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Reisinger, Ryan R., Ari S. Friedlaender, Alexandre N. Zerbini, Daniel M. Palacios, Virginia Andrews-Goff, Luciano Dalla Rosa, Mike Double, et al. "Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales." Remote Sensing 13, no. 11 (May 25, 2021): 2074. http://dx.doi.org/10.3390/rs13112074.
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Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection.
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Meißner, Karin, and Alexander Darr. "Distribution of Magelona species (Polychaeta: Magelonidae) in the German Bight (North Sea): a modeling approach." Zoosymposia 2, no. 1 (August 31, 2009): 567–86. http://dx.doi.org/10.11646/zoosymposia.2.1.39.
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The aim of the present study was the development of species-habitat models for four Magelona species (Polychaeta: Magelonidae) found in the German Bight in the SE North Sea. Analyses were based on field data and data obtained from reexamination of material deposited in museum collections. In addition, data on environmental variables were retrieved from the sediment map by Figge (1981) and from long-term monitoring data sets. The statistical modeling technique applied was multivariate adaptive regression splines (MARS). Predictive accuracy measures were calculated for each model. The candidate model with highest discriminatory power was selected for predictive mapping. Models with excellent predictive performance were developed for Magelona johnstoni, M. filiformis and M. alleni based on the analyzed set of environmental predictors. In each of the developed habitat models the most important predictor was a sediment parameter, either median grain size diameter (M. johnstoni) or mud content (M. alleni, M. filiformis). Salinity and water depth were also of importance. Model predictions were aimed to allow evaluation of habitat suitability for the investigated species in the German Bight. According to our results suitable habitats for M. johnstoni are numerous and a wide distribution of this species could be expected. Habitat suitability for M. filiformis in the German Bight was suggested to be high in areas with mud contents below 10 % at water depths between 25 and 35 m. The M. alleni habitat model indicated the presence of suitable habitats where sands with elevated mud contents are present and where water depths exceed 30 m.
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Enwright, Nicholas M., Lei Wang, Hongqing Wang, Michael J. Osland, Laura C. Feher, Sinéad M. Borchert, and Richard H. Day. "Modeling Barrier Island Habitats Using Landscape Position Information." Remote Sensing 11, no. 8 (April 24, 2019): 976. http://dx.doi.org/10.3390/rs11080976.
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Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.
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Rice, M. B., A. D. Apa, and L. A. Wiechman. "The importance of seasonal resource selection when managing a threatened species: targeting conservation actions within critical habitat designations for the Gunnison sage-grouse." Wildlife Research 44, no. 5 (2017): 407. http://dx.doi.org/10.1071/wr17027.
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Context The ability to identify priority habitat is critical for species of conservation concern. The designation of critical habitat under the US Endangered Species Act 1973 identifies areas occupied by the species that are important for conservation and may need special management or protection. However, relatively few species’ critical habitats designations incorporate habitat suitability models or seasonal specificity, even when that information exists. Gunnison sage-grouse (GUSG) have declined substantially from their historical range and were listed as threatened by the US Fish and Wildlife Service (USFWS) in November 2014. GUSG are distributed into eight isolated populations in Colorado and Utah, and one population, the Gunnison Basin (GB), has been the focus of much research. Aims To provide season-specific resource selection models to improve targeted conservation actions within the designated critical habitat in the GB. Methods We utilised radio-telemetry data from GUSG captured and monitored from 2004 to 2010. We were able to estimate resource selection models for the breeding (1 April–15 July) and summer (16 July–30 September) seasons in the GB using vegetation, topographical and anthropogenic variables. We compared the seasonal models with the existing critical habitat to investigate whether the more specific seasonal models helped identify priority habitat for GUSG. Key results The predictive surface for the breeding model indicated higher use of large areas of sagebrush, whereas the predictive surface for the summer model predicted use of more diverse habitats. The breeding and summer models (combined) matched the current critical habitat designation 68.5% of the time. We found that although the overall habitat was similar between the critical habitat designation and our combined models, the pattern and configuration of the habitat were very different. Conclusions These models highlight areas with favourable environmental variables and spatial juxtaposition to establish priority habitat within the critical habitat designated by USFWS. More seasonally specific resource selection models will assist in identifying specific areas within the critical habitat designation to concentrate habitat improvements, conservation and restoration within the GB. Implications This information can be used to provide insight into the patterns of seasonal habitat selection and can identify priority GUSG habitat to incorporate into critical habitat designation for targeted management actions.
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Haxton, Tim J., C. Scott Findlay, and R. W. Threader. "Predictive Value of a Lake Sturgeon Habitat Suitability Model." North American Journal of Fisheries Management 28, no. 5 (October 2008): 1373–83. http://dx.doi.org/10.1577/m07-146.1.
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Street, Garrett M., Lucas M. Vander Vennen, Tal Avgar, Anna Mosser, Morgan L. Anderson, Arthur R. Rodgers, and John M. Fryxell. "Habitat selection following recent disturbance: model transferability with implications for management and conservation of moose (Alces alces)." Canadian Journal of Zoology 93, no. 11 (November 2015): 813–21. http://dx.doi.org/10.1139/cjz-2015-0005.
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Site-specific variation in relative habitat abundance and disturbance regimes may produce differences in habitat preferences of associated populations. An evaluation of the predictive power of habitat selection models across space would benefit our understanding of the reliability of models of selection and space use in predicting animal occurrence. We used presence–absence data from winter surveys of moose (Alces alces (L., 1758)) to estimate resource selection functions (RSFs) across two study sites using Far North Land Cover updated with recent disturbance from fire and timber harvest. Moose selected foraging habitat (e.g., deciduous land cover) and for increasing deciduous foliage cover (ΔNDVI, i.e., the difference in the normalized difference vegetation index). Snow depth negatively influenced habitat selection, likely due to increased predation risk and reduced movement and foraging efficiency. Models lost little predictive power when applied to another site based on comparison of receiver operating characteristic (ROC) curves. Our results corroborated the current body of knowledge concerning moose habitat selection, i.e., moose preferentially use forest stands dominated by deciduous species, but suggested that moose strongly avoided very recently disturbed areas. Minimal site-specific variation and ROC comparison suggests that RSFs may be extended into novel systems, given adequate consideration for habitat quality and abundance, thereby simplifying management needs of this important species.
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TAKEMURA, Shion, Yoshihisa AKAMATSU, and Mahito KAMADA. "Evaluation of vulnerability of mangrove habitats using predictive habitat distribution model in Palau Islands." Journal of Japan Society of Civil Engineers, Ser. G (Environmental Research) 68, no. 5 (2012): I_105—I_110. http://dx.doi.org/10.2208/jscejer.68.i_105.
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Buechling, Arne, and Claudine Tobalske. "Predictive Habitat Modeling of Rare Plant Species in Pacific Northwest Forests." Western Journal of Applied Forestry 26, no. 2 (April 1, 2011): 71–81. http://dx.doi.org/10.1093/wjaf/26.2.71.
