EOOup = 30,697.58 km2). Fourth, we measured the area of occupancy (AOO = 228 km2) and used SDM to estimate its upper limit (AOOup = 1,827.39 km2). We analysed the Cipo Canastero sky islands in terms of landscape metrics including size, isolation, protected area coverage, shape index, core area index, and proportion covered by SDM. We observed a very fragmented distribution, especially in the North sector of the species distribution, composed of small and isolated populations (separated by up to 112 km); the South sector is the core of its distribution and is composed of larger, more connected patches with differences in shape complexity that are not strongly influenced by an edge effect. The range sizes calculated, along with other reported information regarding population and habitat trends, justifies the inclusion of the species in at least the ‘Vulnerable’ category.
11
Støa, Bente, Rune Halvorsen, Jogeir N. Stokland e Vladimir I. Gusarov. "How much is enough? Influence of number of presence observations on the performance of species distribution models". Sommerfeltia 39, n. 1 (1 gennaio 2019): 1–28. http://dx.doi.org/10.2478/som-2019-0001.
Abstract (sommario):
Abstract Species distribution modeling (SDM) can be useful for many applied purposes, e.g., mapping and monitoring of rare and endangered species. Sparse presence data are a recurrent, major obstacle to precise modeling of species distributions. Thus, knowing the minimum number of presences required to obtain reliable distribution models is of fundamental importance for applied use of SDM. This study uses a novel approach to assess the critical sample size (CSS) sufficient for an accurate prediction of species distributions with Maximum Entropy Modeling (MaxEnt). Large presence datasets for thirty insect species, ranging from generalists to specialists regarding their responses to main bioclimatic gradients, were used to produce reference distribution models. Models based on replicated subsamples of different size drawn randomly from the full dataset were compared to the reference model using the index of vector similarity distribution models. Models based on replicated subsamples of different size drawn randomly from the full dataset were compared to the reference model using the index of vector similarity (IVS). Two thresholds for IVS were determined based on comparison of nine reference models to random null models. The threshold values correspond to 0.95 and 0.99 probability that a model outperforms a random null model in terms of similarity to the reference dataset. For 90% of the species, clearly nonrandom models were obtained with less than 10 presence observations, and for 97% of the species with less than 15 presence observations. We conclude that the number of presence observations required to produce nonrandom models is generally low and, accordingly, that even sparse datasets may be useful for distribution modelling.
12
LAHSSINI, Said, Said HAJIB, Hicham Lahlaoi, Hicham MHARZI ALAOUI e Abdellatif Khattabi. "Modelling Spatial Distribution of the Carob Tree (Ceratonia siliqua L.) in Azilal Province, Morocco". Journal of Geography and Geology 7, n. 4 (2 dicembre 2015): 33. http://dx.doi.org/10.5539/jgg.v7n4p33.
Abstract (sommario):
Factors determining forest species distribution include, in addition to external factors such as human interference and environmental management strategies, also soil and hydrological characteristics and climate conditions in any given areas. Modelling<strong> </strong>distribution has practical application in forest conservation and management, and help decision makers to develop strategies aimed at forest restoration, development of mountainous areas and the continuous and sustainable provision of forest-related services. Species distribution modelling (SDM) can be used for predicting species distribution based on tree presence records and on a number of environmental predictors. In this study we used MaxEnt for niche modelling in predicting carob (<em>Ceratonia siliqua </em>L.) trees spatial distribution in the Province of Azilal in Morocco. The results obtained show that a large area of the mountain regions is suitable for the expansion of <em>Ceratonia siliqua</em> stands. These findings will help decision makers in forest planning to better identify suitable sites for carob tree plantations and assess the potential of the exiting populations.
13
Devika, M., e K. Amitha Bachan. "Ecoregion level niche specific habitat prediction of threatened Syzygium caryophyllatum (Myrtaceae) for reintroduction and ecorestoration". Indian Journal of Forestry 46, n. 2 (30 settembre 2023): 78–86. http://dx.doi.org/10.54207/bsmps1000-2023-663hox.
Abstract (sommario):
Syzygium caryophyllatum (L.) Alston is a medium-sized threatened tree that mainly occupies the low-elevation evergreen patches of the Western Ghats (India) - Sri Lanka biodiversity hotspot. The present study predicts the potential habitats of Syzygium caryophyllatum at the ecoregion level for prioritising its conservation and restoration area. The bioclimatic species distribution modelling (SDM) using 19 bioclimatic parameters of World Clim used here to elucidate fundamental niche of the species. The standardised vegetation and landuse layer used in this model for the prediction of potential niche of the species incorporating biotic factors. The incorporation of standardised vegetation layer for the inclusion of Eltonian factors along with MaxEnt based Ecological Niche Modelling helped to refine its predicted area from 10,824 km2 to 8,595 km2 within the Western Ghats. The model adopted with the MaxEnt SDM with additional biotic layers to better accommodate the Grinnellian and Eltonian niche factors. The ecoregion level prediction for the potential habitat of the threatened tree species provides adequate information for the niche specific conservation and ecorestoration planning ensuring ecosystem-based approach (EbA).
14
Effrosynidis, Dimitrios, Athanassios Tsikliras, Avi Arampatzis e Georgios Sylaios. "Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea". Applied Sciences 10, n. 24 (13 dicembre 2020): 8900. http://dx.doi.org/10.3390/app10248900.
Abstract (sommario):
In this work a fish species distribution model (SDM) was developed, by merging species occurrence data with environmental layers, with the scope to produce high resolution habitability maps for the whole Mediterranean Sea. The final model is capable to predict the probability of occurrence of each fish species at any location in the Mediterranean Sea. Eight pelagic, commercial fish species were selected for this study namely Engraulis encrasicolus, Sardina pilchardus, Sardinella aurita, Scomber colias, Scomber scombrus, Spicara smaris, Thunnus thynnus and Xiphias gladius. The SDM environmental predictors were obtained from the databases of Copernicus Marine Environmental Service (CMEMS) and the European Marine Observation and Data Network (EMODnet). The probabilities of fish occurrence data in low resolution and with several gaps were obtained from Aquamaps (FAO Fishbase). Data pre-processing involved feature engineering to construct 6830 features, representing the distribution of several mean-monthly environmental variables, covering a time-span of 10 years. Feature selection with the ensemble Reciprocal Ranking method was used to rank the features according to their relative importance. This technique increased model’s performance by 34%. Ten machine learning algorithms were then applied and tested based on their overall performance per species. The XGBoost algorithm performed better and was used as the final model. Feature categories were explored, with neighbor-based, extreme values, monthly and surface ones contributing most to the model. Environmental variables like salinity, temperature, distance to coast, dissolved oxygen and nitrate were found the strongest ones in predicting the probability of occurrence for the above eight species.
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Bladon, Andrew J., Paul F. Donald, Nigel J. Collar, Jarso Denge, Galgalo Dadacha, Mengistu Wondafrash e Rhys E. Green. "Climatic change and extinction risk of two globally threatened Ethiopian endemic bird species". PLOS ONE 16, n. 5 (19 maggio 2021): e0249633. http://dx.doi.org/10.1371/journal.pone.0249633.
Abstract (sommario):
Climate change is having profound effects on the distributions of species globally. Trait-based assessments predict that specialist and range-restricted species are among those most likely to be at risk of extinction from such changes. Understanding individual species’ responses to climate change is therefore critical for informing conservation planning. We use an established Species Distribution Modelling (SDM) protocol to describe the curious range-restriction of the globally threatened White-tailed Swallow (Hirundo megaensis) to a small area in southern Ethiopia. We find that, across a range of modelling approaches, the distribution of this species is well described by two climatic variables, maximum temperature and dry season precipitation. These same two variables have been previously found to limit the distribution of the unrelated but closely sympatric Ethiopian Bush-crow (Zavattariornis stresemanni). We project the future climatic suitability for both species under a range of climate scenarios and modelling approaches. Both species are at severe risk of extinction within the next half century, as the climate in 68–84% (for the swallow) and 90–100% (for the bush-crow) of their current ranges is predicted to become unsuitable. Intensive conservation measures, such as assisted migration and captive-breeding, may be the only options available to safeguard these two species. Their projected disappearance in the wild offers an opportunity to test the reliability of SDMs for predicting the fate of wild species. Monitoring future changes in the distribution and abundance of the bush-crow is particularly tractable because its nests are conspicuous and visible over large distances.
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Fyllas, Nikolaos M., Theano Koufaki, Christodoulos I. Sazeides, Gavriil Spyroglou e Konstantinos Theodorou. "Potential Impacts of Climate Change on the Habitat Suitability of the Dominant Tree Species in Greece". Plants 11, n. 12 (20 giugno 2022): 1616. http://dx.doi.org/10.3390/plants11121616.
