Статті в журналах: "Joint species distribution models"

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Zurell, Damaris, Niklaus E. Zimmermann, Helge Gross, Andri Baltensweiler, Thomas Sattler, and Rafael O. Wüest. "Testing species assemblage predictions from stacked and joint species distribution models." Journal of Biogeography 47, no. 1 (June 5, 2019): 101–13. http://dx.doi.org/10.1111/jbi.13608.

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Wilkinson, David P., Nick Golding, Gurutzeta Guillera‐Arroita, Reid Tingley, and Michael A. McCarthy. "A comparison of joint species distribution models for presence–absence data." Methods in Ecology and Evolution 10, no. 2 (November 3, 2018): 198–211. http://dx.doi.org/10.1111/2041-210x.13106.

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3

Yong, Juan, Guangshuang Duan, Shaozhi Chen, and Xiangdong Lei. "Environmental Response of Tree Species Distribution in Northeast China with the Joint Species Distribution Model." Forests 15, no. 6 (June 13, 2024): 1026. http://dx.doi.org/10.3390/f15061026.

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The composition, distribution, and growth of native natural forests are important references for the restoration, structural adjustment, and close-to-nature transformation of artificial forests. The joint species distribution model is a powerful tool for analyzing community structure and interspecific relationships. It has been widely used in biogeography, community ecology, and animal ecology, but it has not been extended to natural forest conservation and restoration in China. Therefore, based on the 9th National Forest Inventory data in Jilin Province, combined with environmental factors and functional traits of tree species, this study adopted the joint species distribution model—including a model with all variables (model FULL), a model with environmental factors (model ENV), and a model with spatial factors (model SPACE)—to examine the distribution of multiple tree species. The results show that, in models FULL and ENV, the environmental factors explaining the model variation were ranked as follows, climate > site > soil. The explanatory power was as follows: model FULL (AUC = 0.8325, Tjur R2 = 0.2326) > model ENV (AUC = 0.7664, Tjur R2 = 0.1454) > model SPACE (AUC = 0.7297, Tjur R2 = 0.1346). Tree species niches in model ENV were similar to those in model FULL. Compared to predictive power, we found that the information transmitted by environmental and spatial predictors overlaps, so the choice between model FULL and ENV should be based on the purpose of the model, rather than the difference in predictive ability. Both models can be used to study the adaptive distribution of multiple tree species in northeast China.

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Ovaskainen, Otso, David B. Roy, Richard Fox, and Barbara J. Anderson. "Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models." Methods in Ecology and Evolution 7, no. 4 (December 18, 2015): 428–36. http://dx.doi.org/10.1111/2041-210x.12502.

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D’Acunto, Laura E., Leonard Pearlstine, and Stephanie S. Romañach. "Joint species distribution models of Everglades wading birds to inform restoration planning." PLOS ONE 16, no. 1 (January 28, 2021): e0245973. http://dx.doi.org/10.1371/journal.pone.0245973.

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Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0–1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

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Hogg, Stephanie Elizabeth, Yan Wang, and Lewi Stone. "Effectiveness of joint species distribution models in the presence of imperfect detection." Methods in Ecology and Evolution 12, no. 8 (July 9, 2021): 1458–74. http://dx.doi.org/10.1111/2041-210x.13614.

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König, Christian, Rafael O. Wüest, Catherine H. Graham, Dirk Nikolaus Karger, Thomas Sattler, Niklaus E. Zimmermann, and Damaris Zurell. "Scale dependency of joint species distribution models challenges interpretation of biotic interactions." Journal of Biogeography 48, no. 7 (April 2021): 1541–51. http://dx.doi.org/10.1111/jbi.14106.

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Gavin, Daniel G., Matthew C. Fitzpatrick, Paul F. Gugger, Katy D. Heath, Francisco Rodríguez-Sánchez, Solomon Z. Dobrowski, Arndt Hampe, et al. "Climate refugia: joint inference from fossil records, species distribution models and phylogeography." New Phytologist 204, no. 1 (July 16, 2014): 37–54. http://dx.doi.org/10.1111/nph.12929.

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Tikhonov, Gleb, Nerea Abrego, David Dunson, and Otso Ovaskainen. "Using joint species distribution models for evaluating how species‐to‐species associations depend on the environmental context." Methods in Ecology and Evolution 8, no. 4 (April 2017): 443–52. http://dx.doi.org/10.1111/2041-210x.12723.

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Wagner, Tyler, Gretchen J. A. Hansen, Erin M. Schliep, Bethany J. Bethke, Andrew E. Honsey, Peter C. Jacobson, Benjamen C. Kline, and Shannon L. White. "Improved understanding and prediction of freshwater fish communities through the use of joint species distribution models." Canadian Journal of Fisheries and Aquatic Sciences 77, no. 9 (September 2020): 1540–51. http://dx.doi.org/10.1139/cjfas-2019-0348.

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Two primary goals in fisheries research are to (i) understand how habitat and environmental conditions influence the distribution of fishes across the landscape and (ii) make predictions about how fish communities will respond to environmental and anthropogenic change. In inland, freshwater ecosystems, quantitative approaches traditionally used to accomplish these goals largely ignore the effects of species interactions (competition, predation, mutualism) on shaping community structure, potentially leading to erroneous conclusions regarding habitat associations and unrealistic predictions about species distributions. Using two contrasting case studies, we highlight how joint species distribution models (JSDMs) can address the aforementioned deficiencies by simultaneously quantifying the effects of abiotic habitat variables and species dependencies. In particular, we show that conditional predictions of species occurrence from JSDMs can better predict species presence or absence compared with predictions that ignore species dependencies. JSDMs also allow for the estimation of site-specific probabilities of species co-occurrence, which can be informative for generating hypotheses about species interactions. JSDMs provide a flexible framework that can be used to address a variety of questions in fisheries science and management.

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Briscoe Runquist, Ryan D., Thomas A. Lake, and David A. Moeller. "Improving predictions of range expansion for invasive species using joint species distribution models and surrogate co‐occurring species." Journal of Biogeography 48, no. 7 (March 24, 2021): 1693–705. http://dx.doi.org/10.1111/jbi.14105.

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Thorson, James T., James N. Ianelli, Elise A. Larsen, Leslie Ries, Mark D. Scheuerell, Cody Szuwalski, and Elise F. Zipkin. "Joint dynamic species distribution models: a tool for community ordination and spatio-temporal monitoring." Global Ecology and Biogeography 25, no. 9 (May 29, 2016): 1144–58. http://dx.doi.org/10.1111/geb.12464.

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13

Deneu, Benjamin, Maximilien Servajean, Pierre Bonnet, Christophe Botella, François Munoz, and Alexis Joly. "Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment." PLOS Computational Biology 17, no. 4 (April 19, 2021): e1008856. http://dx.doi.org/10.1371/journal.pcbi.1008856.