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Abstract Certification requirements associated with the Sustainable Forestry Initiative include efforts to identify and protect occurrences of endangered plant species. Habitat models were constructed in this study using maximum entropy and random forest algorithms to generate independent predictions for four selected rare plants, Castilleja chambersii, Erythronium elegans, Filipendula occidentalis, and Sidalcea nelsoniana, associated with divergent physical environments. Explanatory variables used to model rare plant occurrence included Landsat Enhanced Thematic Mapper Plus spectral imagery, spectral-based vegetation indices, climatic data, and several terrain variables derived from a digital elevation model. Models were trained with known occurrence records obtained from the Oregon Biodiversity Information Center. Subsequent field surveys were conducted to acquire randomly located test data for comparative model evaluation. A range of accuracy statistics was computed that indicated generally high prediction accuracy for both methods. Model performance was highest for species with narrow, well-defined ecological requirements at scales comparable to the resolution of the calibration data. Species with relatively broad ecological distributions or with extremely specific habitat requirements were less accurately predicted. Random forest-based models generally produced higher rates of prediction success than maximum entropy when model performance was limited by the ecology of a species. Field surveys identified 22 previously unknown populations of the target rare plants, suggesting the efficacy of habitat models for predicting rare species occurrence and their utility as a prescriptive tool for land management planning.
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Alabia, Irene D., Sei-Ichi Saitoh, Hiromichi Igarashi, Yoichi Ishikawa, Norihisa Usui, Masafumi Kamachi, Toshiyuki Awaji, and Masaki Seito. "Ensemble squid habitat model using three-dimensional ocean data." ICES Journal of Marine Science 73, no. 7 (May 6, 2016): 1863–74. http://dx.doi.org/10.1093/icesjms/fsw075.
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Abstract Neon flying squid (Ommastrephes bartramii) is a large pelagic squid internationally harvested in the North Pacific. Here, we examined its potential habitat in the central North Pacific using an ensemble modelling approach. Initially, ten statistical models were constructed by combining the squid fishing points, selected vertical layers of the sea temperature and salinity, sea surface height (SSH), and SSH gradient from the multi-variate ocean variational estimation system for the western North Pacific from June to July 1999–2011. The variable selection analyses have captured the importance of vertical temperature and salinity layers at the upper 300 and 440 m, respectively, coinciding with the reported vertical ranges of diel migration for the squid's primary prey species in the North Pacific. The evaluation of the habitat predictions using the independent sets of the presence data from 2012 to 2014 showed significant variability in the predictive accuracy, which is likely reflective of the interannual differences in environmental conditions across the validation periods. Our findings from ensemble habitat model approach using three-dimensional oceanographic data were able to characterize the near- and subsurface habitats of the neon flying squid. Moreover, our results underpinned the possible link between interannual environmental variability and spatio-temporal patterns of potential squid habitats. As such, these further suggest that an ensemble model approach could present a promising tool for operational fishery application and squid resource management.
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Socolar, Jacob B., and David S. Wilcove. "Forest-type specialization strongly predicts avian responses to tropical agriculture." Proceedings of the Royal Society B: Biological Sciences 286, no. 1913 (October 23, 2019): 20191724. http://dx.doi.org/10.1098/rspb.2019.1724.
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Species’ traits influence how populations respond to land-use change. However, even in well-characterized groups such as birds, widely studied traits explain only a modest proportion of the variance in response across species. Here, we show that associations with particular forest types strongly predict the sensitivity of forest-dwelling Amazonian birds to agriculture. Incorporating these fine-scale habitat associations into models of population response dramatically improves predictive performance and markedly outperforms the functional traits that commonly appear in similar analyses. Moreover, by identifying habitat features that support assemblages of unusually sensitive habitat-specialist species, our model furnishes straightforward conservation recommendations. In Amazonia, species that specialize on forests along a soil–nutrient gradient (i.e. both rich-soil specialists and poor-soil specialists) are exceptionally sensitive to agriculture, whereas species that specialize on floodplain forests are unusually insensitive. Thus, habitat specialization per se does not predict disturbance sensitivity, but particular habitat associations do. A focus on conserving specific habitats that harbour highly sensitive avifaunas (e.g. poor-soil forest) would protect a critically threatened component of regional biodiversity. We present a conceptual model to explain the divergent responses of habitat specialists in the different habitats, and we suggest that similar patterns and conservation opportunities probably exist for other taxa and regions.
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Holmes, Stephen B., Lisa A. Venier, Brian J. Naylor, and J. Ryan Zimmerling. "A test of Ontario's Habitat Suitability Matrix as a forest management planning tool for forest birds." Forestry Chronicle 83, no. 4 (August 1, 2007): 570–79. http://dx.doi.org/10.5558/tfc83570-4.
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We used point-count data collected as part of Bird Studies Canada's Boreal Forest Bird Program to validate habitat suitability models for 22 forest bird species in Ontario's Habitat Suitability Matrix. We found that many of the species'models performed relatively poorly in discriminating between occupied and unoccupied sites, primarily due to the high error of commission rates (false positive predictions). Since species presence and abundance were assessed by single, five minute point counts, insufficient sampling was at least partly responsible for some of the observed over-prediction. Results suggested that model parameters for at least nine of the species tested (hairy woodpecker [Picoides villosus], blueheaded vireo [Vireo solitarius], red-eyed vireo [Vireo olivaceus], red-breasted nuthatch [Sitta canadensis], Swainson's thrush [Catharus ustulatus], hermit thrush [Catharus guttatus], Tennessee warbler [Vermivora peregrina], Blackburnian warbler [Dendroica fusca] and dark-eyed junco [Junco hyemalis]) should be reviewed to improve the predictive capability of the models and to ensure appropriate consideration of the habitat needs of these species during forest management planning. Key words: boreal forest, forest birds, discrimination capacity, habitat models, habitat suitability matrix, model accuracy, model validation, relative operating characteristic curve, ROC
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Dadashi-Jourdehi, Amirhossein, Bahman Shams Esfandabad, Abbas Ahmadi, Hamid Reza Rezaei, and Hamid Toranj-Zar. "Predicting the potential distribution of striped hyena Hyaena hyaena in Iran." Zoology and Ecology 29, no. 2 (July 30, 2019): 109–14. http://dx.doi.org/10.35513/21658005.2019.2.6.
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Predictive potential distribution modelling is crucial in outlining habitat usage and establishing conservation management priorities. Species distribution models estimate the relationship between species occurrences and environmental and spatial characteristics. Herein, we used maximum entropy distribution modelling (MaxEnt) for predicting the potential distribution of the striped hyena Hyaena hyaena in the entire country of Iran, using a number of occurrence records (i.e. 118) and environmental variables derived from remote sensing. The MaxEnt model had a high success rate according to test AUC scores (0.97). Our results are congruent with previous studies, suggesting that mountainous regions in northern and western Iran and the plain regions in central and eastern Iran are suitable habitats for H. hyaena.
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Bayne, Erin M., Samuel Haché, and Keith A. Hobson. "Comparing the predictive capability of forest songbird habitat models based on remotely sensed versus ground-based vegetation information." Canadian Journal of Forest Research 40, no. 1 (January 2010): 65–71. http://dx.doi.org/10.1139/x09-170.
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Habitat suitability models allow for predictive modeling of the supply of wildlife habitat through time under various forest harvesting scenarios. These models often rely on remotely sensed data in a forest resource inventory (FRI). However, the level of detail and (or) the accuracy of a FRI may be insufficient to accurately predict habitat suitability for forest birds. We tested if detailed vegetation measurements created habitat suitability models with better predictive power than models that used FRI data alone and if rough estimates of shrub cover were sufficient to supplement FRI data to create models with similar predictive power. For 28 species of forest birds, we found that less of the variation in abundance and (or) occurrence (% deviance explained) could be explained by models using FRI data alone (34% ± 2%) than by models using detailed vegetation information (40% ± 2%). However, when shrub density rank was included with FRI data, we found no difference in the deviance explained by the two model sets (39% ± 2% vs. 40% ± 2%). The best-fitting models containing the same vegetation parameters but using different methods of vegetation sampling were similar. These results suggest that coarse habitat classification schemes may be as effective in describing the major variance in bird community structure in the boreal forest as detailed vegetation inventory data.