Abstract (sommario):
Climate change is affecting species distribution and ecosystem form and function. Forests provide a range of ecosystem services, and understanding their vulnerability to climate change is important for designing effective adaptation strategies. Species Distribution Modelling (SDM) has been extensively used to derive habitat suitability maps under current conditions and project species distribution shifts under climate change. In this study, we model the current and future habitat suitability of the dominant tree species in Greece (Abies cephalonica, Abies borisii-regis, Pinus brutia, Pinus halepensis, Pinus nigra, Quercus ilex, Quercus pubescens, Quercus frainetto and Fagus sylvatica), based on species-specific presence data from the EU-Forest database, enhanced with data from Greece that is currently under-represented in terms of tree species occurrence points. By including these additional presence data, areas with relatively drier conditions for some of the study species were included in the SDM development, yielding a potentially lower vulnerability under climate change conditions. SDMs were developed for each taxon using climate and soil data at a resolution of ~1 km2. Model performance was assessed under current conditions and was found to adequately simulate potential distributions. Subsequently, the models were used to project the potential distribution of each species under the SSP1-2.6 and SSP5-8.5 scenarios for the 2041–2070 and 2071–2100 time periods. Under climate change scenarios, a reduction in habitat-suitable areas was predicted for most study species, with higher elevation taxa experiencing more pronounced potential habitat shrinkages. An exception was the endemic A. cephalonica and its sister species A. borisii-regis, which, although currently found at mid and high elevations, seem able to maintain their potential distribution under most climate change scenarios. Our findings suggest that climate change could significantly affect the distribution and dynamics of forest ecosystems in Greece, with important ecological, economic and social implications, and thus adequate mitigation measures should be implemented.
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Adamova, Valeria V., Mikhail A. Orlov e Alexander V. Sheludkov. "Land snails <i>Brephulopsis cylindrica </i>and <i>Xeropicta derbentina </i>(Gastropoda: Stylommatophora): case study of invasive species distribution modelling". Ruthenica, Russian Malacological Journal 32, n. 3 (1 luglio 2022): 121–36. http://dx.doi.org/10.35885/ruthenica.2022.32(3).5.
Abstract (sommario):
The terrestrial snails Brephulopsis cylindrica and Xeropicta derbentina are native to steppes of the Northern Black Sea region; X. derbentina has also initially inhabited Eastern Mediterranean, the Caucasus, and Anatolia. However, in last decades the species are increasingly reported outside of their natural range which renders them as likely invasive. The paper aims to assess the ecological suitability of adjacent habitats in terms of the molluscs invasion. We address this using species distribution modelling (SDM). The selected environment predictors for SDM included 22 environment factors such as land cover, enhanced vegetation index (EVI), altitude, and 19 bioclimatic parameters. The resulted model suggests that the area with maximum suitability extends from the natural range to the Central Danube lowland in the West and up to the Volga Upland in the Northeast. These regions have similar EVI and are largely croplands. Among the predictors, the minimum temperature of the coldest month has the greatest impact on the modelling results, which agrees with the variable being the limiting factor for the distribution of subtropical invertebrates. The study reinforces the notions that X. derbentina and B. cylindrica are likely to further expand the boundaries of their range thus posing threats to native ecosystems.
18
Jones, Miranda C., e William W. L. Cheung. "Multi-model ensemble projections of climate change effects on global marine biodiversity". ICES Journal of Marine Science 72, n. 3 (10 ottobre 2014): 741–52. http://dx.doi.org/10.1093/icesjms/fsu172.
Abstract (sommario):
Abstract Species distribution models (SDMs) are important tools to explore the effects of future global changes in biodiversity. Previous studies show that variability is introduced into projected distributions through alternative datasets and modelling procedures. However, a multi-model approach to assess biogeographic shifts at the global scale is still rarely applied, particularly in the marine environment. Here, we apply three commonly used SDMs (AquaMaps, Maxent, and the Dynamic Bioclimate Envelope Model) to assess the global patterns of change in species richness, invasion, and extinction intensity in the world oceans. We make species-specific projections of distribution shift using each SDM, subsequently aggregating them to calculate indices of change across a set of 802 species of exploited marine fish and invertebrates. Results indicate an average poleward latitudinal shift across species and SDMs at a rate of 15.5 and 25.6 km decade−1 for a low and high emissions climate change scenario, respectively. Predicted distribution shifts resulted in hotspots of local invasion intensity in high latitude regions, while local extinctions were concentrated near the equator. Specifically, between 10°N and 10°S, we predicted that, on average, 6.5 species would become locally extinct per 0.5° latitude under the climate change emissions scenario Representative Concentration Pathway 8.5. Average invasions were predicted to be 2.0 species per 0.5° latitude in the Arctic Ocean and 1.5 species per 0.5° latitude in the Southern Ocean. These averaged global hotspots of invasion and local extinction intensity are robust to the different SDM used and coincide with high levels of agreement.
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Hintze, Frederico, Ricardo B. Machado e Enrico Bernard. "Bioacoustics for in situ validation of species distribution modelling: An example with bats in Brazil". PLOS ONE 16, n. 10 (20 ottobre 2021): e0248797. http://dx.doi.org/10.1371/journal.pone.0248797.
Abstract (sommario):
Species distribution modelling (SDM) gained importance on biodiversity distribution and conservation studies worldwide, including prioritizing areas for public policies and international treaties. Useful for large-scale approaches and species distribution estimates, it is a plus considering that a minor fraction of the planet is adequately sampled. However, minimizing errors is challenging, but essential, considering the uses and consequences of such models. In situ validation of the SDM outputs should be a key-step—in some cases, urgent. Bioacoustics can be used to validate and refine those outputs, especially if the focal species’ vocalizations are conspicuous and species-specific. This is the case of echolocating bats. Here, we used extensive acoustic monitoring (>120 validation points over an area of >758,000 km2, and producing >300,000 sound files) to validate MaxEnt outputs for six neotropical bat species in a poorly-sampled region of Brazil. Based on in situ validation, we evaluated four threshold-dependent theoretical evaluation metrics’ ability in predicting models’ performance. We also assessed the performance of three widely used thresholds to convert continuous SDMs into presence/absence maps. We demonstrated that MaxEnt produces very different outputs, requiring a careful choice on thresholds and modeling parameters. Although all theoretical evaluation metrics studied were positively correlated with accuracy, we empirically demonstrated that metrics based on specificity-sensitivity and sensitivity-precision are better for testing models, considering that most SDMs are based on unbalanced data. Without independent field validation, we found that using an arbitrary threshold for modelling can be a precarious approach with many possible outcomes, even after getting good evaluation scores. Bioacoustics proved to be important for validating SDMs for the six bat species analyzed, allowing a better refinement of SDMs in large and under-sampled regions, with relatively low sampling effort. Regardless of the species assessing method used, our research highlighted the vital necessity of in situ validation for SDMs.
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Kaliontzopoulou, A., J. C. Brito, M. A. Carretero, S. Larbes e D. J. Harris. "Modelling the partially unknown distribution of wall lizards (Podarcis) in North Africa: ecological affinities, potential areas of occurrence, and methodological constraints". Canadian Journal of Zoology 86, n. 9 (settembre 2008): 992–1001. http://dx.doi.org/10.1139/z08-078.
Abstract (sommario):
Species distribution modelling (SDM) is a powerful tool to investigate various biological questions with a spatial component, but is also sensitive to presence-data characteristics, particularly data precision and clustering. Here, we investigate the effect of these two factors on SDM using Maxent as the modelling technique and wall lizards (genus Podarcis Wagler, 1830) from North Africa as a model system. Podarcis are not ubiquitous in Africa as they are in Europe, but their ecological and distributional characteristics in this area are poorly known. Our results show that the most important environmental factors related to the distribution of this genus in North Africa are humidity, habitat type, and temperature. The areas of potential distribution predicted by models based on data sets with different precision and clustering characteristics show high relatedness to coastal areas and mountain ranges and extend to areas were presence records for these lizards are lacking. Our comparison of models based on different data sets indicates that finer scale models, even if based on fewer presence locations, outperform coarser scale ones. Data clustering does not have a negative effect on model performance, but is rather overcome by sample-size effects. Similar approaches may be of general application to other stenoic species for which available locations are scarce in comparison with the extension of the study area.
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Pleguezuelos, Juan M., Elisa Mora, Philip de Pous, Daniel Escoriza, Margarita Metallinou, David Donaire, Mar Comas e Salvador Carranza. "Elusive but widespread? The potential distribution and genetic variation of Hyalosaurus koellikeri (Günther, 1873) in the Maghreb". Amphibia-Reptilia 32, n. 3 (2011): 385–97. http://dx.doi.org/10.1163/017353711x587732.