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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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Rahman, Anis Ur, Gleb Tikhonov, Jari Oksanen, Tuomas Rossi, and Otso Ovaskainen. "Accelerating joint species distribution modelling with Hmsc-HPC by GPU porting." PLOS Computational Biology 20, no. 9 (September 3, 2024): e1011914. http://dx.doi.org/10.1371/journal.pcbi.1011914.

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Joint species distribution modelling (JSDM) is a widely used statistical method that analyzes combined patterns of all species in a community, linking empirical data to ecological theory and enhancing community-wide prediction tasks. However, fitting JSDMs to large datasets is often computationally demanding and time-consuming. Recent studies have introduced new statistical and machine learning techniques to provide more scalable fitting algorithms, but extending these to complex JSDM structures that account for spatial dependencies or multi-level sampling designs remains challenging. In this study, we aim to enhance JSDM scalability by leveraging high-performance computing (HPC) resources for an existing fitting method. Our work focuses on the Hmsc R-package, a widely used JSDM framework that supports the integration of various dataset types into a single comprehensive model. We developed a GPU-compatible implementation of its model-fitting algorithm using Python and the TensorFlow library. Despite these changes, our enhanced framework retains the original user interface of the Hmsc R-package. We evaluated the performance of the proposed implementation across various model configurations and dataset sizes. Our results show a significant increase in model fitting speed for most models compared to the baseline Hmsc R-package. For the largest datasets, we achieved speed-ups of over 1000 times, demonstrating the substantial potential of GPU porting for previously CPU-bound JSDM software. This advancement opens promising opportunities for better utilizing the rapidly accumulating new biodiversity data resources for inference and prediction.

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Caradima, Bogdan, Nele Schuwirth, and Peter Reichert. "From individual to joint species distribution models: A comparison of model complexity and predictive performance." Journal of Biogeography 46, no. 10 (August 8, 2019): 2260–74. http://dx.doi.org/10.1111/jbi.13668.

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16

Clark, James S., Alan E. Gelfand, Christopher W. Woodall, and Kai Zhu. "More than the sum of the parts: forest climate response from joint species distribution models." Ecological Applications 24, no. 5 (July 2014): 990–99. http://dx.doi.org/10.1890/13-1015.1.

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17

Zhang, Chongliang, Yong Chen, Binduo Xu, Ying Xue, and Yiping Ren. "Comparing the prediction of joint species distribution models with respect to characteristics of sampling data." Ecography 41, no. 11 (February 26, 2018): 1876–87. http://dx.doi.org/10.1111/ecog.03571.

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18

Zhang, Chongliang, Yong Chen, Binduo Xu, Ying Xue, and Yiping Ren. "Evaluating the influence of spatially varying catchability on multispecies distribution modelling." ICES Journal of Marine Science 77, no. 5 (May 9, 2020): 1841–53. http://dx.doi.org/10.1093/icesjms/fsaa068.

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Abstract Varying catchability is a common feature in fisheries and has great impacts on fisheries assessments and species distribution models. However, spatial variations in catchability have been rarely evaluated, especially in the multispecies context. We advocate that the need for multispecies models stands for both challenges and opportunities to handle spatial catchability. This study evaluated the influence of spatially varying catchability on the performance of a novel joint species distribution model, namely Hierarchical Modelling of Species Communities (HMSC). We implemented the model under nine simulation scenarios to account for diverse spatial patterns of catchability and conducted empirical tests using survey data from Yellow Sea, China. Our results showed that ignoring variability in catchability could lead to substantial errors in the inferences of species response to environment. Meanwhile, the models’ predictive power was less impacted, yielding proper predictions of relative abundance. Incorporating a spatially autocorrelated structure substantially improved the predictability of HMSC in both simulation and empirical tests. Nevertheless, combined sources of spatial catchabilities could largely diminish the advantage of HMSC in inference and prediction. We highlight situations where catchability needs to be explicitly accounted for in modelling fish distributions, and suggest directions for future applications and development of JSDMs.

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CARDADOR, LAURA, JOSÉ A. DÍAZ-LUQUE, FERNANDO HIRALDO, JAMES D. GILARDI, and JOSÉ L. TELLA. "The effects of spatial survey bias and habitat suitability on predicting the distribution of threatened species living in remote areas." Bird Conservation International 28, no. 4 (October 17, 2017): 581–92. http://dx.doi.org/10.1017/s0959270917000144.

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SummaryKnowledge of a species’ potential distribution and the suitability of available habitat are fundamental for effective conservation planning and management. However, the quality of information on the distribution of species and their required habitats is highly variable in terms of accuracy and availability across taxa and regions, particularly in tropical landscapes where accessibility is especially challenging. Species distribution models (SDMs) provide predictive tools for addressing gaps for poorly surveyed species, but they rarely consider biases in geographical distribution of records and their consequences. We applied SDMs and variation partitioning analyses to investigate the relative importance of habitat characteristics, human accessibility, and their joint effects in the global distribution of the Critically Endangered Blue-throated MacawAra glaucogularis, a species endemic to the Amazonian flooded savannas of Bolivia. The probability of occurrence was skewed towards more accessible areas, mostly secondary roads. Variability in observed occurrence patterns was mostly accounted for by the pure effect of habitat characteristics (76.2%), indicating that bias in the geographical distribution of occurrences does not invalidate species-habitat relationships derived from niche models. However, observed spatial covariation between land-use at a landscape scale and accessibility (joint contribution: 22.3%) may confound the independent role of land-use in the species distribution. New surveys should prioritise collecting data in more remote (less accessible) areas better distributed with respect to land-use composition at a landscape scale. Our results encourage wider application of partitioning methods to quantify the extent of sampling bias in datasets used in habitat modelling for a better understanding of species-habitat relationships, and add insights into the potential distribution of our study species and opportunities for its conservation.

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Banville, Francis, Dominique Gravel, and Timothée Poisot. "What constrains food webs? A maximum entropy framework for predicting their structure with minimal biases." PLOS Computational Biology 19, no. 9 (September 5, 2023): e1011458. http://dx.doi.org/10.1371/journal.pcbi.1011458.

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Food webs are complex ecological networks whose structure is both ecologically and statistically constrained, with many network properties being correlated with each other. Despite the recognition of these invariable relationships in food webs, the use of the principle of maximum entropy (MaxEnt) in network ecology is still rare. This is surprising considering that MaxEnt is a statistical tool precisely designed for understanding and predicting many types of constrained systems. This principle asserts that the least-biased probability distribution of a system’s property, constrained by prior knowledge about that system, is the one with maximum information entropy. MaxEnt has been proven useful in many ecological modeling problems, but its application in food webs and other ecological networks is limited. Here we show how MaxEnt can be used to derive many food-web properties both analytically and heuristically. First, we show how the joint degree distribution (the joint probability distribution of the numbers of prey and predators for each species in the network) can be derived analytically using the number of species and the number of interactions in food webs. Second, we present a heuristic and flexible approach of finding a network’s adjacency matrix (the network’s representation in matrix format) based on simulated annealing and SVD entropy. We built two heuristic models using the connectance and the joint degree sequence as statistical constraints, respectively. We compared both models’ predictions against corresponding null and neutral models commonly used in network ecology using open access data of terrestrial and aquatic food webs sampled globally (N = 257). We found that the heuristic model constrained by the joint degree sequence was a good predictor of many measures of food-web structure, especially the nestedness and motifs distribution. Specifically, our results suggest that the structure of terrestrial and aquatic food webs is mainly driven by their joint degree distribution.