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Lindenmayer, D. B., and R. C. Lacy. "Small mammals, habitat patches and PVA models: a field test of model predictive ability." Biological Conservation 103, no. 3 (March 2002): 247–65. http://dx.doi.org/10.1016/s0006-3207(01)00134-3.
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Pedersen, Å. Ø., J. U. Jepsen, N. G. Yoccoz, and E. Fuglei. "Ecological correlates of the distribution of territorial Svalbard rock ptarmigan (Lagopus muta hyperborea)." Canadian Journal of Zoology 85, no. 1 (January 2007): 122–32. http://dx.doi.org/10.1139/z06-197.
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Predictive habitat models have become important research and management tools for monitoring the spatial distribution and abundance of wildlife species. In this paper we develop and evaluate statistical habitat models for presence of territorial Svalbard rock ptarmigan ( Lagopus muta hyperborea Sundevall, 1845) cocks in spring and apply the best model to assess ptarmigan habitat selection in a larger extrapolated region. Terrain variables were extracted at detailed (10 m digital elevation model (DEM)) and coarse (50 m DEM) scales to compare model performance. Sets of candidate environmental variables related to terrain and vegetation cover were developed and explanatory variables were calculated at increasing distances from the count site to well above the typical size of ptarmigan territory. We used ecological niche factor analysis to describe the difference between used and available sites. Survey sites used by cocks were characterized by a restricted range of altitude, a high degree of terrain heterogeneity, and dense vegetation cover compared with overall site availability in the survey region. We then used model selection criteria (AICc) to find the most parsimonious logistic regression models estimating habitat resource selection functions for cocks. Detailed terrain variables were better predictors than coarse terrain variables. The normalized difference vegetation index (NDVI) was a good predictor of presence of territorial cocks, but not as good as the most preferred habitat type. Owing to limited availability of high-quality vegetation maps, the best model containing NDVI and 10 m DEM variables was used for extrapolation of male ptarmigan habitat. Our results show that it is possible to obtain a model with a high ability to rank habitats using a low number of map-derived variables. Such rankings can then be used to improve field sampling designs and are therefore a useful tool for management and conservation of ptarmigan and wildlife in Arctic and alpine areas.
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Evans, Andrew, Richard Odom, Lynn Resler, W. Mark Ford, and Steve Prisley. "Developing a Topographic Model to Predict the Northern Hardwood Forest Type within Carolina Northern Flying Squirrel (Glaucomys sabrinus coloratus) Recovery Areas of the Southern Appalachians." International Journal of Forestry Research 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/179415.
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The northern hardwood forest type is an important habitat component for the endangered Carolina northern flying squirrel (CNFS;Glaucomys sabrinus coloratus) for den sites and corridor habitats between boreo-montane conifer patches foraging areas. Our study related terrain data to presence of northern hardwood forest type in the recovery areas of CNFS in the southern Appalachian Mountains of western North Carolina, eastern Tennessee, and southwestern Virginia. We recorded overstory species composition and terrain variables at 338 points, to construct a robust, spatially predictive model. Terrain variables analyzed included elevation, aspect, slope gradient, site curvature, and topographic exposure. We used an information-theoretic approach to assess seven models based on associations noted in existing literature as well as an inclusive global model. Our results indicate that, on a regional scale, elevation, aspect, and topographic exposure index (TEI) are significant predictors of the presence of the northern hardwood forest type in the southern Appalachians. Our elevation + TEI model was the best approximating model (the lowest AICc score) for predicting northern hardwood forest type correctly classifying approximately 78% of our sample points. We then used these data to create region-wide predictive maps of the distribution of the northern hardwood forest type within CNFS recovery areas.
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Vanderhoof, Melanie, Barbara A. Holzman, and Chris Rogers. "Predicting the Distribution of Perennial Pepperweed (Lepidium latifolium), San Francisco Bay Area, California." Invasive Plant Science and Management 2, no. 3 (July 2009): 260–69. http://dx.doi.org/10.1614/ipsm-09-005.1.
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AbstractPerennial pepperweed is an invasive plant species that occurs throughout the western United States. This study develops a predictive model for perennial pepperweed distribution for the San Francisco Bay Area, based on spatial variables. Distribution data were developed by mapping perennial pepperweed along the shoreline of the South San Francisco Bay, using geographic positioning system units. Spatial relationships between its distribution and spatial variables were tested using binomial logistic regression. Predictive models were mapped using geographic information systems (GIS), and high risk areas within the San Francisco Bay Area were identified. Perennial pepperweed was found to occur within marsh habitats with full tidal action and near open water. This study demonstrates that habitat variables from widely available GIS layers can be used to predict distribution patterns for perennial pepperweed. The model results were compared to land ownership within the study area to demonstrate a management application of the model.
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Hamer, Thomas E., Daniel E. Varland, Trent L. McDonald, and Douglas Meekins. "Predictive Model of Habitat Suitability for the Marbled Murrelet in Western Washington." Journal of Wildlife Management 72, no. 4 (May 2008): 983–93. http://dx.doi.org/10.2193/2006-565.
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Rooper, CN, GR Hoff, DE Stevenson, JW Orr, and IB Spies. "Skate egg nursery habitat in the eastern Bering Sea: a predictive model." Marine Ecology Progress Series 609 (January 17, 2019): 163–78. http://dx.doi.org/10.3354/meps12809.
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Norris, D. Ryan, and Caz M. Taylor. "Predicting the consequences of carry-over effects for migratory populations." Biology Letters 2, no. 1 (November 2, 2005): 148–51. http://dx.doi.org/10.1098/rsbl.2005.0397.
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Migratory animals present a unique challenge for predicting population size because they are influenced by events in multiple stages of the annual cycle that are separated by large geographic distances. Here, we develop a model that incorporates non-fatal carry-over effects to predict changes in population size and show how this can be integrated with predictive models of habitat loss and deterioration. Examples from Barn swallows, Greater snow geese and American redstarts show how carry-over effects can be estimated and integrated into the model. Incorporation of carry-over effects should increase the predictive power of models. However, the challenge for developing accurate predictions rests both on the ability to estimate parameters from multiple stages of the annual cycle and to understand how events between these periods interact to influence individual success.
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Germaine, Steve, Drew Ignizio, Doug Keinath, and Holly Copeland. "Predicting Occupancy for Pygmy Rabbits in Wyoming: An Independent Evaluation of Two Species Distribution Models." Journal of Fish and Wildlife Management 5, no. 2 (August 1, 2014): 298–314. http://dx.doi.org/10.3996/022014-jfwm-016.