Abstract (sommario):
AbstractThe genetic variability and the potential distribution under past (Last Glacial Maximum; LGM (MIROC and CCSM simulations)) and present conditions were studied for the anguid Hyalosaurus koellikeri, using analyses of two mitochondrial (ND1 and ND2) and one nuclear (PRLR) gene and species distribution modelling (SDM) including 19 geographical coordinates, covering most of its distribution range. Unexpectedly, the genetic results show that H. koellikeri presents a very low level of variability both in the mitochondrial and nuclear genes studied. The present predicted distribution of H. koellikeri revealed a large potential distribution in both north and eastwards directions, with suitable areas predicted in places where the species has never been reported before, as for instance the Rif Mountains in Morocco, as well as into most parts of northern Algeria and Tunisia. The LGM distribution is even larger compared to the present, with a continuous predicted distribution from Morocco to Tunisia, and even into Libya under the MIROC simulation. The results of the genetic and SDM analyses suggest that the now isolated populations from Debdou and Tlemcen have probably been in contact during the LGM, but its absence from both present and past predicted suitable areas is still a mystery. Hyalosaurus koellikeri depends mainly on closed deciduous forests (typically Cedrus atlantica and Quercus sp.) and open deciduous shrubland with high amounts of annual rainfall. The results of this study and the absence of recent sightings of the species outside the core distribution might indicate a regression of the species. Hence, a reevaluation of the conservation status of the species seems warranted.
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Wang, Fang, Min Li, Haixia Zheng, Tian Dong e Xianhong Zhang. "A Phylogeographical Analysis of the Beetle Pest Species Callosobruchus chinensis (Linnaeus, 1758) in China". Insects 13, n. 2 (29 gennaio 2022): 145. http://dx.doi.org/10.3390/insects13020145.
Abstract (sommario):
Callosobruchus chinensis (Coleoptera Bruchidae), is a pest of different varieties of legumes. In this paper, a phylogeographical analysis of C. chinensis was conducted to provide knowledge for the prevention and control of C. chinensis. A total of 224 concatenated mitochondrial sequences were obtained from 273 individuals. Suitable habitat shifts were predicted by the distribution modelling (SDM). Phylogeny, genetic structure and population demographic history were analyzed using multiple software. Finally, the least-cost path (LCP) method was used to identify possible dispersal corridors and genetic connectivity. The SDM results suggested that the distribution of C. chinensis experienced expansion and contraction with changing climate. Spatial distribution of mtDNA haplotypes showed there was partial continuity among different geographical populations of C. chinensis, except for the Hohhot (Inner Mongolia) population. Bayesian skyline plots showed that the population had a recent expansion during 0.0125 Ma and 0.025 Ma. The expansion and divergent events were traced back to Quaternary glaciations. The LCP method confirmed that there were no clear dispersal routes. Our findings indicated that climatic cycles of the Pleistocene glaciations, unsuitable climate and geographic isolation played important roles in the genetic differentiation of C. chinensis. Human activities weaken the genetic differentiation between populations. With the change in climate, the suitable areas of C. chinensis will disperse greatly in the future.
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Usmadi, Didi, Sutomo Sutomo, Rajif Iryadi, Siti Fatimah Hanum, I. Dewa Putu Darma e I. Putu Agus Hendra Wibawa. "Predicting Species Distribution for True Indigo (Indigofera tinctoria L.) in Citarum Watershed, West Java, Indonesia". Journal of Tropical Biodiversity and Biotechnology 6, n. 3 (20 settembre 2021): 65398. http://dx.doi.org/10.22146/jtbb.65398.
Abstract (sommario):
Citarum watershed is a region of approximately 6,610 km2 in West Java, Indonesia. Citarum watershed has been degraded through historical land use and vegetation clearing. Rehabilitation of Citarum watershed uses Indigofera tinctoria L. that has value as a source of natural blue dye and is considered suitable for the region. Species distribution modelling and Habitat suitability index (SDM/HSI) were undertaken for I. tinctoria. The occurrence and environment data (bioclimatic, topography, and soil type) were input to HSI. Results of the Indigofera tinctoria habitat suitability model in Citarum watershed are very good (0.9–1) for some parts of the Citarum watershed. The medium and high suitability areas were respectively 4.49% and 4.37% of the area were located in the lowlands (Bekasi Regency and Karawang Regency). Prediction based on climate modelling for 2050 and 2070 estimated that the medium-high suitability area of Indigofera tinctoria will be reduced relative to the present.
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Hambuckers, Alain, Simon de Harenne, Eberth Rocha Ledezma, Lilian Zúñiga Zeballos e Louis François. "Predicting the Future Distribution of Ara rubrogenys, an Endemic Endangered Bird Species of the Andes, Taking into Account Trophic Interactions". Diversity 13, n. 2 (21 febbraio 2021): 94. http://dx.doi.org/10.3390/d13020094.
Abstract (sommario):
Species distribution models (SDMs) are commonly used with climate only to predict animal distribution changes. This approach however neglects the evolution of other components of the niche, like food resource availability. SDMs are also commonly used with plants. This also suffers limitations, notably an inability to capture the fertilizing effect of the rising CO2 concentration strengthening resilience to water stress. Alternatively, process-based dynamic vegetation models (DVMs) respond to CO2 concentration. To test the impact of the plant modelling method to model plant resources of animals, we studied the distribution of a Bolivian macaw, assuming that, under future climate, DVMs produce more conservative results than SDMs. We modelled the bird with an SDM driven by climate. For the plant, we used SDMs or a DVM. Under future climates, the macaw SDM showed increased probabilities of presence over the area of distribution and connected range extensions. For plants, SDMs did not forecast overall response. By contrast, the DVM produced increases of productivity, occupancy and diversity, also towards higher altitudes. The results offered positive perspectives for the macaw, more optimistic with the DVM than with the SDMs, than initially assumed. Nevertheless, major common threats remain, challenging the short-term survival of the macaw.
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Alsos, Inger Greve, Dorothee Ehrich, Wilfried Thuiller, Pernille Bronken Eidesen, Andreas Tribsch, Peter Schönswetter, Claire Lagaye, Pierre Taberlet e Christian Brochmann. "Genetic consequences of climate change for northern plants". Proceedings of the Royal Society B: Biological Sciences 279, n. 1735 (4 gennaio 2012): 2042–51. http://dx.doi.org/10.1098/rspb.2011.2363.
Abstract (sommario):
Climate change will lead to loss of range for many species, and thus to loss of genetic diversity crucial for their long-term persistence. We analysed range-wide genetic diversity (amplified fragment length polymorphisms) in 9581 samples from 1200 populations of 27 northern plant species, to assess genetic consequences of range reduction and potential association with species traits. We used species distribution modelling (SDM, eight techniques, two global circulation models and two emission scenarios) to predict loss of range and genetic diversity by 2080. Loss of genetic diversity varied considerably among species, and this variation could be explained by dispersal adaptation (up to 57%) and by genetic differentiation among populations ( F ST ; up to 61%). Herbs lacking adaptations for long-distance dispersal were estimated to lose genetic diversity at higher rate than dwarf shrubs adapted to long-distance dispersal. The expected range reduction in these 27 northern species was larger than reported for temperate plants, and all were predicted to lose genetic diversity according to at least one scenario. SDM combined with F ST estimates and/or with species trait information thus allows the prediction of species' vulnerability to climate change, aiding rational prioritization of conservation efforts.
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Datta, Arunava, Oliver Schweiger e Ingolf Kühn. "Origin of climatic data can determine the transferability of species distribution models". NeoBiota 59 (28 luglio 2020): 61–76. http://dx.doi.org/10.3897/neobiota.59.36299.