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Viljanen, Markus, Lisa Tostrams, Niels Schoffelen, Jan van de Kassteele, Leon Marshall, Merijn Moens, Wouter Beukema, and Wieger Wamelink. "A joint model for the estimation of species distributions and environmental characteristics from point-referenced data." PLOS ONE 19, no. 6 (June 21, 2024): e0304942. http://dx.doi.org/10.1371/journal.pone.0304942.

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Background Predicting and explaining species occurrence using environmental characteristics is essential for nature conservation and management. Species distribution models consider species occurrence as the dependent variable and environmental conditions as the independent variables. Suitable conditions are estimated based on a sample of species observations, where one assumes that the underlying environmental conditions are known. This is not always the case, as environmental variables at broad spatial scales are regularly extrapolated from point-referenced data. However, treating the predicted environmental conditions as accurate surveys of independent variables at a specific point does not take into account their uncertainty. Methods We present a joint hierarchical Bayesian model where models for the environmental variables, rather than a set of predicted values, are input to the species distribution model. All models are fitted together based only on point-referenced observations, which results in a correct propagation of uncertainty. We use 50 plant species representative of the Dutch flora in natural areas with 8 soil condition predictors taken during field visits in the Netherlands as a case study. We compare the proposed model to the standard approach by studying the difference in associations, predicted maps, and cross-validated accuracy. Findings We find that there are differences between the two approaches in the estimated association between soil conditions and species occurrence (correlation 0.64-0.84), but the predicted maps are quite similar (correlation 0.82-1.00). The differences are more pronounced in the rarer species. The cross-validated accuracy is substantially better for 5 species out of the 50, and the species can also help to predict the soil characteristics. The estimated associations tend to have a smaller magnitude with more certainty. Conclusion These findings suggests that the standard model is often sufficient for prediction, but effort should be taken to develop models which take the uncertainty in the independent variables into account for interpretation.

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Neves, Tomé, Luís Borda-de-Água, Maria da Luz Mathias, and Joaquim T. Tapisso. "The Influence of the Interaction between Climate and Competition on the Distributional Limits of European Shrews." Animals 12, no. 1 (December 28, 2021): 57. http://dx.doi.org/10.3390/ani12010057.

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It is known that species’ distributions are influenced by several ecological factors. Nonetheless, the geographical scale upon which the influence of these factors is perceived is largely undefined. We assessed the importance of competition in regulating the distributional limits of species at large geographical scales. We focus on species with similar diets, the European Soricidae shrews, and how interspecific competition changes along climatic gradients. We used presence data for the seven most widespread terrestrial species of Soricidae in Europe, gathered from GBIF, European museums, and climate data from WorldClim. We made use of two Joint Species Distribution Models to analyse the correlations between species’ presences, aiming to understand the distinct roles of climate and competition in shaping species’ distributions. Our results support three key conclusions: (i) climate alone does not explain all species’ distributions at large scales; (ii) negative interactions, such as competition, seem to play a strong role in defining species’ range limits, even at large scales; and (iii) the impact of competition on a species’ distribution varies along a climatic gradient, becoming stronger at the climatic extremes. Our conclusions support previous research, highlighting the importance of considering biotic interactions when studying species’ distributions, regardless of geographical scale.

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Zurell, Damaris, Laura J. Pollock, and Wilfried Thuiller. "Do joint species distribution models reliably detect interspecific interactions from co-occurrence data in homogenous environments?" Ecography 41, no. 11 (April 19, 2018): 1812–19. http://dx.doi.org/10.1111/ecog.03315.

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Bokhutlo, Thethela, Eduardo R. Cunha, and Kirk O. Winemiller. "Inference of Fish Community Assembly in Intermittent Rivers Using Joint Species Distribution Models and Trophic Guilds." Open Journal of Ecology 13, no. 07 (2023): 497–515. http://dx.doi.org/10.4236/oje.2023.137030.

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Thorson, James T., and Lewis A. K. Barnett. "Comparing estimates of abundance trends and distribution shifts using single- and multispecies models of fishes and biogenic habitat." ICES Journal of Marine Science 74, no. 5 (January 14, 2017): 1311–21. http://dx.doi.org/10.1093/icesjms/fsw193.

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Several approaches have been developed over the last decade to simultaneously estimate distribution or density for multiple species (e.g. “joint species distribution” or “multispecies occupancy” models). However, there has been little research comparing estimates of abundance trends or distribution shifts from these multispecies models with similar single-species estimates. We seek to determine whether a model including correlations among species (and particularly species that may affect habitat quality, termed “biogenic habitat”) improves predictive performance or decreases standard errors for estimates of total biomass and distribution shift relative to similar single-species models. To accomplish this objective, we apply a vector-autoregressive spatio-temporal (VAST) model that simultaneously estimates spatio-temporal variation in density for multiple species, and present an application of this model using data for eight US Pacific Coast rockfishes (Sebastes spp.), thornyheads (Sebastolobus spp.), and structure-forming invertebrates (SFIs). We identified three fish groups having similar spatial distribution (northern Sebastes, coastwide Sebastes, and Sebastolobus species), and estimated differences among groups in their association with SFI. The multispecies model was more parsimonious and had better predictive performance than fitting a single-species model to each taxon individually, and estimated fine-scale variation in density even for species with relatively few encounters (which the single-species model was unable to do). However, the single-species models showed similar abundance trends and distribution shifts to those of the multispecies model, with slightly smaller standard errors. Therefore, we conclude that spatial variation in density (and annual variation in these patterns) is correlated among fishes and SFI, with congeneric fishes more correlated than species from different genera. However, explicitly modelling correlations among fishes and biogenic habitat does not seem to improve precision for estimates of abundance trends or distribution shifts for these fishes.

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Guillaumet, Alban, and Roger Prodon. "Avian succession along ecological gradients: Insight from species-poor and species-rich communities of Sylvia warblers." Current Zoology 57, no. 3 (June 1, 2011): 307–17. http://dx.doi.org/10.1093/czoolo/57.3.307.