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Abstract Species distribution models are an important component of natural-resource conservation planning efforts. Independent, external evaluation of their accuracy is important before they are used in management contexts. We evaluated the classification accuracy of two species distribution models designed to predict the distribution of pygmy rabbit Brachylagus idahoensis habitat in southwestern Wyoming, USA. The Nature Conservancy model was deductive and based on published information and expert opinion, whereas the Wyoming Natural Diversity Database model was statistically derived using historical observation data. We randomly selected 187 evaluation survey points throughout southwestern Wyoming in areas predicted to be habitat and areas predicted to be nonhabitat for each model. The Nature Conservancy model correctly classified 39 of 77 (50.6%) unoccupied evaluation plots and 65 of 88 (73.9%) occupied plots for an overall classification success of 63.3%. The Wyoming Natural Diversity Database model correctly classified 53 of 95 (55.8%) unoccupied plots and 59 of 88 (67.0%) occupied plots for an overall classification success of 61.2%. Based on 95% asymptotic confidence intervals, classification success of the two models did not differ. The models jointly classified 10.8% of the area as habitat and 47.4% of the area as nonhabitat, but were discordant in classifying the remaining 41.9% of the area. To evaluate how anthropogenic development affected model predictive success, we surveyed 120 additional plots among three density levels of gas-field road networks. Classification success declined sharply for both models as road-density level increased beyond 5 km of roads per km-squared area. Both models were more effective at predicting habitat than nonhabitat in relatively undeveloped areas, and neither was effective at accounting for the effects of gas-energy-development road networks. Resource managers who wish to know the amount of pygmy rabbit habitat present in an area or wanting to direct gas-drilling efforts away from pygmy rabbit habitat may want to consider both models in an ensemble manner, where more confidence is placed in mapped areas (i.e., pixels) for which both models agree than for areas where there is model disagreement.
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Shiroyama, Risa, Manna Wang, and Chihiro Yoshimura. "Effect of sample size on habitat suitability estimation using random forests: a case of bluegill, Lepomis macrochirus." Annales de Limnologie - International Journal of Limnology 56 (2020): 13. http://dx.doi.org/10.1051/limn/2020010.
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Species distribution models (SDMs) have been used to understand the habitat suitability of key species. Habitat suitability plots, one outcome from SDMs, are valuable for understanding the habitat suitability and behavior of organisms. The sample size is often constrained by budget and time, and could largely influence the reliability of habitat suitability plots. To understand the effect of sample size on habitat suitability plots, the present study utilized random forests (RF) combined with partial dependence function. And the bluegill (Lepomis macrochirus), a main exotic fish species in the Japan rivers, was selected as target species in this study. Total of 1010 samples of bluegill observations along with four environmental variables were surveyed by the National Censuses on River Environments. The area under curves was calculated after generating RF models, to assess the predictive model performance, and this process was repeated 1000 times. To draw habitat suitability plots, we applied partial dependence function to the formulated RF models, and 15 different sample sizes were set to examine the effect on habitat suitability plots. We concluded that habitat suitability plots are affected by sample size and prediction performance. Notably, habitat suitability plots drawn from the sample size of 50 greatly varied among the 1000-time iterations, and they are all different from the observations. Furthermore, to deal with the case of limited samples, we proposed a novel approach “averaged habitat suitability plot” for delineating habitat suitability plots. The proposed approach enables us to assess the habitat suitability even with a small sample size.
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Sacks, Benjamin N., Mark J. Statham, and Heiko U. Wittmer. "A Preliminary Range-Wide Distribution Model for the Sacramento Valley Red Fox." Journal of Fish and Wildlife Management 8, no. 1 (December 1, 2016): 28–38. http://dx.doi.org/10.3996/072016-jfwm-057.
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Abstract The Sacramento Valley red fox Vulpes vulpes patwin of California is a newly named subspecies recently found to be distinct both from other native red foxes and nearby introduced populations. The Sacramento Valley red fox experienced a historical demographic bottleneck resulting in a critically small genetic effective population size, causing concern over its current status and management requirements, yet little is known about its contemporary abundance, demographic trajectory, or habitat use. The hot, arid Sacramento Valley contrasts starkly in climate and physiography with the boreal habitats of other indigenous red foxes in western North America, indicating the need to obtain information specifically on the habitat requirements of this subspecies. A 3-y effort to locate reproductive den sites throughout the Sacramento Valley resulted in 42 independent dens, which we used to obtain preliminary information on habitat use and to develop a distribution model for this subspecies, and 28 Sacramento Valley red foxes killed by vehicles, which we used as independent data to test the models. Foxes were present significantly more than expected in grasslands and less than expected in wetlands and flooded agriculture and also tended to occur in proximity to human development, potentially as refuges from coyotes Canis latrans. We used Maxent to build predictive models. The best model, which incorporated vegetation/land-use classes and proximity to human development, identified 24% of the study area as predicted-presence habitat, which contained 76% of the den sites used to construct the model and 89% of independent locations used to test the model. Our model greatly narrowed the area over which foxes are predicted to occur and will facilitate future surveys to assess occupancy and ultimately abundance and population trends.
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Hobday, Alistair J., Jason R. Hartog, Claire M. Spillman, and Oscar Alves. "Seasonal forecasting of tuna habitat for dynamic spatial management." Canadian Journal of Fisheries and Aquatic Sciences 68, no. 5 (May 2011): 898–911. http://dx.doi.org/10.1139/f2011-031.
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Capture of the target, bycatch, and protected species in fisheries is often regulated through spatial measures that partition fishing effort, including areal closures. In eastern Australian waters, southern bluefin tuna (SBT, Thunnus maccoyii ) are a quota-limited species in a multispecies longline fishery; minimizing capture by nonquota holders is an important management concern. A habitat preference model (conditioned with electronic tag data) coupled with ocean reanalysis data has been used since 2003 to generate real-time predicted maps of SBT distribution (nowcasts). These maps are used by fishery managers to restrict fisher access to areas with high predicted SBT distribution. Here we use the coupled ocean–atmosphere model, POAMA (predictive ocean atmosphere model for Australia), and a habitat model to forecast SBT distribution at lead times of up to 4 months. These forecasts are comparable with nowcasts derived from the operational system, and show skill in predicting SBT habitat boundaries out to lead-times of 3–4 months. For this fishery, seasonal forecasts can provide managers and fishers with valuable insights into future habitat distributions for the upcoming months, to better inform operational decisions.
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Rizo-Aguilar, Areli, José Antonio Guerrero, Mircea G. Hidalgo-Mihart, and Alberto González-Romero. "Relationship between the abundance of the Endangered volcano rabbit Romerolagus diazi and vegetation structure in the Sierra Chichinautzin mountain range, Mexico." Oryx 49, no. 2 (May 12, 2014): 360–65. http://dx.doi.org/10.1017/s0030605313000975.
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AbstractThe volcano rabbit Romerolagus diazi is endemic to the mountains of central Mexico, where its habitat has been gradually destroyed to make way for agriculture, ranching and logging, and by forest fires. The volcano rabbit is categorized as Endangered on the IUCN Red List. We evaluated the relationship between the abundance of the volcano rabbit and vegetation structure at a small scale (0.25 ha). Using a general linear model we generated a set of 21 predictive models and proposed the best model as a habitat quality index. Our results suggest that greater height and cover of bunchgrasses and the presence of a shrub layer offer the volcano rabbit the best refuge from predators. The habitat quality index and the limited available habitat documented in our survey indicate that the populations of volcano rabbits in the study area are more threatened than previously thought. As R. diazi is a habitat specialist it does not have the option of moving to another habitat type.
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Gardner, Beth, Patrick J. Sullivan, and Arthur J. Lembo, Jr. "Predicting stream temperatures: geostatistical model comparison using alternative distance metrics." Canadian Journal of Fisheries and Aquatic Sciences 60, no. 3 (March 1, 2003): 344–51. http://dx.doi.org/10.1139/f03-025.