Abstract (sommario):
Methodological research on species distribution modelling (SDM) has so far largely focused on the choice of appropriate modelling algorithms and variable selection approaches, but the consequences of choosing amongst different sources of environmental data has scarcely been investigated. Bioclimatic variables are commonly used as predictors in SDMs. Currently, several online databases offer the same sets of bioclimatic variables, but they differ in underlying source of raw data and method of data processing (extrapolation and downscaling). In this paper, we asked whether predictive performance and spatial transferability of SDMs are affected by the choice of two different bioclimatic databases viz. WorldClim 2 and Chelsa 1.2. We used presence-absence data of the invasive plant Ageratina adenophora from the Western Himalaya for training SDMs and a set of independently-collected presence-only datasets from the Central and Eastern Himalaya to evaluate the transferability of the SDMs beyond the training range. We found that the performance of SDMs was, to a large degree, affected by the choice of the climatic dataset. Models calibrated on Chelsa 1.2 outperformed WorldClim 2 in terms of internal evaluation on the calibration dataset. However, when the model was transferred beyond the calibration range to the Central and Eastern Himalaya, models based on WorldClim 2 performed substantially better. We recommend that, in addition to the choice of predictor variables, the choice of predictor datasets with these variables should not be based merely on subjective decision whenever several options are available. Instead, such decisions should be based on robust evaluation of the most appropriate dataset for a given geographic region and species being modelled. Moreover, decisions could also depend on the objective of the study, i.e. projecting within the calibration range or beyond. Therefore, a quantitative evaluation of predictor datasets from alternative sources should be routinely performed as an integral part of the modelling procedure.
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Shipley, Benjamin, Bistra Dilkina e Jenny McGuire. "Megasdm: Modelling Species Ranges in The Past And Future". Bulletin of the Florida Museum of Natural History 60, n. 2 (16 febbraio 2023): 115. http://dx.doi.org/10.58782/flmnh.zwwl8127.
Abstract (sommario):
As we enter the Anthropocene, unprecedented climatic and landscape changes are leading to global extinctions and the reorganization of many species’ ranges. Understanding how species ranges have changed through time can contextualize long-term interactions between geography and ecology, offer insight into how they may change in the future, and inform conservation of vulnerable species. Species distribution models (SDMs) can be an important method for examining these range shifts, both in the future and through the past, by providing hypotheses about the responses of species’ ranges to certain scenarios. Here, I present several avenues for exploring hypotheses on range shifts using the megaSDM R package. This package facilitates realistic spatiotemporal SDM analyses by incorporating dispersal probabilities, creating time-step maps of range change dynamics, and efficiently handling large datasets and intensive subsampling techniques, while still allowing model-specific tuning. Using megaSDM, with the ongoing expansion of the nine-banded armadillo (Dasypus novemcinctus) as an example, I show how dispersal rate constraints can be incorporated into predictions of range shifts through time, introducing the concept of “invadable suitability”. Comparing dispersal-constrained to unconstrained models, I establish the importance of considering the dispersal ability of a species when projecting its range through time. Finally, I demonstrate the effects of transient range dynamics (e.g., a momentary range contraction in a period of prolonged expansion) on modelled species distributions, showing that these dynamics can be accounted for by incorporating many incremental time steps. These improvements in SDMs allow us to test and refine hypotheses that forecast or hindcast species range shifts. They are small but important steps towards treating conservation as a dynamic, rather than static, field and bringing a paleontological perspective to the preservation of life on Earth.
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Di Febbraro, Mirko, Ludovico Frate, Maria Carla de Francesco, Angela Stanisci, Francesco Pio Tozzi, Marco Varricchione e Maria Laura Carranza. "Modelling Beach Litter Accumulation on Mediterranean Coastal Landscapes: An Integrative Framework Using Species Distribution Models". Land 10, n. 1 (9 gennaio 2021): 54. http://dx.doi.org/10.3390/land10010054.
Abstract (sommario):
Beach litter accumulation patterns are influenced by biotic and abiotic factors, as well as by the distribution of anthropogenic sources. Although the importance of comprehensive approaches to deal with anthropogenic litter pollution is acknowledged, integrated studies including geomorphologic, biotic, and anthropic factors in relation to beach debris accumulation are still needed. In this perspective, Species Distribution Models (SDMs) might represent an appropriate tool to predict litter accumulation probability in relation to environmental conditions. In this context, we explored the applicability of a SDM–type modelling approach (a Litter Distribution Model; LDM) to map litter accumulation in coastal sand dunes. Starting from 180 litter sampling plots combined with fine–resolution variables, we calibrated LDMs from litter items classified either by their material type or origin. We also mapped litter accumulation hotspots. LDMs achieved fair-to-good predictive performance, with LDMs for litter classified by material type performing significantly better than models for litter classified by origin. Accumulation hotspots were mostly localized along the beach, by beach accesses, and at river mouths. In light of the promising results achieved by LDMs in this study, we conclude that this tool can be successfully applied within a coastal litter management context.
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Di Febbraro, Mirko, Ludovico Frate, Maria Carla de Francesco, Angela Stanisci, Francesco Pio Tozzi, Marco Varricchione e Maria Laura Carranza. "Modelling Beach Litter Accumulation on Mediterranean Coastal Landscapes: An Integrative Framework Using Species Distribution Models". Land 10, n. 1 (9 gennaio 2021): 54. http://dx.doi.org/10.3390/land10010054.
Abstract (sommario):
Beach litter accumulation patterns are influenced by biotic and abiotic factors, as well as by the distribution of anthropogenic sources. Although the importance of comprehensive approaches to deal with anthropogenic litter pollution is acknowledged, integrated studies including geomorphologic, biotic, and anthropic factors in relation to beach debris accumulation are still needed. In this perspective, Species Distribution Models (SDMs) might represent an appropriate tool to predict litter accumulation probability in relation to environmental conditions. In this context, we explored the applicability of a SDM–type modelling approach (a Litter Distribution Model; LDM) to map litter accumulation in coastal sand dunes. Starting from 180 litter sampling plots combined with fine–resolution variables, we calibrated LDMs from litter items classified either by their material type or origin. We also mapped litter accumulation hotspots. LDMs achieved fair-to-good predictive performance, with LDMs for litter classified by material type performing significantly better than models for litter classified by origin. Accumulation hotspots were mostly localized along the beach, by beach accesses, and at river mouths. In light of the promising results achieved by LDMs in this study, we conclude that this tool can be successfully applied within a coastal litter management context.
30
Pradervand, Jean-Nicolas, Anne Dubuis, Loïc Pellissier, Antoine Guisan e Christophe Randin. "Very high resolution environmental predictors in species distribution models". Progress in Physical Geography: Earth and Environment 38, n. 1 (17 dicembre 2013): 79–96. http://dx.doi.org/10.1177/0309133313512667.
Abstract (sommario):
Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.
31
Riccieri, Alessandra, Emiliano Mancini, Mattia Iannella, Daniele Salvi e Marco A. Bologna. "Phylogenetics and population structure of the steppe species Hycleus polymorphus (Coleoptera: Meloidae: Mylabrini) reveal multiple refugia in Mediterranean mountain ranges". Biological Journal of the Linnean Society 130, n. 3 (27 maggio 2020): 507–19. http://dx.doi.org/10.1093/biolinnean/blaa056.
Abstract (sommario):
Abstract Many continental species distributed in the Eurasian steppe occur as relict populations in the mountains of Western Europe. Their biogeographical responses to Quaternary climate changes have been poorly studied; however, they could have responded as cold-adapted species. We investigated the biogeographic history of a steppe beetle, Hycleus polymorphus, using mitochondrial and nuclear DNA sequences (COI, CAD, ITS2), and species distribution modelling (SDM) under present and past bioclimatic envelopes. We first performed a phylogenetic assessment to define species boundaries within the H. polymorphus species group. Specimens previously treated as Hycleus humerosus on morphological grounds are assigned to H. polymorphus, and those identified as Hycleus zebraeus assigned to Hycleus atratus. ITS2 data analyses revealed a strong phylogeographical structure of H. polymorphus populations, with four haplogroups corresponding to the (i) Italian Alps, (ii) French Alps and Pyrenees, (iii) South Balkan and Pontic mountains, and (iv) North Dinaric Alps. Based on these analyses and the SDM, we propose that during a glacial period, following the spread of steppic habitat, H. polymorphus underwent a range expansion from Asia to South-West Europe. Within the Mediterranean area, during the last interglacial the climatic suitability for the species was limited to mountains that acted as refugia and prompted allopatric divergence into four main lineages.
32
Nuñez, José J., Elkin Y. Suárez-Villota, Camila A. Quercia, Angel P. Olivares e Jack W. Sites Jr. "Phylogeographic analysis and species distribution modelling of the wood frog Batrachyla leptopus (Batrachylidae) reveal interglacial diversification in south western Patagonia". PeerJ 8 (6 ottobre 2020): e9980. http://dx.doi.org/10.7717/peerj.9980.