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Abstract The mechanisms responsible for species replacement during ecological successions is a long-standing and open debate. In this study, we examined the distribution of the Sardinian warbler Sylvia melanocephala along two grassland-to-forest gradients, one in a high-diversity area (Albera-Aspres chain in Catalonia: eight Sylvia warbler species) and one in a low-diversity area (Mount Hymittos in Greece: four species). In Catalonia, distribution models suggested that the apparent exclusion of S. melanocephala from the open and forest ends of the gradient may be explained entirely by the preference of S. melanocephala for mid-successional shrublands. However, a joint analysis of both data sets revealed that: 1) S. melanocephala was more evenly distributed along the vegetation gradient in Greece, suggesting ecological release in the low-diversity area; and 2) a distribution model assuming interspecific competition (based on the distribution of Sylvia species showing a negative co-occurrence pattern with S. melanocephala) had a significantly higher predictive ability than a distribution model based on habitat variables alone. Our study supports the view that species turnover along ecological gradients generally results from a combination of intrinsic preferences and interspecific competition.

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Chen, Kai, Kevin S. Burgess, Fangliang He, Xiang-Yun Yang, Lian-Ming Gao, and De-Zhu Li. "Seed traits and phylogeny explain plants' geographic distribution." Biogeosciences 19, no. 19 (October 12, 2022): 4801–10. http://dx.doi.org/10.5194/bg-19-4801-2022.

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Abstract. Understanding the mechanisms that shape the geographic distribution of plant species is a central theme of biogeography. Although seed mass, seed dispersal mode and phylogeny have long been suspected to affect species distribution, the link between the sources of variation in these attributes and their effects on the distribution of seed plants are poorly documented. This study aims to quantify the joint effects of key seed traits and phylogeny on species distribution. We collected the seed mass and seed dispersal mode from 1426 species of seed plants representing 501 genera of 122 families and used 4 138 851 specimens to model species distributional range size. Phylogenetic generalized least-squares regression and variation partitioning were performed to estimate the effects of seed mass, seed dispersal mode and phylogeny on species distribution. We found that species distributional range size was significantly constrained by phylogeny. Seed mass and its intraspecific variation were also important in limiting species distribution, but their effects were different among species with different dispersal modes. Variation partitioning revealed that seed mass, seed mass variability, seed dispersal mode and phylogeny together explained 46.82 % of the variance in species range size. Although seed traits are not typically used to model the geographic distributions of seed plants, our study provides direct evidence showing seed mass, seed dispersal mode and phylogeny are important in explaining species geographic distribution. This finding underscores the necessity to include seed traits and the phylogenetic history of species in climate-based niche models for predicting the response of plant geographic distribution to climate change.

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James Omaiye, Ojonubah. "Numerical Analysis of Ordinary Differential Equations of Ecological Competing Species Across Diverse Environments." African Journal of Mathematics and Statistics Studies 6, no. 1 (March 15, 2023): 88–102. http://dx.doi.org/10.52589/ajmss_evssxtr7.

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In a geographical region, species have their range margins (i.e., the geographic boundaries where species can be found). Several species distribution models have shown that environmental factors (i.e., abiotic factors) and species interactions (i.e., biotic interactions) are responsible for shaping the distributions of species. Yet, most of the models often focus on one of these factors and ignore their joint effects. Consequently, predicting which species will exist and at what range margins is a challenge in ecology. Thus, in this paper, the combined influences of these ecological factors on multi-species community structures are studied. An ordinary differential equations (ODE) model is employed to study multi-species competition interactions across diverse environments. The model is numerically analysed for the range margins of the species and threshold values of competition strength which leads to the presence-absence of species. It is observed that the range margins are influenced by competition between species combined with environmental factors and the threshold values of competition strength correspond to transcritical bifurcation. Depending on the species’ competition strengths, the model exhibits coexistence and exclusion of species, mediated by weak and aggressive biotic interactions, respectively. It is observed that ecologically similar species competitively affect each other more than dissimilar species.

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Pandey, Bikram, Nirdesh Nepal, Salina Tripathi, Kaiwen Pan, Mohammed A. Dakhil, Arbindra Timilsina, Meta F. Justine, Saroj Koirala, and Kamal B. Nepali. "Distribution Pattern of Gymnosperms’ Richness in Nepal: Effect of Environmental Constrains along Elevational Gradients." Plants 9, no. 5 (May 14, 2020): 625. http://dx.doi.org/10.3390/plants9050625.

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Understanding the pattern of species distribution and the underlying mechanism is essential for conservation planning. Several climatic variables determine the species diversity, and the dependency of species on climate motivates ecologists and bio-geographers to explain the richness patterns along with elevation and environmental correlates. We used interpolated elevational distribution data to examine the relative importance of climatic variables in determining the species richness pattern of 26 species of gymnosperms in the longest elevation gradients in the world. Thirteen environmental variables were divided into three predictors set representing each hypothesis model (energy-water, physical-tolerance, and climatic-seasonality); to explain the species richness pattern of gymnosperms along the elevational gradient. We performed generalized linear models and variation partitioning to evaluate the relevant role of environmental variables on species richness patterns. Our findings showed that the gymnosperms’ richness formed a hump-shaped distribution pattern. The individual effect of energy-water predictor set was identified as the primary determinant of species richness. While, the joint effects of energy-water and physical-tolerance predictors have explained highest variations in gymnosperm distribution. The multiple environmental indicators are essential drivers of species distribution and have direct implications in understanding the effect of climate change on the species richness pattern.

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Shitikov, V. K., T. D. Zinchenko, and L. V. Golovatyuk. "Models of Joint Distribution of Species on the Example of Benthic Communities from Small Rivers of the Volga Basin." Biology Bulletin Reviews 12, no. 1 (January 2022): 84–93. http://dx.doi.org/10.1134/s2079086422010078.

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Adebiyi, Adeyemi A., Jasper F. Kok, Yang Wang, Akinori Ito, David A. Ridley, Pierre Nabat, and Chun Zhao. "Dust Constraints from joint Observational-Modelling-experiMental analysis (DustCOMM): comparison with measurements and model simulations." Atmospheric Chemistry and Physics 20, no. 2 (January 23, 2020): 829–63. http://dx.doi.org/10.5194/acp-20-829-2020.

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Abstract. Mineral dust is the most abundant aerosol species by mass in the atmosphere, and it impacts global climate, biogeochemistry, and human health. Understanding these varied impacts on the Earth system requires accurate knowledge of dust abundance, size, and optical properties, and how they vary in space and time. However, current global models show substantial biases against measurements of these dust properties. For instance, recent studies suggest that atmospheric dust is substantially coarser and more aspherical than accounted for in models, leading to persistent biases in modelled impacts of dust on the Earth system. Here, we facilitate more accurate constraints on dust impacts by developing a new dataset: Dust Constraints from joint Observational-Modelling-experiMental analysis (DustCOMM). This dataset combines an ensemble of global model simulations with observational and experimental constraints on dust size distribution and shape to obtain more accurate constraints on three-dimensional (3-D) atmospheric dust properties than is possible from global model simulations alone. Specifically, we present annual and seasonal climatologies of the 3-D dust size distribution, 3-D dust mass extinction efficiency at 550 nm, and two-dimensional (2-D) atmospheric dust loading. Comparisons with independent measurements taken over several locations, heights, and seasons show that DustCOMM estimates consistently outperform conventional global model simulations. In particular, DustCOMM achieves a substantial reduction in the bias relative to measured dust size distributions in the 0.5–20 µm diameter range. Furthermore, DustCOMM reproduces measurements of dust mass extinction efficiency to almost within the experimental uncertainties, whereas global models generally overestimate the mass extinction efficiency. DustCOMM thus provides more accurate constraints on 3-D dust properties, and as such can be used to improve global models or serve as an alternative to global model simulations in constraining dust impacts on the Earth system.