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The Beaverkill Watershed in the Catskill Mountains of New York, U.S.A., shows evidence of high summer stream temperatures throughout critical trout habitat. Because habitat quality, as characterized by stream temperature, dramatically influences trout communities, it is important for biologists to identify and map these characteristics and to monitor how they change over time. Stream temperatures were recorded over time throughout the Beaverkill Watershed and were used to identify thermal refugia and areas of thermal stress. Seventy-two temperature loggers were placed throughout the watershed during the summer of 2000. Three geostatistical metrics for predicting temperature across the watershed were constructed and evaluated. The first metric utilized the shortest path between temperature loggers without using stream network information. A second metric used distances calculated along the stream network in both upstream and downstream directions. Our final metric was a modified network system in which the distances were weighted by stream order. Each metric was found to provide predictive capability, with added complexity improving the accuracy of represented stream temperatures.
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Rinaldi, A., V. Montalto, A. Manganaro, A. Mazzola, S. Mirto, M. Sanfilippo, and G. Sarà. "Predictive mechanistic bioenergetics to model habitat suitability of shellfish culture in coastal lakes." Estuarine, Coastal and Shelf Science 144 (May 2014): 89–98. http://dx.doi.org/10.1016/j.ecss.2014.04.013.
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Bashari, Hossein, and Mahmoud-Reza Hemami. "A predictive diagnostic model for wild sheep (Ovis orientalis) habitat suitability in Iran." Journal for Nature Conservation 21, no. 5 (October 2013): 319–25. http://dx.doi.org/10.1016/j.jnc.2013.03.005.
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Stansfield, W. F., J. P. McTague, and R. Lacapa. "Dominant-height and site-index equations for ponderosa pine in east-central Arizona." Canadian Journal of Forest Research 21, no. 5 (May 1, 1991): 606–11. http://dx.doi.org/10.1139/x91-083.
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A dominant-height equation for ponderosa pine (Pinusponderosa Laws.) was constructed utilizing a parameter prediction method and a model by J.E. King. The site-index equation was obtained by inverting the dominant-height equation. A method was examined for indirectly obtaining parameter prediction equations. It proved superior to two direct parameter prediction approaches. Other site-quality variables, such as habitat type groups and elevation, were included in the dominant-height and site-index equations and were successful in refining predictive capability.
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Beale, Colin M., and Jack J. Lennon. "Incorporating uncertainty in predictive species distribution modelling." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1586 (January 19, 2012): 247–58. http://dx.doi.org/10.1098/rstb.2011.0178.
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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.
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Hastie, Gordon D., René J. Swift, George Slesser, Paul M. Thompson, and William R. Turrell. "Environmental models for predicting oceanic dolphin habitat in the Northeast Atlantic." ICES Journal of Marine Science 62, no. 4 (January 1, 2005): 760–70. http://dx.doi.org/10.1016/j.icesjms.2005.02.004.
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Abstract Dolphin distributions have been related to a range of oceanographic determinants. The complex topography and hydrography of the Faroe-Shetland Channel have a significant influence on the distribution of many species. However, there is no published detail on how dolphin distributions there are influenced by either topography or hydrography. The study therefore aims to relate dolphin distributions in the Faroe-Shetland Channel to environmental variables, using a general additive modelling framework applied to passive acoustic survey data. Models were created using data from 2001, and were cross-validated to test their predictive power. Predictions were calculated at each stage in the model-building process, and were tested against data from 2002. The results suggest that water noise level, time of day, month, water depth, and surface temperature were significant influences on the probability of detecting dolphins acoustically during 2001. Furthermore, the model was a significant predictor of dolphin distribution in 2002. The model with the greatest predictive power included the terms water noise level, time of day, month, and water depth. The results provide information of potential use in understanding the determinants of dolphin distributions, and hopefully will help managers address concerns about the potential impacts on dolphins of anthropogenic activity.
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Almarinez, Billy Joel M., Mary Jane A. Fadri, Richard Lasina, Mary Angelique A. Tavera, Thaddeus M. Carvajal, Kozo Watanabe, Jesusa C. Legaspi, and Divina M. Amalin. "A Bioclimate-Based Maximum Entropy Model for Comperiella calauanica Barrion, Almarinez and Amalin (Hymenoptera: Encyrtidae) in the Philippines." Insects 12, no. 1 (January 4, 2021): 26. http://dx.doi.org/10.3390/insects12010026.
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Comperiella calauanica is a host-specific endoparasitoid and effective biological control agent of the diaspidid Aspidiotus rigidus, whose outbreak from 2010 to 2015 severely threatened the coconut industry in the Philippines. Using the maximum entropy (Maxent) algorithm, we developed a species distribution model (SDM) for C. calauanica based on 19 bioclimatic variables, using occurrence data obtained mostly from field surveys conducted in A. rigidus-infested areas in Luzon Island from 2014 to 2016. The calculated the area under the ROC curve (AUC) values for the model were very high (0.966, standard deviation = 0.005), indicating the model’s high predictive power. Precipitation seasonality was found to have the highest relative contribution to model development. Response curves produced by Maxent suggested the positive influence of mean temperature of the driest quarter, and negative influence of precipitation of the driest and coldest quarters on habitat suitability. Given that C. calauanica has been found to always occur with A. rigidus in Luzon Island due to high host-specificity, the SDM for the parasitoid may also be considered and used as a predictive model for its host. This was confirmed through field surveys conducted between late 2016 and early 2018, which found and confirmed the occurrence of A. rigidus in three areas predicted by the SDM to have moderate to high habitat suitability or probability of occurrence of C. calauanica: Zamboanga City in Mindanao; Isabela City in Basilan Island; and Tablas Island in Romblon. This validation in the field demonstrated the utility of the bioclimate-based SDM for C. calauanica in predicting habitat suitability or probability of occurrence of A. rigidus in the Philippines.
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Cameron, Robert, Ian Goudie, and David Richardson. "Habitat loss exceeds habitat regeneration for an IUCN flagship lichen epiphyte: Erioderma pedicellatum." Canadian Journal of Forest Research 43, no. 11 (November 2013): 1075–80. http://dx.doi.org/10.1139/cjfr-2013-0024.
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The boreal felt lichen (Erioderma pedicellatum (Hue) P.M. Jørg.) is globally critically endangered, being threatened by forestry operations, habitat disturbance, and air pollution. To determine if loss of habitat due to forestry activities has occurred in Nova Scotia, a predictive habitat model was built using historical data from 1988. Satellite data were used for the period between 1987 and 2005 to determine the amount of suitable habitat harvested during this period. Available habitat was modeled through time from 1988 to 2005 in which area harvested was subtracted and regeneration was added in 3- to 5-year time steps. The predicted suitable boreal felt lichen habitat area was then modeled from 2005 to 2055 using the same harvesting assumptions and modeling process, but using 10-year time steps. The results of the model indicated that there has been a loss of 2311 ha (11.5%) in the amount of predicted suitable boreal felt lichen habitat between 1988 and 2005. A forward-projected drop is predicted between 2005 and 2055 that will amount to 4499 ha (25.4%), assuming no change in forest harvesting. Protection of unoccupied habitat surrounding existing boreal felt is recommended.