Abstract (sommario):
Background The evolutionary history of southern South American organisms has been strongly influenced by Pleistocene climate oscillations. Amphibians are good models to evaluate hypotheses about the influence of these climate cycles on population structure and diversification of the biota, because they are sensitive to environmental changes and have restricted dispersal capabilities. We test hypotheses regarding putative forest refugia and expansion events associated with past climatic changes in the wood frog Batrachyla leptopus distributed along ∼1,000 km of length including glaciated and non-glaciated areas in southwestern Patagonia. Methods Using three mitochondrial regions (D-loop, cyt b, and coI) and two nuclear loci (pomc and crybA1), we conducted multilocus phylogeographic analyses and species distribution modelling to gain insights of the evolutionary history of this species. Intraspecific genealogy was explored with maximum likelihood, Bayesian, and phylogenetic network approaches. Diversification time was assessed using molecular clock models in a Bayesian framework, and demographic scenarios were evaluated using approximate Bayesian computation (ABC) and extended Bayesian skyline plot (EBSP). Species distribution models (SDM) were reconstructed using climatic and geographic data. Results Population structure and genealogical analyses support the existence of four lineages distributed north to south, with moderate to high phylogenetic support (Bootstrap > 70%; BPP > 0.92). The diversification time of B. leptopus’ populations began at ∼0.107 mya. The divergence between A and B lineages would have occurred by the late Pleistocene, approximately 0.068 mya, and divergence between C and D lineages was approximately 0.065 mya. The ABC simulations indicate that lineages coalesced at two different time periods, suggesting the presence of at least two glacial refugia and a postglacial colonization route that may have generated two southern lineages (p = 0.93, type I error: <0.094, type II error: 0.134). EBSP, mismatch distribution and neutrality indexes suggest sudden population expansion at ∼0.02 mya for all lineages. SDM infers fragmented distributions of B. leptopus associated with Pleistocene glaciations. Although the present populations of B. leptopus are found in zones affected by the last glacial maximum (∼0.023 mya), our analyses recover an older history of interglacial diversification (0.107–0.019 mya). In addition, we hypothesize two glacial refugia and three interglacial colonization routes, one of which gave rise to two expanding lineages in the south.
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Deneu, Benjamin, Maximilien Servajean, Pierre Bonnet, Christophe Botella, François Munoz e Alexis Joly. "Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment". PLOS Computational Biology 17, n. 4 (19 aprile 2021): e1008856. http://dx.doi.org/10.1371/journal.pcbi.1008856.
Abstract (sommario):
Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.
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Tytar, V. M., e Ya R. Oksentyuk. "Modelling the Bioclimatic Niche of a Cohort of Selected Mite Species (Acari, Acariformes) Associated with the Infestation of Stored Products". Vestnik Zoologii 53, n. 5 (1 ottobre 2019): 399–416. http://dx.doi.org/10.2478/vzoo-2019-0036.
Abstract (sommario):
Abstract In this study an attempt is made to highlight important variables shaping the current bioclimatic niche of a number of mite species associated with the infestation of stored products by employing a species distribution modeling (SDM) approach. Using the ENVIREM dataset of bioclimatic variables, performance of the most robust models was mostly influenced by: 1) indices based on potential evapotranspiration, which characterize ambient energy and are mostly correlated with temperature variables, moisture regimes, and 2) strong fluctuations in temperature reflecting the severity of climate and/or extreme weather events. Although the considered mite species occupy man-made ecosystems, they remain more or less affected by the surrounding bioclimatic environment and therefore could be subjected to contemporary climate change. In this respect investigations are needed to see how this will affect future management targets concerning the safety of food storages.
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Michailidou, Danai-Eleni, Maria Lazarina e Stefanos P. Sgardelis. "Temperature and Prey Species Richness Drive the Broad-Scale Distribution of a Generalist Predator". Diversity 13, n. 4 (15 aprile 2021): 169. http://dx.doi.org/10.3390/d13040169.
Abstract (sommario):
The ongoing climate change and the unprecedented rate of biodiversity loss render the need to accurately project future species distributional patterns more critical than ever. Mounting evidence suggests that not only abiotic factors, but also biotic interactions drive broad-scale distributional patterns. Here, we explored the effect of predator-prey interaction on the predator distribution, using as target species the widespread and generalist grass snake (Natrix natrix). We used ensemble Species Distribution Modeling (SDM) to build a model only with abiotic variables (abiotic model) and a biotic one including prey species richness. Then we projected the future grass snake distribution using a modest emission scenario assuming an unhindered and no dispersal scenario. The two models performed equally well, with temperature and prey species richness emerging as the top drivers of species distribution in the abiotic and biotic models, respectively. In the future, a severe range contraction is anticipated in the case of no dispersal, a likely possibility as reptiles are poor dispersers. If the species can disperse freely, an improbable scenario due to habitat loss and fragmentation, it will lose part of its contemporary distribution, but it will expand northwards.
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Ramírez-Rodríguez, Rubén, Manuel Melendo-Luque, Juan Diego Rus-Moreno e Francisco Amich. "Potential changes in the distribution of Delphinium bolosii and related taxa of the series Fissa from the Iberian Peninsula under future climate change scenarios". Nature Conservation 43 (27 aprile 2021): 147–66. http://dx.doi.org/10.3897/natureconservation.43.63876.
Abstract (sommario):
A particular threat posed by climate change for biodiversity conservation, one which has scarcely been studied, is the overlap of the potential distribution areas in phylogenetically closely related species. In this study, Species Distribution Modelling (SDM) was used to investigate the potential changes in the distribution of Delphinium bolosii and D. fissum subsp. sordidum under future climatic scenarios. These two closely related and endangered endemic species from the Iberian Peninsula do not have complete reproductive barriers between them. The two models selected different predictors with a similar effect in the biological cycle. Both taxa need low winter temperatures to break seed dormancy and sufficient rainfall to complete the flowering and fruiting stages. The current potential distribution areas of both taxa do not currently overlap. However, the results showed that potential changes may take place in the species’ distribution range under future climate scenarios. The models predict a reduction of the potential distribution area of D. bolosii while, conversely, the potential distribution area of D. fissum subsp. sordidum increased. In both cases, the predicted contraction in range is very high, and loss of habitat suitability in some current localities is worrying. Notwithstanding, the models do not predict overlaps of potential areas under climate change scenarios. Our findings can be used to define areas and populations of high priority for conservation or to take action against the impacts of climate change on these endangered species.
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Stephens, Christopher R., Constantino González-Salazar e Pedro Romero-Martínez. "“Does a Respiratory Virus Have an Ecological Niche, and If So, Can It Be Mapped?” Yes and Yes". Tropical Medicine and Infectious Disease 8, n. 3 (17 marzo 2023): 178. http://dx.doi.org/10.3390/tropicalmed8030178.
Abstract (sommario):
Although the utility of Ecological Niche Models (ENM) and Species Distribution Models (SDM) has been demonstrated in many ecological applications, their suitability for modelling epidemics or pandemics, such as SARS-Cov-2, has been questioned. In this paper, contrary to this viewpoint, we show that ENMs and SDMs can be created that can describe the evolution of pandemics, both in space and time. As an illustrative use case, we create models for predicting confirmed cases of COVID-19, viewed as our target “species”, in Mexico through 2020 and 2021, showing that the models are predictive in both space and time. In order to achieve this, we extend a recently developed Bayesian framework for niche modelling, to include: (i) dynamic, non-equilibrium “species” distributions; (ii) a wider set of habitat variables, including behavioural, socio-economic and socio-demographic variables, as well as standard climatic variables; (iii) distinct models and associated niches for different species characteristics, showing how the niche, as deduced through presence-absence data, can differ from that deduced from abundance data. We show that the niche associated with those places with the highest abundance of cases has been highly conserved throughout the pandemic, while the inferred niche associated with presence of cases has been changing. Finally, we show how causal chains can be inferred and confounding identified by showing that behavioural and social factors are much more predictive than climate and that, further, the latter is confounded by the former.
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Zhang, Hong-Xiang, Qian Wang e Zhi-Bin Wen. "Spatial Genetic Structure of Prunus mongolica in Arid Northwestern China Based on RAD Sequencing Data". Diversity 13, n. 8 (23 agosto 2021): 397. http://dx.doi.org/10.3390/d13080397.
Abstract (sommario):
The extensive range of sand deserts, gravel deserts, and recent human activities have shaped habitat fragmentation of relict and endangered plants in arid northwestern China. Prunus mongolica is a relict and endangered shrub that is mainly distributed in the study area. In the present study, population genomics was integrated with a species distribution model (SDM) to investigate the spatial genetic diversity and structure of P. mongolica populations in response to habitat fragmentation and create a proposal for the conservation of this endangered species. The results showed that the northern marginal populations were the first isolated from other populations. The SDM suggested that these marginal populations had low levels of habitat suitability during the glacial period. They could not obtain migration corridors, and thus possessed low levels of gene flow connection with other populations. Additionally, several populations underwent secondarily geographical isolation from other central populations, which preserved particular genetic lineages. Genetic diversity was higher in southern populations than in northern ones. It was concluded that long-term geographical isolation after historical habitat fragmentation promoted the divergence of marginal populations and refugial populations along mountains from other populations. The southern populations could have persisted in their distribution ranges and harbored higher levels of genetic diversity than the northern populations, whose distribution ranges fluctuated in response to paleoclimatic changes. We propose that the marginal populations of P. mongolica should be well considered in conservation management.