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Smith, James A., and Daniel D. Johnson. "Evaluating drivers and predictability of catch composition in a highly mixed trawl fishery using stacked and joint species distribution models." Fisheries Research 279 (November 2024): 107151. http://dx.doi.org/10.1016/j.fishres.2024.107151.

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Jensen, Alexander J., Ryan P. Kelly, William H. Satterthwaite, Eric J. Ward, Paul Moran, and Andrew Olaf Shelton. "Modeling ocean distributions and abundances of natural- and hatchery-origin Chinook salmon stocks with integrated genetic and tagging data." PeerJ 11 (November 28, 2023): e16487. http://dx.doi.org/10.7717/peerj.16487.

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Background Considerable resources are spent to track fish movement in marine environments, often with the intent of estimating behavior, distribution, and abundance. Resulting data from these monitoring efforts, including tagging studies and genetic sampling, often can be siloed. For Pacific salmon in the Northeast Pacific Ocean, predominant data sources for fish monitoring are coded wire tags (CWTs) and genetic stock identification (GSI). Despite their complementary strengths and weaknesses in coverage and information content, the two data streams rarely have been integrated to inform Pacific salmon biology and management. Joint, or integrated, models can combine and contextualize multiple data sources in a single statistical framework to produce more robust estimates of fish populations. Methods We introduce and fit a comprehensive joint model that integrates data from CWT recoveries and GSI sampling to inform the marine life history of Chinook salmon stocks at spatial and temporal scales relevant to ongoing fisheries management efforts. In a departure from similar models based primarily on CWT recoveries, modeled stocks in the new framework encompass both hatchery- and natural-origin fish. We specifically model the spatial distribution and marine abundance of four distinct stocks with spawning locations in California and southern Oregon, one of which is listed under the U.S. Endangered Species Act. Results Using the joint model, we generated the most comprehensive estimates of marine distribution to date for all modeled Chinook salmon stocks, including historically data poor and low abundance stocks. Estimated marine distributions from the joint model were broadly similar to estimates from a simpler, CWT-only model but did suggest some differences in distribution in select seasons. Model output also included novel stock-, year-, and season-specific estimates of marine abundance. We observed and partially addressed several challenges in model convergence with the use of supplemental data sources and model constraints; similar difficulties are not unexpected with integrated modeling. We identify several options for improved data collection that could address issues in convergence and increase confidence in model estimates of abundance. We expect these model advances and results provide management-relevant biological insights, with the potential to inform future mixed-stock fisheries management efforts, as well as a foundation for more expansive and comprehensive analyses to follow.

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Sánchez-Barradas, Alejandro, Wesley Dáttilo, Diego Santiago-Alarcon, W. Daniel Kissling, and Fabricio Villalobos. "Combining Geographic Distribution and Trait Information to Infer Predator–Prey Species-Level Interaction Properties." Diversity 15, no. 1 (January 4, 2023): 61. http://dx.doi.org/10.3390/d15010061.

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Biotic interactions are a key component of the proper functioning of ecosystems. However, information on biotic interactions is spatially and taxonomically biased and limited to several groups. The most efficient strategy to fill these gaps is to combine spatial information (species ranges) with different sources of information (functional and field data) to infer potential interactions. This approach is possible due to the fact that there is a correspondence between the traits of two trophic levels (e.g., predator and prey sizes are correlated). Therefore, our objective was to evaluate the performance of the joint use of spatial, functional and field data to infer properties of the predator–prey interaction for five neotropical cats. To do this, we used presence–absence matrices to obtain lists of potential prey species per grid-cell for each predator range. These lists were filtered according to different criteria (models), and for each model, an interaction property was estimated and compared with field observations. Our results show that the use of functional information and co-occurrence allows us to generate values similar to those observed in the field. We also observed that there were differences in model performance related to the intrinsic characteristics of the predator (body size) and the interaction property being evaluated.

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Hur, Chan, and Hyeyoung Park. "Zero-Shot Image Classification with Rectified Embedding Vectors Using a Caption Generator." Applied Sciences 13, no. 12 (June 13, 2023): 7071. http://dx.doi.org/10.3390/app13127071.

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Although image recognition technologies are developing rapidly with deep learning, conventional recognition models trained by supervised learning with class labels do not work well when test inputs from untrained classes are given. For example, a recognizer trained to classify Asian bird species cannot recognize the species of kiwi, because the class label “kiwi” and its image samples have not been seen during training. To overcome this limitation, zero-shot classification has been studied recently, and the joint-embedding-based approach has been suggested as one of the promised solutions. In this approach, image features and text descriptions belonging to the same class are trained to be closely located in a common joint-embedding space. Once we obtain the embedding function that can represent the semantic relationship of image–text pairs in training data, test images and text descriptions (prototypes) of unseen classes can also be mapped to the joint-embedding space for classification. The main challenge with this approach is mapping inputs of two different modalities into a common space, and previous works suffer from the inconsistency between the distribution of two feature sets on joint-embedding space extracted from the heterogeneous inputs. To treat this problem, we propose a novel method of employing additional textual information to rectify the visual representation of input images. Since the conceptual information of test classes is generally given as texts, we expect that the additional descriptions from a caption generator can adjust the visual feature for better matching with the representation of the test classes. We also propose to use the generated textual descriptions to augment training samples for learning joint-embedding space. In the experiments on two benchmark datasets, the proposed method shows significant performance improvements of 1.4% on the CUB dataset and 5.5% on the flower dataset, in comparison to existing models.

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Scharringhausen, M., A. C. Aikin, J. P. Burrows, and M. Sinnhuber. "First space-borne measurements of the altitude distribution of mesospheric magnesium species." Atmospheric Chemistry and Physics Discussions 7, no. 2 (April 2, 2007): 4597–656. http://dx.doi.org/10.5194/acpd-7-4597-2007.