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Sun, Yue, Yanze Yu, Jinhao Guo, and Minghai Zhang. "The Winter Habitat Selection of Red Deer (Cervus elaphus) Based on a Multi-Scale Model." Animals 10, no. 12 (December 21, 2020): 2454. http://dx.doi.org/10.3390/ani10122454.
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Single-scale frameworks are often used to analyze the habitat selections of species. Research on habitat selection can be significantly improved using multi-scale models that enable greater in-depth analyses of the scale dependence between species and specific environmental factors. In this study, the winter habitat selection of red deer in the Gogostaihanwula Nature Reserve, Inner Mongolia, was studied using a multi-scale model. Each selected covariate was included in multi-scale models at their “characteristic scale”, and we used an all subsets approach and model selection framework to assess habitat selection. The results showed that: (1) Univariate logistic regression analysis showed that the response scale of red deer to environmental factors was different among different covariate. The optimal scale of the single covariate was 800–3200 m, slope (SLP), altitude (ELE), and ratio of deciduous broad-leaved forests were 800 m in large scale, except that the farmland ratio was 200 m in fine scale. The optimal scale of road density and grassland ratio is both 1600 m, and the optimal scale of net forest production capacity is 3200 m; (2) distance to forest edges, distance to cement roads, distance to villages, altitude, distance to all road, and slope of the region were the most important factors affecting winter habitat selection. The outcomes of this study indicate that future studies on the effectiveness of habitat selections will benefit from multi-scale models. In addition to increasing interpretive and predictive capabilities, multi-scale habitat selection models enhance our understanding of how species respond to their environments and contribute to the formulation of effective conservation and management strategies for ungulata.
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Anlauf-Dunn, Kara J., Eric J. Ward, Matt Strickland, and Kim Jones. "Habitat connectivity, complexity, and quality: predicting adult coho salmon occupancy and abundance." Canadian Journal of Fisheries and Aquatic Sciences 71, no. 12 (December 2014): 1864–76. http://dx.doi.org/10.1139/cjfas-2014-0162.
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The distribution, quality, and connectivity of instream habitat can influence adult salmon occupancy and abundance patterns and alter population dynamics. In this study, we evaluated the relationships between adult coho salmon (Oncorhynchus kisutch) occupancy and abundance with instream habitat conditions, including measures of spawning gravel, habitat complexity, and juvenile rearing habitat. We used corresponding adult salmon spawning and instream habitat data collected within coastal Oregon watersheds as part of a long-term monitoring program. We modeled two processes as a function of habitat characteristics: the number of coho salmon when they were present and the occupancy probabilities of coho salmon. The results from both submodels were then combined into an estimate of total abundance at each site. Adult coho salmon occupancy was best predicted by the capacity of the habitat to support parr during the winter, complex pools, percent bedrock, and site distance to the ocean. Although lacking the predictive capacity of the occupancy model, increases in adult coho counts at sites were also influenced by the site distance to the ocean, and there is evidence that both percent gravel and complex pools may also be valuable predictors. By taking advantage of long-term datasets with broad spatial range, using an integrative approach across coho salmon life stages, and utilizing innovative Bayesian modeling techniques, this study is a unique approach to understanding a complicated ecological narrative. Combined, our results indicate the spatial distribution and proximity of spawning and rearing habitats may maximize productivity for coho salmon in coastal Oregon watersheds.
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Iampietro, Pat J., Rikk G. Kvitek, and Erica Morris. "Recent Advances in Automated Genus-specific Marine Habitat Mapping Enabled by High-resolution Multibeam Bathymetry." Marine Technology Society Journal 39, no. 3 (September 1, 2005): 83–93. http://dx.doi.org/10.4031/002533205787442495.
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There is a great need for accurate, comprehensive maps of seafloor habitat for use in fish stock assessments, marine protected area design, and other resource management pursuits. Recent advances in acoustic remote sensing technology have made it possible to obtain high-resolution (meter to sub-meter) digital elevation models (DEMs) of seafloor bathymetry that can rival or surpass those available for the terrestrial environment. The acquisition and processing of these data are expensive, however, requiring specialized equipment, expertise, and large amounts of both field and laboratory effort per unit area mapped. Further, the interpretation and classification of these data into maps of habitat type is typically (and appropriately) performed only by trained experts that are familiar with both seafloor geomorphology and the nature and limitations of the data sources. Because it is done visually, this interpretation can be very time-consuming and may yield subjective results that are not comparable from site-to-site or between individual interpreters.We applied an algorithmic terrain analysis approach to efficiently and objectively classify seafloor habitats using the quantifiable landscape metric Topographic Position Index (TPI). We used high-resolution multibeam bathymetry, together with precisely geolocated (± 5 m) ROV observations of fish distribution, to produce a preliminary genus-specific habitat suitability model for eight rockfish (Sebastes) species in the Del Monte shale beds of Monterey Bay, California. A high-resolution (2 m) multibeam bathymetry Digital Elevation Model (DEM) was generated and used to produce a derived TPI surface model using repeatable, algorithmic methods. This data layer, together with the positions and counts by species from 229 rockfish observations (2892 total fish) was then used to create preliminary predictive models of habitat suitability and fish distribution, as well as stock estimates for the study area. A second, independent fish observation data set was used to validate the models.
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Hofmann, Michaela M., Constantin M. Zohner, and Susanne S. Renner. "Narrow habitat breadth and late-summer emergence increases extinction vulnerability in Central European bees." Proceedings of the Royal Society B: Biological Sciences 286, no. 1898 (March 6, 2019): 20190316. http://dx.doi.org/10.1098/rspb.2019.0316.
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Evaluating intrinsic and extrinsic traits that predispose species to local extinction is important for targeting conservation efforts. Among the species of special concern in Europe are bees, which, along with butterflies, are the best monitored insects. Bees are most species-rich in Mediterranean-type climates with short winters, warm springs, and dry summers. In Central Europe, climate warming per se is, therefore, expected to benefit most bee species, while pesticides and the loss of habitats and plant diversity should constitute threats. Here, we use the bee fauna of Germany, which has been monitored for Red Lists for over 40 years, to analyse the effects of habitat breadth, pollen specialization, body size, nesting sites, sociality, duration of flight activity, and time of emergence during the season. We tested each factor's predictive power against changes in commonness and Red List status, using phylogenetically informed hierarchical Bayesian (HB) models. Extinction vulnerability is strongly increased in bees flying in late summer, with a statistical model that included flight time, habitat preference, and duration of activity correctly predicting the vulnerability status of 85% of the species. Conversely, spring emergence and occurrence in urban areas each reduce vulnerability, pointing to intensive land use especially harming summer-active bees, with the combination of these factors currently shifting Germany's bee diversity towards warm-adapted, spring-flying, city-dwelling species.
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Ritter, Michael W., and Julie A. Savidge. "A Predictive Model of Wetland Habitat Use on Guam by Endangered Mariana Common Moorhens." Condor 101, no. 2 (May 1999): 282–87. http://dx.doi.org/10.2307/1369991.
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PEARSON, Kara, Robert CAMERON, and R. Troy MCMULLIN. "Habitat associations and distribution model forFuscopannaria leucostictain Nova Scotia, Canada." Lichenologist 50, no. 4 (July 2018): 487–97. http://dx.doi.org/10.1017/s0024282918000300.
