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48 results for “Habitat Distribution Model”
Data from: Habitat suitability models reveal extensive distribution of deep warm water coral frameworks in the Red Sea
<p>Deep-sea coral frameworks are understudied in the Red Sea, where conditions in the deep are conspicuously warm and saline compared to other basins. Habitat suitability models can be used to predict the distribution pattern of species or assemblages where direct observation is difficult. Here we show how coral frameworks, built by species within the families Caryophylliidae and Dendrophylliidae, are distributed between water depths of 150 m and 700 m in the northern Red Sea and Gulf of Aqaba. To extrapolate the known (ground-truthed) positions of these deep frameworks, we use environmental and geomorphometric variables to inform well-performing maximum entropy models. Over 250 km2 of seafloor in our study area are identified as suitable for such frameworks, equivalent to at least 35% of the area of photic-zone coral reefs in the same region. We hence contend that deep-water coral frameworks are an important and underappreciated repository of Red Sea biodiversity.</p>
Fig. 2 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 2. Linear relationship (solid line) and 95 % confidence interval (gray area) between habitat quality predicted by the BART model (x-axis) and shell height (H in millimeters, y-axis), derived from the linear mixed model.
Fig. 4. Partial dependence plot for topographic Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 4. Partial dependence plot for topographic Fig. 5. Partial dependence plot for terrain roughness wetness index (TWI). index (tri).
Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 6. Partial dependence plot for pH water (phh2o). Fig. 7. Partial dependence plot for silt content (SLT).
Habitats as predictors in species distribution models: Shall we use continuous or binary data?
<p>The representation of a land cover type (i.e., habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g., wetlands) while for common habitats (e.g., forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e., the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g., using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology, and ecological conservation.</p>
Fig. 3. Partial dependence plot for BIO17 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 3. Partial dependence plot for BIO17 = Precipitation of Driest Quarter; gray area = 95 % confidence interval.
Figure 4 in Modeling habitat suitability and current distribution of the Maghreb magpie (Pica mauritanica)
Figure 4. (A) Current distribution of the Maghreb magpie in North Africa, (B) binary map of habitat suitability with a threshold> 0.6.
Figure 6 in Modeling habitat suitability and current distribution of the Maghreb magpie (Pica mauritanica)
Figure 6. Response curves of the explanatory variables included in the species distribution model (SDM) for Pica mauritanica. (MTWQ: mean temperature of wettest quarter).
Figure 5 in Modeling habitat suitability and current distribution of the Maghreb magpie (Pica mauritanica)
Figure 5. Two-dimensional plots of Pica mauritanica niche hypervolume with the most influential variables.
Habitats as predictors in species distribution models: Shall we use continuous or binary data?
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Fig.1 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig.1. Sampling sites for Vestia turgida in Ukraine (photo by O. Baidashnikov).
Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 5. Partial dependence plot for terrain roughness index (tri).
Fig. 7 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 7. Partial dependence plot for silt content (SLT).
Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 6. Partial dependence plot for pH water (phh2o).
Data from: Occurrence-habitat mismatching and niche truncation when modelling distributions affected by anthropogenic range contractions
<p><strong>Aims: </strong>Human-induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data—where occurrences represent distributions before (past bias) or after (recent bias) ARCs—underpins these data misrepresentations. Occurrence-habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance.</p> <p><strong>Location:</strong> Virtual Southeast Asia</p> <p><strong>Methods:</strong> Using 100 virtual species, we simulated ARCs with 100-year land-use data and generated temporally biased (past, recent) occurrence datasets. We modelled datasets with and without a contemporary land-use variable (conventional modelling protocols) and with a temporally dynamic land-use variable. We evaluated each model's ability to predict historical and contemporary distributions.</p> <p><strong>Results:</strong> Greater ARC resulted in greater occurrence-habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence-habitat mismatching prevented models with the contemporary land-use variable from predicting anthropogenic-related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land-use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land-use variable consistently outperformed models without.</p> <p><strong>Main conclusions:</strong> We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of anthropogenic range contractions, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three-step strategy for handling data misrepresentations: maximise the temporal range of anthropogenic predictors, exclude mismatched occurrences, and test for residual data misrepresentations.</p>
Modelling the potential global distribution of suitable habitat for the biological control agent Heterorhabditis indica
<p class="MsoNoSpacing">Entomopathogenic nematode (EPN) <em>Heterorhabditis indica</em> is a promising biocontrol candidate. Despite the acknowledged importance of EPN in pest control, no extensive data sets or maps have been developed on their distribution at global level. This study is the first attempt to generate Ecological Niche Models (ENM) for <em>H. indica</em> and its global Habitat Suitability Map (HSM) to generate biogeographical information and predicts its global geographical range of prospective areas for its exploration and to help identify the suitable release areas for biocontrol purpose. The aim of the modelling exercise was to access the influence of temperature and soil moisture on the biogeographical patterns of <em>H. indica</em> at the global level. CLIMEX software was used to model the distribution of <em>H. indica</em> and access to the influence of environmental variable on its global distribution. In total, 162 records of <em>H. indica</em> occurrence from 27 countries over 25 years was combined to generate the known distribution data. The model was further fine-tuned using the direct experimental observations of the <em>H. indica</em>'s growth response to temperature and soil moisture. Model predicts much of the tropics and subtropics has suitable climatic conditions for <em>H. indica</em>. It further predicts that <em>H. indica</em> distribution can extends into warmer temperate climates. Examination of the model output, predictions maps at a global level indicate that <em>H. indica</em> distribution may be limited by cold stress, heat stress and dry stresses in different areas. However, cold stress appears to be the major limiting factor. This study, highlighted an efficient way to construct HSM for EPN potentially useful in the search/release of target species in new locations. The study showed that <em>H. indica</em> which is known as warm adapted EPN generally found in tropics and subtropics can potentially establish itself in warmer temperate climates as well. The model can also be used to decide the release timing of EPN by adjusting with season for maximum growth. The model developed in the current study clearly identified the value and potential of Habitat Suitability Map (HSM) in planning of future surveys and application of <em>H. indica.</em></p>
Figure 1 in Modeling habitat suitability and current distribution of the Maghreb magpie (Pica mauritanica)
Figure 1. The distribution of Pica mauritanica throughout North Africa.