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Downie, AL, T. Noble-James, A. Chaverra e KL Howell. "Predicting sea pen (Pennatulacea) distribution on the UK continental shelf: evidence of range modification by benthic trawling". Marine Ecology Progress Series 670 (22 luglio 2021): 75–91. http://dx.doi.org/10.3354/meps13744.
Abstract (sommario):
Sea pen communities are United Nations General Assembly-designated Vulnerable Marine Ecosystems which occur worldwide in soft-bottom sediments where trawling often occurs. However, the ability of marine managers to assess, monitor and mitigate impacts to sea pens at national scales has been constrained by a limited understanding of their environmental requirements, geographical distribution and responses to trawling. In this study, we used random forest species distribution modelling (SDM) to predict the distribution of suitable habitat for 3 sea pen species (tall sea pen Funiculina quadrangularis, slender sea pen Virgularia mirabilis and phosphorescent sea pen Pennatula phosphorea) on the UK continental shelf, exploring the results relative to the distribution of fishing activity. Occurrence of all 3 species corresponded to areas of low current and wave velocity, where suspended matter in the water column was also low. However, for F. quadrangularis, the largest species, the models indicated substantially different drivers of distribution between the Greater North Sea and Celtic Seas ICES Ecoregions. This disparity appears to reflect modification to the range and realised niche of this species in the Greater North Sea, due to trawling impacts. P. phosphorea and V. mirabilis appear to be more resilient to trawling, with no clear negative relationships observed. Our findings illustrate the value of broadscale qualitative comparisons between SDMs and human activity data for insights on pressure-state relationships. When combined with robust distribution maps, this improved understanding of vulnerability will enable marine managers to make ecologically sound, defensible decisions and deliver tangible conservation outcomes for sea pen communities.
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CREGO, RAMIRO D., CLAYTON K. NIELSEN e KARL A. DIDIER. "Climate change and conservation implications for wet meadows in dry Patagonia". Environmental Conservation 41, n. 2 (28 agosto 2013): 122–31. http://dx.doi.org/10.1017/s037689291300026x.
Abstract (sommario):
SUMMARYClimate change is predicted to be a major threat for biodiversity and, from a conservation prospective, it is important to understand how ecosystems may respond to that change. Predicted climate change effects on the distribution of meadows in the arid and semi-arid Argentinean Patagonia by 2050 were assessed for change trends and areas of desertification vulnerability using species distribution models (SDM) and climate-change models. Four modelling techniques composed an ensemble-forecasting approach. Suitable areas for meadows will decrease by 7.85% by 2050 given predicted changes in climate. However, there were two contrasting trends: severe reduction of suitable areas for meadows in north-west Patagonia and Tierra del Fuego Island, and an expansion of suitable areas for meadows in the south and a small section in the north-west. Meadows in Patagonia will likely be impacted by climate change, probably due to changes in precipitation regimes, and consequently many species that rely on meadows in an arid environment will also be impacted. Given the low level of protection of meadows in Patagonia, such information on meadow distribution and vulnerability to climate change will be important for increasing and improving the network of conservation areas through conservation planning.
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Buri, Aline, Carmen Cianfrani, Eric Pinto-Figueroa, Erika Yashiro, Jorge E. Spangenberg, Thierry Adatte, Eric Verrecchia, Antoine Guisan e Jean-Nicolas Pradervand. "Soil factors improve predictions of plant species distribution in a mountain environment". Progress in Physical Geography: Earth and Environment 41, n. 6 (31 ottobre 2017): 703–22. http://dx.doi.org/10.1177/0309133317738162.
Abstract (sommario):
Explanatory studies suggest that using very high resolution (VHR, 1–5 m resolution) topo-climatic predictors may improve the predictive power of plant species distribution models (SDMs). However, the use of VHR topo-climatic data alone was recently shown not to significantly improve SDM predictions. This suggests that new ecologically-meaningful VHR variables based on more direct field measurements are needed, especially since non topo-climatic variables, such as soil parameters, are important for plants. In this study, we investigated the effects of adding mapped VHR predictors at a 5 m resolution, including field measurements of temperature, carbon isotope composition of soil organic matter (δ13CSOM values) and soil pH, to topo-climatic predictors in SDMs for the Swiss Alps. We used data from field temperature loggers to construct temperature maps, and we modelled the geographic variation in δ13CSOM and soil pH values. We then tested the effect of adding these VHR mapped variables as predictors into 154 plant SDMs and assessed the improvement in spatial predictions across the study area. Our results demonstrate that the use of VHR predictors based on more proximal field measurements, particularly soil parameters, can significantly increase the predictive power of models. Predicted soil pH was the second most important predictor after temperature, and predicted δ13CSOM was fourth. The greatest increase in model performance was for species found at high elevation (i.e. 1500–2000 m a.s.l.). Addition of predicted soil factors thus allowed better capturing of plant requirements in our models, showing that these can explain species distributions in ways complementary to topo-climatic variables. Modelling techniques to generalize edaphic information in space and then predict plant species distributions revealed a great potential in complex landscapes such as the mountain region considered in this study.
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Ganesh, S. R., V. A. M. P. K. Samarawickrama, Aishwarya J. Urs, Achyuthan N. Srikanthan e Omkar Dilip Adhikari. "Resurrection of <i>Boiga ranawanei</i> Samarawickrama, Samarawickrama, Wijesena et Orlov, 2005, with Expanded Descriptions and Species Distribution Modelling of Some Indian Taxa of the <i>B. ceylonensis</i> Group (Reptilia: Colubridae)". Russian Journal of Herpetology 29, n. 6 (11 dicembre 2022): 341–54. http://dx.doi.org/10.30906/1026-2296-2022-29-6-341-354.
Abstract (sommario):
We resurrect the nominal taxon Boiga ranawanei Samarawickrama, Samarawickrama, Wijesena et Orlov, 2005 as a valid species endemic to Sri Lanka. We uphold the view that B. ranawanei is conspecific with what was previously considered as the Sri Lankan population of ‘B. beddomei’. The revised concept of B. beddomei as a species endemic to the Western Ghats of India and altogether absent from Sri Lanka necessitated the Sri Lankan ‘B. beddomei’ population be conferred with the available nomen B. ranawanei. We also report on further specimens of the two Indian species closely associated with B. ranawanei – B. beddomei that was associated in a nomenclatural sense and B. flaviviridis that is associated in a taxonomic sense. Here, we expand the descriptions of B. beddomei and especially that of B. flaviviridis based on many more additional specimens from several localities across Peninsular India. We again update the key to this group with the inclusion of B. ranawanei. We perform Species Distribution Modelling (SDM) to elaborate on the recorded and simulated distribution range envelopes of the Indian taxa (B. flaviviridis, B. beddomei, B. nuchalis, B. thackerayi) in the B. ceylonesis group. Thus we explain the geographical discontinuity among these taxa to further corroborate our findings on their taxonomic statuses.
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Hidayat, A., Y. B. Sulistioadi, W. Kustiawan, Sukartiningsih, Z. Haryanto e R. B. Suba. "Habitat suitability of Proboscis Monkey Nasalis larvatus Wurmb. in Muara Kaman Sedulang Nature Reserve, East Kalimantan". IOP Conference Series: Earth and Environmental Science 1282, n. 1 (1 dicembre 2023): 012018. http://dx.doi.org/10.1088/1755-1315/1282/1/012018.
Abstract (sommario):
Abstract Proboscis monkey (Nasalis larvatus Wurmb.) is an endemic primate species to Borneo’s island. Muara Kaman Sedulang Nature Reserve is one of protected area for this species in the island, but it’s riverine habitat is largely lost by forest fires and conversion for aquaculture, agriculture and settlements. A conservation action in the area needs spatial information regarding the habitat suitability of proboscis monkey. This study aimed to project a spatial model for habitat suitability of proboscis monkey in the reserve through species distribution modelling (SDM) using the Maximum Entropy (MaxEnt), based on the species’ occurrence records and environmental predictors. The results showed that suitable habitat for proboscis monkey was 3,410.27 ha (5.22% of the nature reserve total area). Model performance evaluation shows an accuracy of the Area Under Curve (AUC) value of 0.940, which is a very good category. Jackknife-test results showed the presence of secondary swamp forest (40.8%), distance from water bodies (25.9%), land cover (12.4%), secondary forest (6.3%) and mixed gardens (5.1%) are five variables that significantly contribute to the distribution of proboscis monkeys in the reserve. This study provides suggestions for conservation of proboscis monkey in remaining suitable habitat, which include local people involvement, monitoring and habitat restoration.