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AbstractFuscopannaria leucostictais a rare and understudied cyanolichen with an interesting and unusual distribution in tertiary relict hotspots worldwide. There is a relatively large population in eastern North America, where it occurs mostly throughout the Appalachian Mountains and reaches its northernmost extent in New Brunswick and Nova Scotia, Canada. The ability to detect this species, and thus determine its habitat requirements, is critical for understanding how it might be affected by human-induced environmental degradation. Maximum entropy modelling with MaxEnt was used to predict the distribution of suitable habitat for this species in Nova Scotia using 62 presence locations, 1405 pseudo-absence locations and four environmental covariates: depth to water table (a proxy for relative soil moisture), distance to the coast and mean annual temperature and precipitation. Our predictive maps identify important habitat features and areas of high suitability in Nova Scotia with an area under the curve value of 0·85. The predicted distribution of this lichen was most affected by temperature. This study elucidates locations as well as species-habitat relationships forF. leucosticta,providing land managers with baseline data that can aid in the discovery of additional populations and provide a better understanding of its ecological requirements which will support the development of sound conservation strategies for this rare lichen.
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McCleary, Richard J., and Marwan A. Hassan. "Predictive modeling and spatial mapping of fish distributions in small streams of the Canadian Rocky Mountain foothills." Canadian Journal of Fisheries and Aquatic Sciences 65, no. 2 (February 1, 2008): 319–33. http://dx.doi.org/10.1139/f07-161.
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We developed an automated procedure for modeling spatial distribution of fish occurrence using logistic regression models and geographic information system (GIS) tools. Predictors were measured from a digital elevation model (DEM) and stream layers. We evaluated the accuracy of GIS measures of reach slope through a comparison with field measures. Resource selection function models were used to explain presence-absence of bull trout (Salvelinus confluentus), rainbow trout, (Oncorhynchus mykiss), nonnative brook trout (Salvelinus fontinalis), and all fishes. Our models were extrapolated based on low, medium, and high levels of probability to produce reach-scale maps across 12 000 km2. We attempted to improve models by adding land-use variables; however, the terrain best suited to road building and harvest also contained the habitat selected by rainbow trout, whereas bull trout generally selected terrain too steep for land use. These confounding factors emphasize the need for process-based investigations in addition to correlative approaches to identify habitat requirements. This automated method provides a rapid evaluation of fish habitat across remote areas useful for salmonid conservation and research planning.
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Evangelista, Paul H., John Norman, Lakew Berhanu, Sunil Kumar, and Nathaniel Alley. "Predicting habitat suitability for the endemic mountain nyala (Tragelaphus buxtoni) in Ethiopia." Wildlife Research 35, no. 5 (2008): 409. http://dx.doi.org/10.1071/wr07173.
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The use of statistical models to predict species distributions and suitable habitat has become an essential tool for wildlife management and conservation planning. Models have been especially useful with rare and endangered wildlife species. One such species is the mountain nyala (Tragelaphus buxtoni), a spiral-horned antelope endemic to the Ethiopian highlands. The full range of the species has never been adequately defined and recent discoveries of new populations suggest that others may exist undetected. To identify potential mountain nyala occurrences, we used classification tree analysis to predict suitable habitat using 76 climatic, topographical and vegetative variables. Three model evaluation methods showed a strong performance of the final model with an overall accuracy of 90%, Cohen’s maximised κ of 0.80 and area under the receiver operating characteristic curve (AUC) value of 0.89. Minimum temperature and maximum precipitation generally had the greatest predictive contributions to suitable mountain nyala habitat. The predicted habitat covered an area of 39 378 km2, with the majority occurring in remote forests on the southern escarpment of the Bale Mountains. Other areas within the predicted range may be too impacted by human and livestock populations to support mountain nyala; however, the model will be useful in directing future surveys for new populations while offering clues to the species historical range.
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Perzanowski, Kajetan, Maciej Januszczak, and Rafał Łopucki. "Historical changes in land use influence current habitat preferences of large herbivores." Landscape Ecology 34, no. 10 (September 12, 2019): 2251–59. http://dx.doi.org/10.1007/s10980-019-00899-4.
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Abstract Context Predicting habitat use patterns is a key issue in the management of large herbivore populations. Particularly, indicators providing a model of the spatial distribution of a population in a simple way, without the necessity of laborious field research, are still being sought. Analysis of historical landscape changes can be one of such predictive tools. Objectives We tested the hypothesis that historical changes in land use can be used as an effective factor enabling prediction of spatial distribution. As a case study, data on habitat preferences of European bison Bison bonasus (wisents) were used. Methods Spatial distribution of 17302 records of the presence of wisents, collected over the period of 10 years, was compared using contemporary and historical habitat maps for the Bieszczady Mts. (Poland). The area of approx. 87 thousand ha was selected, where the density of human population decreased over four times, and the percentage of forests increased from over 30% to almost 80% due to land abandonment. Results Wisents were recorded significantly more frequently in parts of the forest that in the past were used for agriculture. We found that identification of parts of the forest overgrowing former cultivated fields makes it possible to predict the spatial distribution of wisent herds with very high probability. Conclusions Information on historical changes in land use can be used as a simple and effective factor enabling prediction of habitat selection by wisents. Such an approach can potentially be useful for similar assessments of other large wild herbivores.
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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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Theodoropoulos, Christos, Nikolaos Skoulikidis, Anastasios Stamou, and Elias Dimitriou. "Spatiotemporal Variation in Benthic-Invertebrates-Based Physical Habitat Modelling: Can We Use Generic Instead of Local and Season-Specific Habitat Suitability Criteria?" Water 10, no. 11 (October 24, 2018): 1508. http://dx.doi.org/10.3390/w10111508.
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Generic habitat suitability criteria (HC) are often developed from spatially and temporally variable hydroecological datasets to increase generality, cost-effectiveness, and time-efficiency of habitat models. For benthic macroinvertebrates (BMIs), however, there is no prior knowledge on the spatiotemporal variation in their habitat preferences and how this may be reflected in the final environmental flow (e-flow) predictions. In this study, we used a large, spatiotemporally variable BMI-hydroecological dataset and developed generic, local, and season-specific subsets of HC for three seasons and two river types within various data pre-treatment options. Each subset was used to train a fuzzy habitat model, predict the habitat suitability in two hydrodynamically-simulated river reaches, and develop/compare model-based e-flow scenarios. We found that BMIs shift their habitat preferences among seasons and river types; consequently, spatiotemporally variable e-flow predictions were developed, with the seasonal variation being greater than the typological one. Within this variation, however, we found that with proper data pre-treatment, the minimum-acceptable e-flows from the generic models mostly (65–90%) lay within the acceptable e-flows predicted by the local and season-specific models. We conclude that, within specific limitations, generic BMI-HC can be used for geographically extended, cost-effective e-flow assessments, compensating for the within-limits loss of predictive accuracy.
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McCleery, Robert A., and Christa L. Zweig. "Leveraging limited information to understand ecological relationships of endangered Florida salt marsh vole." Journal of Mammalogy 97, no. 4 (May 9, 2016): 1249–55. http://dx.doi.org/10.1093/jmammal/gyw084.