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Abstract. We present a joint retrieval as well as first results for mesospheric air density and mesospheric Magnesium species (Mg and Mg+) using limb data from the SCIAMACHY instrument on board the European ENVISAT satellite. Metallic species like neutral Mg, ionized Mg+ and others (Fe, Si, Li, etc.) ablate from meteoric dust, enter the gas phase and occur at high altitudes (≥70 km). Emissions from these species are clearly observed in the SCIAMACHY limb measurements. These emissions are used to retrieve total and thermospheric column densities as well as altitude-resolved profiles of metallic species in the altitude range of 70–92 km. In this paper, neutral Magnesium as well as its ionized counterpart Mg+ is considered. These species feature resonance fluorescence in the wavelength range 279 and 285 nm and thus have a rather simple excitation process. A radiative transfer model (RTM) for the mesosphere has been developed and validated. Based on a ray tracing kernel, radiances in a large wavelength range from 240–300 nm covering limb as well as nadir geometry can be calculated. The forward model has been validated and shows good agreement with established models in the given wavelength range and a large altitude range. The RTM has been coupled to a retrieval based on Optimal Estimation. Air density is retrieved from Rayleigh backscatter light. Mesospheric Mg and Mg+ number densities are retrieved from their emission signals observed in the limb scans of SCIAMACHY. Other species like iron, silicon, OH and NO can be investigated in principle with the same algorithm. Based on the retrieval presented here, SCIAMACHY offers the opportunity to investigate mesospheric species on a global scale and with good vertical resolution for the first time.

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Ruiz-Diaz, Raquel, Maria Grazia Pennino, Jonathan A. D. Fisher, and Tyler D. Eddy. "Decadal changes in biomass and distribution of key fisheries species on Newfoundland’s Grand Banks." PLOS ONE 19, no. 4 (April 1, 2024): e0300311. http://dx.doi.org/10.1371/journal.pone.0300311.

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Canadian fisheries management has embraced the precautionary approach and the incorporation of ecosystem information into decision-making processes. Accurate estimation of fish stock biomass is crucial for ensuring sustainable exploitation of marine resources. Spatio-temporal models can provide improved indices of biomass as they capture spatial and temporal correlations in data and can account for environmental factors influencing biomass distributions. In this study, we developed a spatio-temporal generalized additive model (st-GAM) to investigate the relationships between bottom temperature, depth, and the biomass of three key fished species on The Grand Banks: snow crab (Chionoecetes opilio), yellowtail flounder (Limanda ferruginea), and Atlantic cod (Gadus morhua). Our findings revealed changes in the centre of gravity of Atlantic cod that could be related to a northern shift of the species within the Grand Banks or to a faster recovery of the 2J3KL stock. Atlantic cod also displayed hyperaggregation behaviour with the species showing a continuous distribution over the Grand Banks when biomass is high. These findings suggest a joint stock assessment between the 2J3KL and 3NO stocks would be advisable. However, barriers may need to be addressed to achieve collaboration between the two distinct regulatory bodies (i.e., DFO and NAFO) in charge of managing the stocks. Snow crab and yellowtail flounder centres of gravity have remained relatively constant over time. We also estimated novel indices of biomass, informed by environmental factors. Our study represents a step towards ecosystem-based fisheries management for the highly dynamic Grand Banks.

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Dobson, A. P. "The population dynamics of competition between parasites." Parasitology 91, no. 2 (October 1985): 317–47. http://dx.doi.org/10.1017/s0031182000057401.

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A number of published studies of competition between parasite species are examined and compared. It is suggested that two general levels of interaction are discernible: these correspond to the two levels of competition recognized by workers studying free-living animals and plants: ‘exploitation’ and ‘interference’ competition. The former may be defined as the joint utilization of a host species by two or more parasite species, while the latter occurs when antagonistic mechanisms are utilized by one species either to reduce the survival or fecundity of a second species or to displace it from a preferred site of attachment. Data illustrating both levels of interaction are collated from a survey of the published literature and these suggest that interference competition invariably operates asymmetrically. The data are also used to estimate a number of population parameters which are important in determining the impact of competition at the population level. Theoretical models of host-parasite associations for both classes of competition are used to examine the expected patterns of population dynamics that will be exhibited by simple two-species communities of parasites that utilize the same host population. The analysis suggests that the most important factor allowing competing species of parasites to coexist is the statistical distribution of the parasites within the host population. A joint stable equilibrium should be possible if both species are aggregated in their distribution. The size of the parasite burdens at equilibrium is then determined by other life-history parameters such as pathogenicity, rates of resource utilization and antagonistic ability. Comparison of these theoretical expectations with a variety of sets of empirical data forms the basis for a discussion about the importance of competition in natural parasite populations. The models are used to assess quantitatively the potential for using competing parasite species as biological control agents for pathogens of economic or medical importance. The most important criterion for identifying a successful control agent is an ability to infect a high proportion of the host population. If such a parasite species also exhibits an intermediate level of pathology or an efficient ability to utilize shared common resources, antagonistic interactions between the parasite species contribute only secondarily to the success of the control. Competition in parasites is compared with competition in free-living animals and plants. The comparison suggests further experimental tests which may help to assess the importance of competition in determining the structure of more complex parasite-host communities.

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Besacier Monbertrand, Anne-Laure, Pablo Timoner, Kazi Rahman, Paolo Burlando, Simone Fatichi, Yves Gonseth, Frédéric Moser, Emmanuel Castella, and Anthony Lehmann. "Assessing the Vulnerability of Aquatic Macroinvertebrates to Climate Warming in a Mountainous Watershed: Supplementing Presence-Only Data with Species Traits." Water 11, no. 4 (March 27, 2019): 636. http://dx.doi.org/10.3390/w11040636.

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Mountainous running water ecosystems are vulnerable to climate change with major changes coming from warming temperatures. Species distribution will be affected and some species are anticipated to be winners (increasing their range) or losers (at risk of extinction). Climate change vulnerability is seldom integrated when assessing threat status for lists of species at risk (Red Lists), even though this might appear an important addition in the current context. The main objective of our study was to assess the potential vulnerability of Ephemeroptera (E), Plecoptera (P) and Trichoptera (T) species to global warming in a Swiss mountainous region by supplementing Species Distribution Models (SDMs) with a trait-based approach, using available historical occurrence and environmental data and to compare our outcomes with the Swiss National Red List. First, we used nine different modelling techniques and topographic, land use, climatic and hydrological variables as predictors of EPT species distribution. The shape of the response curves of the species for the environmental variables in the nine modelling techniques, together with three biological and ecological traits were used to assess the potential vulnerability of each species to climate change. The joint use of SDMs and trait approach appeared complementary and even though discrepancies were highlighted between SDMs and trait analyses, groups of potential “winners” and “losers” were raised out. Plecoptera appeared as the most vulnerable group to global warming. Divergences between current threat status of species and our results pointed out the need to integrate climate change vulnerability in Red List assessments.

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Umair, Muhammad, Xiaofei Hu, Qi Cheng, Shahzad Ali, and Jian Ni. "Distribution Patterns of Gymnosperm Species along Elevations on the Qinghai–Tibet Plateau: Effects of Climatic Seasonality, Energy–Water, and Physical Tolerance Variables." Plants 12, no. 23 (December 4, 2023): 4066. http://dx.doi.org/10.3390/plants12234066.