Figure 3 in Modeling habitat suitability and current distribution of the Maghreb magpie (Pica mauritanica)
Figure 3. Variable importance (based on correlation metric) of the ensemble model.
Fijian habitat and invertebrate species distribution modelling
<p><strong>Aim</strong></p> <p>Spatially explicit protections of coastal habitats determined on the current distribution of species and ecosystems risk becoming obsolete in 100 years if the movement of species ranges outpaces management action. Hence, a critical step of conservation is predicting the efficacy of management actions in future. We aimed to determine how foundational, habitat‐building species will respond to climate change in Fiji.</p> <p><strong>Location</strong></p> <p>The Republic of Fiji.</p> <p><strong>Methods</strong></p> <p>We develop species distribution models (SDMs) using MaxEnt, General Additive Models and Boosted Regression Trees and publicly available data from the Global Biodiversity Information Facility to predict changes in distribution of suitable habitat for mangrove forests, coral habitat, seagrass meadows and critical fisheries invertebrates under several IPCC climate change scenarios in 2070 or 2100. We then overlay predicted distribution models onto existing Fijian protected area network to assess whether today's conservation measures will afford protection to tomorrow's distributions.</p> <p><strong>Results</strong></p> <p>We develop species distribution models (SDMs) using MaxEnt, General Additive Models and Boosted Regression Trees and publicly available data from the Global Biodiversity Information Facility to predict changes in distribution of suitable habitat for mangrove forests, coral habitat, seagrass meadows and critical fisheries invertebrates under several IPCC climate change scenarios in 2070 or 2100. We then overlay predicted distribution models onto existing Fijian protected area network to assess whether today's conservation measures will afford protection to tomorrow's distributions.</p> <p><strong>Main conclusions</strong></p> <p>Species distribution models are a critical tool for conservation managers, as linking spatial distribution data with future climate change scenarios can aid in the creation and resiliency of protected area programmes. New protected area designations should consider the future distribution of species to maximize benefits to those taxa.</p>
Habitat distribution models for pygmy rabbits in Idaho
<p>Environmental relationships can differ across the geographic range of species, especially for widespread generalists. Because habitat specialists are more vulnerable to environmental changes, incorrect assumptions about consistent habitat associations could hinder strategic conservation efforts. We used species distribution models (SDMs) to evaluate intraspecific variation in habitat associations for a habitat specialist of conservation concern, the pygmy rabbit (<em>Brachylagus idahoensis</em>), which is endemic to the sagebrush biome of the western USA. Our goal was to model habitat associations for pygmy rabbits across a portion of their range to evaluate regional variation and contrast predictions with results from a model developed at the rangewide extent. We created inductive SDMs using maximum entropy methods within five ecological regions that encompassed about 20% of the species rangewide distribution and spanned diverse environmental gradients. We included a suite of environmental predictor variables representing topography, vegetation, climate, and soil characteristics. Results of the regional models identified substantial variation in habitat associations across the five regions, with each retaining a unique set of environmental predictors. Bioclimatic variables were the most influential environmental parameters in all five regions, but the specific variables differed. The models developed at regional extents predicted smaller areas of habitat (an average of 15% less for suitable habitat and 80% less for primary habitat) than predictions generated from a model developed at the rangewide extent. Because bioclimatic variables were effective in discriminating areas used by pygmy rabbits, they also provided an opportunity to assess potential changes in habitat distribution by incorporating future climate projections. Distributions modeled under two mid-century emission scenarios projected substantial reductions in suitable habitat for pygmy rabbits across most regions and pronounced variation among regions in the magnitude and direction of the climate effects. Collectively, results of this work underscore the need to incorporate regional variation in habitat associations into planning for current and future conservation and management strategies.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.