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Abstract We were able to substantially increase our knowledge of what is likely the least understood endangered terrestrial mammal in the United States, the Florida salt marsh vole (FSMV; Microtus pennsylvanicus dukecampbelli). We developed a predictive landscape model that estimated 264 ha of potential habitat for FSMVs. Evaluating our model, we found voles at 8 of the 36 sites sampled, yielding a model accuracy of 22% for a subspecies that previously was known from only 3 locations. Within areas of potential habitat, FSMVs selected patches of marsh vegetation > 0.49 ha with at least some (≥ 16.75% and ≤ 43.61%) smooth cordgrass (Spartina alterniflora) cover. Suggestive of a meta-population dynamic, FSMV activity decreased outside of patches of smooth cordgrass and saltgrass (Distichlis spicata) identified by the predictive landscape model. Our hierarchical approach to studying FSMVs allowed us to leverage a limited amount of data to ultimately produce important ecological information about an endangered species. This approach easily may be adapted to other mammals with similar information needs.
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Ejigu, D., A. Bekele, L. Powell, and J. M. Lernould. "Habitat preference of the endangered Ethiopian walia ibex (Capra walie) in the Simien Mountains National Park, Ethiopia." Animal Biodiversity and Conservation 38, no. 1 (March 2015): 1–10. http://dx.doi.org/10.32800/abc.2015.38.0001.
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Walia ibex (Capra walie) is an endangered and endemic species restricted to the Simien Mountains National Park, Ethiopia. Recent expansion of human populations and livestock grazing in the park has prompted concerns that the range and habitats used by walia ibex have changed. We performed observations of walia ibex, conducted pellet counts of walia ibex and livestock, and measured vegetation and classified habitat characteristics at sample points during wet and dry seasons from October 2009 to November 2011. We assessed the effect of habitat characteristics on the presence of pellets of walia ibex, and then used a spatial model to create a predictive map to determine areas of high potential to support walia ibex. Rocky and shrubby habitats were more preferred than herbaceous habitats. Pellet distribution indicated that livestock and walia ibex were not usually found at the same sample point (i.e. 70% of quadrats with walia pellets were without livestock droppings; 73% of quadrats with livestock droppings did not have walia pellets). The best model to describe probability of presence of walia pellets included effects of herb cover (β = 0.047), shrub cover (β = 0.030), distance to cliff (β = –0.001), distance to road (β = 0.001), and altitude (β = 0.004). Walia ibexes have shifted to the eastern, steeper areas of the park, appearing to coincide with the occurrence of more intense, human–related activities in lowlands. Our study shows the complexities of managing areas that support human populations while also serving as a critical habitat for species of conservation concern.
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JOHNSON, CHRIS J., and MICHAEL P. GILLINGHAM. "An evaluation of mapped species distribution models used for conservation planning." Environmental Conservation 32, no. 2 (June 2005): 117–28. http://dx.doi.org/10.1017/s0376892905002171.
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The widespread use of spatial planning tools in conjunction with increases in the availability of geographic information systems and associated data has led to the rapid growth in the exploration and application of species distribution models. Conservation professionals can choose from a considerable number of modelling techniques, but there has been relatively little evaluation of predictive performance, data requirements, or type of inference of these models. Empirical data for woodland caribou Rangifer tarandus caribou was used to examine four species distribution models, namely a qualitative habitat suitability index and quantitative resource selection function, Mahalanobis distance and ecological niche models. Models for three sets of independent variables were developed and then a temporally independent set of caribou locations evaluated predictive performance. The similarity of species distribution maps among the four modelling approaches was also quantified. All of the quantitative species distribution models were good predictors of the validation data set, but the spatial distribution of mapped habitats differed considerably among models. These results suggest that choice of model and variable set could influence the identification of areas for conservation emphasis. Model choice may be limited by the type of species locations or desired inference. Conservation professionals should choose a model and variable set based on the question, the ecology of the species and the availability of requisite data.
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Gavashelishvili, Alexander, and Zura Javakhishvili. "Combining radio-telemetry and random observations to model the habitat of Near Threatened Caucasian grouseTetrao mlokosiewiczi." Oryx 44, no. 4 (October 2010): 491–500. http://dx.doi.org/10.1017/s0030605310000979.
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AbstractThe distribution of the Near Threatened Caucasian grouseTetrao mlokosiewiczi,endemic to the Caucasus, was examined to model the species’ nesting habitat, and thus facilitate its conservation and the identification of Key Biodiversity Areas in the Caucasus. The species’ occurrence was defined by field surveys and radio-telemetry. Data were managed and analysed using a geographical information system and various modelling techniques. Grouse locations were divided into training and testing datasets. Habitat variables measured at training locations were used to develop models, and testing locations were used to validate the models. The final best-fit model suggested that Caucasian grouse prefer open habitat, and the most important independent variables accounting for the species' distribution were annual mean temperature, mean temperature of warmest quarter, precipitation seasonality and proximity to deciduous broad-leaf forest. The incorporation of human disturbance and ruggedness into the final model significantly increased its predictive power. This model provides a tool to improve search effectiveness for Caucasian grouse in the Caucasus and for the conservation and management of the species. The model can predict the probable distribution of Caucasian grouse and the corridors between known populations. Threatened and endemic species are often used as species for setting site-based conservation priorities, and this habitat model could help to identify new Key Biodiversity Areas for protection in the Caucasus. The Ministry of Environmental Protection and Natural Resources of Georgia is going to use the results of this study to reshape existing protected areas and identify new ones.
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Debinski, Diane, Mark Jakubaukas, and Kelly Kindscher. "Modeling Spatial and Temporal Dynamics of Montane Meadows and Biodiversity in the Greater Yellowstone Ecosystem." UW National Parks Service Research Station Annual Reports 22 (January 1, 1998): 59–64. http://dx.doi.org/10.13001/uwnpsrc.1998.3367.
Full textAbstract:
Our project is an examination of ecological dynamics in the Greater Yellowstone Ecosystem (GYE), concentrating specifically upon the spatial and temporal dynamics of montane meadow communities. We are examining both the abiotic aspects of these communities as well as the biodiversity of plant, bird and butterfly communities. Our long-term goal is to develop predictive species assemblage models based upon landscape level habitat analysis. This involves using intensive, local field sampling to test for relationships between species distribution patterns and remotely sensed data. This research involves several steps: 1) quantifying the spatial and temporal variability in montane meadow communities; 2) developing a spectrally-based spatially-explicit model for predicting plant and animal species diversity patterns in montane meadows; and 3) testing the spectrally-based spatiallyÂexplicit model for predicting plant and animal species diversity patterns in montane meadows.
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Debinski, Diane, Mark Jakubauskas, and Kelly Kindscher. "Modeling Spatial and Temporal Dynamics of Montane Meadows and Biodiversity in the Greater Yellowstone Ecosystem." UW National Parks Service Research Station Annual Reports 23 (January 1, 1999): 152–58. http://dx.doi.org/10.13001/uwnpsrc.1999.3399.
Full textAbstract:
Our project is an examination of ecological dynamics in the Greater Yellowstone Ecosystem (GYE), concentrating specifically upon the spatial and temporal dynamics of montane meadow communities. We are examining both the abiotic aspects of these communities as well as the biodiversity of plant, bird and butterfly communities. Our long-term goal is to develop predictive species assemblage models based upon landscape level habitat analysis. This involves using intensive, local field sampling to test for relationships between species distribution patterns and remotely sensed data. This research involves several steps: 1) quantifying the spatial and temporal variability in montane meadow communities; 2) developing a spectrally-based spatially-explicit model for predicting plant and animal species diversity patterns in montane meadows; and 3) testing the spectrally-based spatiallyÂexplicit model for predicting plant and animal species diversity patterns in montane meadows.