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Climate change is one of the most prominent factors influencing the spatial distribution of plants in China, including gymnosperms. Climatic factors influence gymnosperm distribution along elevational gradients on the Qinghai–Xizang (Tibet) Plateau (QTP), and understanding how species adapt to these factors is important for identifying the impacts of global climate change. For the first time, we examined the county-level distribution of gymnosperm species on QTP using data from field surveys, published works, monographs, and internet sources. We used simulated distribution data of gymnosperms (N = 79) along the elevational gradients to investigate the overall impact of environmental variables in explaining the richness pattern of gymnosperms. Eighteen environmental variables were classified into three key variable sets (climatic seasonality, energy–water, and physical tolerance). We employed principal component analysis and generalized linear models to assess the impact of climatic variables on the gymnosperm’s richness pattern. Gymnosperm species are unevenly distributed across the plateau and decline gradually from the southeast to the northwest. The altitudinal gradients have a unimodal relationship with the richness of gymnosperms, with the maximum species richness at an elevation of 3200 m. The joint effects of physical tolerance and energy–water predictors have explained the highest diversity of gymnosperms at mid-elevation. Because the richness peak correlates significantly with the wettest month’s precipitation and moisture index, this confirms the significance of moisture on gymnosperm distributions due to increased precipitation during the wet season. Furthermore, our results provide evidence that climatic seasonality factors are involved in the decline of gymnosperm richness at high elevations. A total of 37% of gymnosperm species on QTP are listed as vulnerable, nearly threatened, or endangered, with elevations ranging from 600 m to 5300 m. As a result, we conclude that gymnosperms are at high risk of extinction because of the current climate fluctuations caused by global climate change. Our research offers fundamental data for the study and protection of gymnosperm species along the steepest elevation gradients.

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Villegas, P., A. Cavagna, M. Cencini, H. Fort, and T. S. Grigera. "Joint assessment of density correlations and fluctuations for analysing spatial tree patterns." Royal Society Open Science 8, no. 1 (January 20, 2021): 202200. http://dx.doi.org/10.1098/rsos.202200.

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Inferring the processes underlying the emergence of observed patterns is a key challenge in theoretical ecology. Much effort has been made in the past decades to collect extensive and detailed information about the spatial distribution of tropical rainforests, as demonstrated, e.g. in the 50 ha tropical forest plot on Barro Colorado Island, Panama. These kinds of plots have been crucial to shed light on diverse qualitative features, emerging both at the single-species or the community level, like the spatial aggregation or clustering at short scales. Here, we build on the progress made in the study of the density correlation functions applied to biological systems, focusing on the importance of accurately defining the borders of the set of trees, and removing the induced biases. We also pinpoint the importance of combining the study of correlations with the scale dependence of fluctuations in density, which are linked to the well-known empirical Taylor’s power law. Density correlations and fluctuations, in conjunction, provide a unique opportunity to interpret the behaviours and, possibly, to allow comparisons between data and models. We also study such quantities in models of spatial patterns and, in particular, we find that a spatially explicit neutral model generates patterns with many qualitative features in common with the empirical ones.

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Menge, Enock O., Alyson Stobo-Wilson, Sofia L. J. Oliveira, and Michael J. Lawes. "The potential distribution of the woody weed Calotropis procera (Aiton) W.T. Aiton (Asclepiadaceae) in Australia." Rangeland Journal 38, no. 1 (2016): 35. http://dx.doi.org/10.1071/rj15081.

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The potential spread of any invasive plant is a central concern in weed risk assessment. Calotropis procera is wind dispersed and forms extensive monospecific stands that reduce the productivity of pastoral land, but its potential distribution and drivers of its spread are not well known. Using maximum entropy methodology, we modelled current and future potential distributions of C. procera in Australia. Occurrence data (n = 5976 presence records) were collated from regional databases and a field survey. Of a set of ‘independent’ environmental correlates, those that best accounted for the observed distribution of C. procera in Australia were distance (km) to roads, average annual rainfall (mm), mean temperature (°C), average wind speed (km/h), beef density and vegetation type, in that order of importance. Current and potential distribution of C. procera was best explained by interactions between anthropogenic disturbance and climatic factors, all underpinned by species characteristics. Models were based on a grid cell size of 5 km × 5 km and model performance was good (mean AUC = 0.916; s.d. = 0.014; AUC = area under the curve; perfect fit = 1). The model showed that C. procera has not saturated its current potential distribution. Models of future spread derived from climate change projections, based on global circulation models in the ‘Representative Concentration Pathway 4.5 emissions scenario for 2035’, show the area suitable for C. procera will increase, increasing the risk the weed poses. Range expansion will occur into all three states surrounding the Northern Territory, but mostly into the north-eastern border regions of Western Australia and north-western Queensland. Joint management of rubber bush at a regional scale across jurisdictions, is urgently advised to avoid future spread of rubber bush and further reductions in pastoral productivity.

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Xu, Yi, Junjie Wang, Anquan Xia, Kangyong Zhang, Xuanyan Dong, Kaipeng Wu, and Guofeng Wu. "Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species." Remote Sensing 11, no. 3 (January 27, 2019): 254. http://dx.doi.org/10.3390/rs11030254.

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Due to continuous degradation of mangrove forests, the accurate monitoring of spatial distribution and species composition of mangroves is essential for restoration, conservation and management of coastal ecosystems. With leaf hyperspectral reflectance, this study aimed to explore the potential of continuous wavelet analysis (CWA) combined with different sample subset partition (stratified random sampling (STRAT), Kennard-Stone sampling algorithm (KS), and sample subset partition based on joint X-Y distances (SPXY)) and feature extraction methods (principal component analysis (PCA), successive projections algorithm (SPA), and vegetation index (VI)) in mangrove species classification. A total of 301 mangrove leaf samples with four species (Avicennia marina, Bruguiera gymnorrhiza, Kandelia obovate and Aegiceras corniculatum) were collected across six different regions. The smoothed reflectance (Smth) and first derivative reflectance (Der) spectra were subjected to CWA using different wavelet scales, and a total of 270 random forest classification models were established and compared. Among the 120 models with CWA of Smth, 88.3% of models increased the overall accuracy (OA) values with an improvement of 0.2–28.6% compared to the model with the Smth spectra; among the 120 models with CWA of Der, 25.8% of models increased the OA values with an improvement of 0.1–11.4% compared to the model with the Der spectra. The model with CWA of Der at the scale of 23 coupling with STRAT and SPA achieved the best classification result (OA = 98.0%), while the best model with Smth and Der alone had OA values of 86.3% and 93.0%, respectively. Moreover, the models using STRAT outperformed those using KS and SPXY, and the models using PCA and SPA had better performances than those using VIs. We have concluded that CWA with suitable scales holds great potential in improving the classification accuracy of mangrove species, and that STRAT combined with the PCA or SPA method is also recommended to improve classification performance. These results may lay the foundation for further studies with UAV-acquired or satellite hyperspectral data, and the encouraging performance of CWA for mangrove species classification can also be extended to other plant species.

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Van Ee, Justin J., Jacob S. Ivan, and Mevin B. Hooten. "Community confounding in joint species distribution models." Scientific Reports 12, no. 1 (July 18, 2022). http://dx.doi.org/10.1038/s41598-022-15694-6.