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Shashkov, M. P. "Open Data Sources for Species Distribution Modelling: Biodiversity Information Systems and Spatial Datasets of Environmental Conditions Variables". Raptors Conservation, n. 2 (2023): 358–62. http://dx.doi.org/10.19074/1814-8654-2023-2-358-362.
Abstract (sommario):
BIOKLIM, the first algorithm for habitat modelling (Species Distribution Modelling (SDM)), was developed in the 1980s. This area of population and ecological research began to gain prominence with the wide availability of computers, development of the Internet, and development of open resources that provide access to data on species occurrences and environmental factors. Most algorithms for SDM (with the exception of the first “bioclimatic envelope” methods) are based on regression analysis and machine learning. The most commonly used today is the MaxEnt maximum entropy method. All methods achieve the objective of revealing quantitative relationships between occurrences of the focal species and environmental variable values where thespecies occurs, with subsequent extrapolation of the ensuing patterns across the entire study area. The result is an assessment of habitat suitability (probability of occurrence) for the species within the study area. Species distribution modelling methods are implemented both as standalone software products (MaxEnt) and as modules for GIS (and for QGIS, SDMToolbox for ArcGIS, etc.) and packages for the R environment (dismo, biomod2, ENMTools, etc.). Any species distribution modelling method requires two types of input data: (1) occurrences of the focal species, represented as a set of points with geographic coordinates; and (2) environmental variables (predictors) that may be valuable for species distribution, in the format of continuous raster layers. Considerable advances in the digitization of scientific collections around the globe and development of other sources for species distribution data have made it possible for researchers to significantly augment their own data to develop more accurate models. Such data are available through thematic repositories, the largest of which is the Global Biodiversity Information Facility – GBIF, which currently provides over 2.5 billion occurrences, twothirds of which relate to birds. Along with data derived from scientific collections, GBIF hosts data from multiple citizen science systems as well. The largest of these is eBird, with 1,277.5 million observations. The iNaturalist system has about 20 million bird observations. A much smaller fraction of data comes from biological collections (8.5 million) and automatic observation systems (camera traps and satellite trackers, 9.5 million). GBIF has accumulated 195,000 bird observations in Kazakhstan, in addition to the above-mentioned data, originating in the following observation systems: Raptors of the World, RU-BIRDS. RU, Hatikka.fi, and Observation.org. The volume of available data on focal species occurrence can reach tens of thousands of records, but much less is required for modelling; for this reason, data filtering and quality control are important steps. When compiling an input dataset of occurrences, the researcher must consider the biological features of focal species. In birds, the circumstances in which a particular individual was encountered is important: on the nest, while hunting during nesting, overwintering, migrating, etc., as well as age group. It is also necessary to note which part of the range is used in the model: breeding, wintering ground, or year-round presence. The records of target species’ occurrences should be more or less evenly distributed over the area of interest, should not raise questions regarding identification, and should have a geographical accuracy comparable to the resolution of the predictor layers used. The environmental variables most in demand are bioclimatic data from the World-Clim resource. Those data reflect the distribution of precipitation and long-term average temperature. Information on soil conditions is provided by SoilGrid250. Layers for land surface classification by habitat type are also available: qualitative (Global Land Cover 2000) and quantitative (Global 1-km Consensus Land Cover). Remote sensing imagery data from the Landsat and Sentinel satellite series are often used as predictors. Both particular image channels and layers with indexes calculated on that basis (e.g., NDVI – Normalized Difference Vegetation Index) can be included in the analysis. The SRTM (Shuttle Radar Topography Mission) digital surface model is also in wide use. It is important to test the predictors for multicollinearity, as strongly correlated factors will introduce uncertainty in the resulting model. The test is performed over the set of values that spatially correspond to the species occurrences, rather than over the entire area of the layers. Among two correlated layers, the less environmentally dependent one is usually left, for which the working hypothesis is tested or allowing comparison of the results with other studies. It is recommended that correlation coefficient values > 0.7 be taken as critical. Predictor selection should be based on focal species biology and ecology. For some species, topography may be important, not only elevation but also, for example, slope steepness. In species associated with wetlands, it is important to include layers related to the hydrological network. The influence of factors can be both direct and indirect. For example, a particular bird species nests in an area with a certain range of mean annual temperatures, but at the local level, it chooses habitats rich in food resources, which in turn may be associated with certain soil characteristics or vegetation types. Therefore, initial model builds typically use multiple layers of environmental variables to identify significant factors and the nature of their influence on the probability of encountering the target species. Usually, no more than ten predictors remain in the final model. There must be at least ten points of occurrence of the focal species for each predictor to build a good quality model.
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Seid, Mohammed Adefa, Binyam Tesfaw Hailu, Tigist Wondimu e Sileshi Nemomissa. "Modelling and Mapping Habitat Suitability for B. aegyptiaca (L.) Del. and B. rotundifolia (Tiegh.) Blatt. under Climate Change in Ethiopia". International Journal of Ecology 2024 (30 maggio 2024): 1–18. http://dx.doi.org/10.1155/2024/6656529.
Abstract (sommario):
Climate change impacts are posing greater risks to biodiversity, food security, and livelihood in Africa, specifically in arid and semi-arid environments. In Ethiopia, the genus Balanites Del., which belongs to the monogeneric family called Balanitaceae, has the multipurpose B. aegyptiaca and B. rotundifolia. However, these species are overlooked and endangered by climate change, and their species distributions are not well documented and understood in Ethiopia. Therefore, this study aimed to model the habitat suitability of these two species using current occurrence data, climate, and landscape data and predict their distribution under climate change. Occurrence points of B. aegyptiaca (n = 224) and B. rotundifolia (n = 80) were collected from field surveys and herbarium. Bioclimatic (WorldClim.v2), soil, and landscape variables were used in the ensemble species distribution models (SDMs) using six algorithms (GLM, GAM, BRT, RF, MARS, and SVM). The ensemble SDMs under the current climate showed that the Central Ethiopian Rift Valley is highly suitable habitats for B. aegyptiaca accounting for an area of 114,517 km2, and the Southern Ethiopian Rift Valley is highly suitable habitats for B. rotundifolia accounting for an area of 41,373 km2. The performance of ensemble SDM under the current climate for B. aegyptiaca showed 0.95 AUC, 0.80 TSS, 0.79 COR, and 0.87 deviance; and that of B. rotundifolia with 0.90 AUC, 0.80 TSS, 0.80 COR, and 0.50 deviance. Temperature annual range (Bio07) and precipitation seasonality (Bio15) for B. aegyptiaca; and precipitation of driest quarter (Bio17) and annual precipitation (Bio12) for B. rotundifolia are the most key bioclimatic variables that affect their distributions. The ensemble SDMs under SSP2-45 and SSP5-85 (HadGEM3-GC31-LL) climates showed that the highly suitable areas will remain suitable for both species (B. aegyptiaca with 116,934 km2 area cover (ΔA = +2.1%) and 125,757 km2 area cover (ΔA = +7.5%) and B. rotundifolia with 29,547 km2 area cover (ΔA = −28.6%) and 50, 894 km2 area cover (ΔA = +23%), respectively). The findings of this study implicated that the Ethiopian Rift Valley region in general for B. aegyptiaca and the Southern Rift Valley of Ethiopia for B. rotundifolia are suitable areas for conservation and sustainable use of the species.
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Amspacher, Katelyn, F. Agustín Jiménez e Clayton Nielsen. "Influence of Habitat on Presence of Striped Skunks in Midwestern North America". Diversity 13, n. 2 (18 febbraio 2021): 83. http://dx.doi.org/10.3390/d13020083.
Abstract (sommario):
Striped skunks (Mephitis mephitis) are urban-adapted, generalist mesocarnivores widely distributed throughout North America. Although striped skunks have been studied extensively at small scales, knowledge of habitat influences on striped skunks at large scales is lacking. We developed a species distribution model (SDM) to examine potential striped skunk presence in a 16,058 km2 portion of southern Illinois, USA. We built models using SDM Toolbox and MaxEnt, and incorporated known presence locations, 1 km2 land cover data, and an index of human modification of the landscape. Land cover and human modification explained 98% and 2% of variation in our model, respectively. The highest presence of striped skunks existed in areas with forest cover and developed open space with moderate human modification. The striped skunk presence was lowest in areas with cultivated crops and woody wetlands with either low or high human modification. Forest cover provides natural food and shelter resources for striped skunks, but resources are likely augmented by human activity in developed open space. Cultivated crops only provide seasonal resources, and inundation limits denning in wooded wetlands. Our model indicated striped skunks are a synanthropic species that regularly inhabits both natural and anthropogenic habitats over a large scale.