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AbstractJoint species distribution models have become ubiquitous for studying species-environment relationships and dependence among species. Accounting for community structure often improves predictive power, but can also affect inference on species-environment relationships. Specifically, some parameterizations of joint species distribution models allow interspecies dependence and environmental effects to explain the same sources of variability in species distributions, a phenomenon we call community confounding. We present a method for measuring community confounding and show how to orthogonalize the environmental and random species effects in suite of joint species distribution models. In a simulation study, we show that community confounding can lead to computational difficulties and that orthogonalizing the environmental and random species effects can alleviate these difficulties. We also discuss the inferential implications of community confounding and orthogonalizing the environmental and random species effects in a case study of mammalian responses to the Colorado bark beetle epidemic in the subalpine forest by comparing the outputs from occupancy models that treat species independently or account for interspecies dependence. We illustrate how joint species distribution models that restrict the random species effects to be orthogonal to the fixed effects can have computational benefits and still recover the inference provided by an unrestricted joint species distribution model.

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45

Hui, Francis K. C., Quan Vu, and Mevin B. Hooten. "Spatial confounding in joint species distribution models." Methods in Ecology and Evolution, September 6, 2024. http://dx.doi.org/10.1111/2041-210x.14420.

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Abstract Joint species distribution models (JSDMs) are a popular method for analysing multivariate abundance data, with important applications such as uncovering how species communities are driven by environmental processes, model‐based ordination to visualise community composition patterns across sites and variance partitioning to quantify the relative contributions of different processes in shaping a species community. One issue that has received relatively little attention in the study of joint species distributions is that of spatial confounding: when one or more of the environmental predictors exhibit spatial correlation, and spatially structured random effects such as spatial factors are also included in the model, then these two components may be collinear with each other. Through a combination of simulations and case studies, we show that if not managed properly, spatial confounding can result in misleading inference on covariate effects in a spatially structured JSDM, along with difficulties in interpreting ordination results and incorrect attribution of variation to environmental processes in a species community. We present one approach to treat spatial confounding called restricted spatial factor analysis, which is designed to ensure that the covariate effects retain their full explanatory power, and ordinations constructed using the spatial factors explain species covariation beyond that accounted for by the measured predictors. We encourage ecologists to consider the inferences they seek to make from spatially structured JSDMs and to ensure that the covariate effects and ordinations they estimate and interpret are aligned with their scientific questions of interest.

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Tobler, Mathias W., Marc Kéry, Francis K. C. Hui, Gurutzeta Guillera‐Arroita, Peter Knaus, and Thomas Sattler. "Joint species distribution models with species correlations and imperfect detection." Ecology 100, no. 8 (May 30, 2019). http://dx.doi.org/10.1002/ecy.2754.

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Bystrova, Daria, Giovanni Poggiato, Billur Bektaş, Julyan Arbel, James S. Clark, Alessandra Guglielmi, and Wilfried Thuiller. "Clustering Species With Residual Covariance Matrix in Joint Species Distribution Models." Frontiers in Ecology and Evolution 9 (March 9, 2021). http://dx.doi.org/10.3389/fevo.2021.601384.

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Modeling species distributions over space and time is one of the major research topics in both ecology and conservation biology. Joint Species Distribution models (JSDMs) have recently been introduced as a tool to better model community data, by inferring a residual covariance matrix between species, after accounting for species' response to the environment. However, these models are computationally demanding, even when latent factors, a common tool for dimension reduction, are used. To address this issue, Taylor-Rodriguez et al. (2017) proposed to use a Dirichlet process, a Bayesian nonparametric prior, to further reduce model dimension by clustering species in the residual covariance matrix. Here, we built on this approach to include a prior knowledge on the potential number of clusters, and instead used a Pitman–Yor process to address some critical limitations of the Dirichlet process. We therefore propose a framework that includes prior knowledge in the residual covariance matrix, providing a tool to analyze clusters of species that share the same residual associations with respect to other species. We applied our methodology to a case study of plant communities in a protected area of the French Alps (the Bauges Regional Park), and demonstrated that our extensions improve dimension reduction and reveal additional information from the residual covariance matrix, notably showing how the estimated clusters are compatible with plant traits, endorsing their importance in shaping communities.

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Kettunen, Juho, Lauri Mehtätalo, Eeva‐Stiina Tuittila, Aino Korrensalo, and Jarno Vanhatalo. "Joint species distribution modeling with competition for space." Environmetrics, October 18, 2023. http://dx.doi.org/10.1002/env.2830.

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AbstractJoint species distribution models (JSDM) are among the most important statistical tools in community ecology. However, existing JSDMs cannot model mutual exclusion between species. We tackle this deficiency in the context of modeling plant percentage cover data, where mutual exclusion arises from limited growing space and competition for light. We propose a hierarchical JSDM where latent Gaussian variable models describe species' niche preferences and Dirichlet‐Multinomial distribution models the observation process and competition between species. We also propose a decision theoretic model comparison and validation approach to assess the goodness of JSDMs in four different types of predictive tasks. We apply our models and methods to a case study on modeling vegetation cover in a boreal peatland. Our results show that ignoring the interspecific interactions and competition reduces models' predictive performance and leads to biased estimates for total percentage cover. Models' relative predictive performance also depends on the predictive task highlighting that model comparison and assessment should resemble the true predictive task. Our results also demonstrate that the proposed JSDM can be used to simultaneously infer interspecific correlations in niche preference as well as mutual competition for space and through that provide novel insight into ecological research.

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Wilkinson, David P., Nick Golding, Gurutzeta Guillera‐Arroita, Reid Tingley, and Michael A. McCarthy. "Defining and evaluating predictions of joint species distribution models." Methods in Ecology and Evolution, November 8, 2020. http://dx.doi.org/10.1111/2041-210x.13518.

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Roberts, Sarah M., Patrick N. Halpin, and James S. Clark. "Jointly modeling marine species to inform the effects of environmental change on an ecological community in the Northwest Atlantic." Scientific Reports 12, no. 1 (January 7, 2022). http://dx.doi.org/10.1038/s41598-021-04110-0.

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AbstractSingle species distribution models (SSDMs) are typically used to understand and predict the distribution and abundance of marine fish by fitting distribution models for each species independently to a combination of abiotic environmental variables. However, species abundances and distributions are influenced by abiotic environmental preferences as well as biotic dependencies such as interspecific competition and predation. When species interact, a joint species distribution model (JSDM) will allow for valid inference of environmental effects. We built a joint species distribution model of marine fish and invertebrates of the Northeast US Continental Shelf, providing evidence on species relationships with the environment as well as the likelihood of species to covary. Predictive performance is similar to SSDMs but the Bayesian joint modeling approach provides two main advantages over single species modeling: (1) the JSDM directly estimates the significance of environmental effects; and (2) predicted species richness accounts for species dependencies. An additional value of JSDMs is that the conditional prediction of species distributions can use not only the environmental associations of species, but also the presence and abundance of other species when forecasting future climatic associations.

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