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Nnko, Happiness Jackson, Paul Simon Gwakisa, Anibariki Ngonyoka, Calvin Sindato e Anna Bond Estes. "Potential impacts of climate change on geographical distribution of three primary vectors of African Trypanosomiasis in Tanzania’s Maasai Steppe: G. m. morsitans, G. pallidipes and G. swynnertoni". PLOS Neglected Tropical Diseases 15, n. 2 (11 febbraio 2021): e0009081. http://dx.doi.org/10.1371/journal.pntd.0009081.
Abstract (sommario):
In the Maasai Steppe, public health and economy are threatened by African Trypanosomiasis, a debilitating and fatal disease to livestock (African Animal Trypanosomiasis -AAT) and humans (Human African Trypanosomiasis—HAT), if not treated. The tsetse fly is the primary vector for both HAT and AAT and climate is an important predictor of their occurrence and the parasites they carry. While understanding tsetse fly distribution is essential for informing vector and disease control strategies, existing distribution maps are old and were based on coarse spatial resolution data, consequently, inaccurately representing vector and disease dynamics necessary to design and implement fit-for-purpose mitigation strategies. Also, the assertion that climate change is altering tsetse fly distribution in Tanzania lacks empirical evidence. Despite tsetse flies posing public health risks and economic hardship, no study has modelled their distributions at a scale needed for local planning. This study used MaxEnt species distribution modelling (SDM) and ecological niche modeling tools to predict potential distribution of three tsetse fly species in Tanzania’s Maasai Steppe from current climate information, and project their distributions to midcentury climatic conditions under representative concentration pathways (RCP) 4.5 scenarios. Current climate results predicted that G. m. morsitans, G. pallidipes and G swynnertoni cover 19,225 km2, 7,113 km2 and 32,335 km2 and future prediction indicated that by the year 2050, the habitable area may decrease by up to 23.13%, 12.9% and 22.8% of current habitable area, respectively. This information can serve as a useful predictor of potential HAT and AAT hotspots and inform surveillance strategies. Distribution maps generated by this study can be useful in guiding tsetse fly control managers, and health, livestock and wildlife officers when setting surveys and surveillance programs. The maps can also inform protected area managers of potential encroachment into the protected areas (PAs) due to shrinkage of tsetse fly habitats outside PAs.
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FERRAZ, KATIA MARIA PASCHOALETTO MICCHI DE BARROS, MARINEZ FERREIRA DE SIQUEIRA, EDUARDO ROBERTO ALEXANDRINO, DANIELA TOMASIO APOLINARIO DA LUZ e HILTON THADEU ZARATE DO COUTO. "Environmental suitability of a highly fragmented and heterogeneous landscape for forest bird species in south-eastern Brazil". Environmental Conservation 39, n. 4 (1 maggio 2012): 316–24. http://dx.doi.org/10.1017/s0376892912000094.
Abstract (sommario):
SUMMARYAssessment of the suitability of anthropogenic landscapes for wildlife species is crucial for setting priorities for biodiversity conservation. This study aimed to analyse the environmental suitability of a highly fragmented region of the Brazilian Atlantic Forest, one of the world's 25 recognized biodiversity hotspots, for forest bird species. Eight forest bird species were selected for the analyses, based on point counts (n = 122) conducted in April–September 2006 and January–March 2009. Six additional variables (landscape diversity, distance from forest and streams, aspect, elevation and slope) were modelled in Maxent for (1) actual and (2) simulated land cover, based on the forest expansion required by existing Brazilian forest legislation. Models were evaluated by bootstrap or jackknife methods and their performance was assessed by AUC, omission error, binomial probability or p value. All predictive models were statistically significant, with high AUC values and low omission errors. A small proportion of the actual landscape (24.41 ± 6.31%) was suitable for forest bird species. The simulated landscapes lead to an increase of c.30% in total suitable areas. In average, models predicted a small increase (23.69 ± 6.95%) in the area of suitable native forest for bird species. Being close to forest increased the environmental suitability of landscapes for all bird species; landscape diversity was also a significant factor for some species. In conclusion, this study demonstrates that species distribution modelling (SDM) successfully predicted bird distribution across a heterogeneous landscape at fine spatial resolution, as all models were biologically relevant and statistically significant. The use of landscape variables as predictors contributed significantly to the results, particularly for species distributions over small extents and at fine scales. This is the first study to evaluate the environmental suitability of the remaining Brazilian Atlantic Forest for bird species in an agricultural landscape, and provides important additional data for regional environmental planning.
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Wang, Bin, Feng Xie, Jiannan Li, Gang Wang, Cheng Li e Jianping Jiang. "Phylogeographic investigation and ecological niche modelling of the endemic frog speciesNanorana pleskeirevealed multiple refugia in the eastern Tibetan Plateau". PeerJ 5 (11 settembre 2017): e3770. http://dx.doi.org/10.7717/peerj.3770.
Abstract (sommario):
The largest plateau Tibetan Plateau supplied an excellent opportunity to investigate the influence of the Pleistocene events on the high-elevation species. To test for the alternative hypotheses of Pleistocene glacial refugia, we used partial sequences of two mitochondrial genes and one nuclear gene to examine the phylogeographic patterns of the endemic frog speciesNanorana pleskeiacross its known range in the eastern Tibetan Plateau, and conducted species distribution modelling (SDM) to explore changes of its distribution range through current and paleo periods. In all data sets, the species was divided into lineage north occupying open plateau platform and lineage south colonizing the mountainous plateau. The divergence of two major clades was estimated at the early Pleistocene. In mtDNA, lineage north contained northeastern and northwestern sublineages, and lineage south had two overlapping-distributed sublineages. Different lineages possessed distinct demographic characteristics, i.e., subdivision in the northeastern sublineage, historical bottleneck effects and recent expansions in the northwestern sublineage and the southeastern sublineage. SDMs depicted that stable suitable habitats had existed in the upper-middle streams of the Yellow River, Dadu River, Jinsha River and Yalong River. These regions were also recognized as the ancestral areas of different lineages. In conclusion,Nanorana pleskeilineages have probably experienced long-term separations. Stable suitable habitats existing in upper-middle streams of major rivers on the eastern Tibetan Plateau and distinct demographic dynamics of different lineages indicated that the lineages possessed independent evolutionary processes in multiple glacial refugia. The findings verified the profound effects of Pleistocene climatic fluctuations on the plateau endemic species.
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Anand, Akash, Manish K. Pandey, Prashant K. Srivastava, Ayushi Gupta e Mohammed Latif Khan. "Integrating Multi-Sensors Data for Species Distribution Mapping Using Deep Learning and Envelope Models". Remote Sensing 13, n. 16 (19 agosto 2021): 3284. http://dx.doi.org/10.3390/rs13163284.
Abstract (sommario):
The integration of ecological and atmospheric characteristics for biodiversity management is fundamental for long-term ecosystem conservation and drafting forest management strategies, especially in the current era of climate change. The explicit modelling of regional ecological responses and their impact on individual species is a significant prerequisite for any adaptation strategy. The present study focuses on predicting the regional distribution of Rhododendron arboreum, a medicinal plant species found in the Himalayan region. Advanced Species Distribution Models (SDM) based on the principle of predefined hypothesis, namely BIOCLIM, was used to model the potential distribution of Rhododendron arboreum. This hypothesis tends to vary with the change in locations, and thus, robust models are required to establish nonlinear complex relations between the input parameters. To address this nonlinear relation, a class of deep neural networks, Convolutional Neural Network (CNN) architecture is proposed, designed, and tested, which eventually gave much better accuracy than the BIOCLIM model. Both of the models were given 16 input parameters, including ecological and atmospheric variables, which were statistically resampled and were then utilized in establishing the linear and nonlinear relationship to better fit the occurrence scenarios of the species. The input parameters were mostly acquired from the recent satellite missions, including MODIS, Sentinel-2, Sentinel-5p, the Shuttle Radar Topography Mission (SRTM), and ECOSTRESS. The performance across all the thresholds was evaluated using the value of the Area Under Curve (AUC) evaluation metrics. The AUC value was found to be 0.917 with CNN, whereas it was 0.68 with BIOCLIM, respectively. The performance evaluation metrics indicate the superiority of CNN for species distribution over BIOCLIM.