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487 results for “species distribution model”
Data from: Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling
Remote sensing techniques offer an opportunity to improve biodiversity modeling and prediction worldwide. Yet, to date, the weather-station based WorldClim dataset has been the primary source of temperature and precipitation information used in correlative species distribution models. WorldClim consists of grids interpolated from in situ station data recorded primarily from 1960 to 1990. Those datasets suffer from uneven geographic coverage, with many areas of Earth poorly represented. Here, we compare two remote sensing data sources for the purposes of biodiversity prediction: MERRA climate reanalysis data and AMSR-E, a pure remote sensing data source. We use these data to generate novel temperature-based bioclimatic information and to model the distributions of 20 species of vertebrates endemic to four regions of South America: Amazonia, the Atlantic Forest, the Cerrado, and Patagonia. We compare the bioclimatic datasets derived from MERRA and AMSR-E information with in situ station data, and contrast species distribution models based on these two products to models built with WorldClim. Surface temperature estimates provided by MERRA and AMSR-E showed warm temperature biases relative to the in situ data fields, but the reliability of these datasets varied in geographic space. Species distribution models derived from the MERRA data performed equally well (in Cerrado, Amazonia, and Patagonia) or better (Atlantic Forest) than models built with the WorldClim data. In contrast, the performance of models constructed with the AMSR-E data was similar to (Amazonia, Atlantic Forest, Cerrado) or worse than (Patagonia) that of models built with WorldClim data. Whereas this initial comparison assessed only temperature fields, efforts to estimate precipitation from remote sensing information hold great promise; furthermore, other environmental datasets with higher spatial and temporal fidelity may improve upon these results.
Data from: Suitability of Laurentian Great Lakes for invasive species based on global species distribution models and local habitat
Efficient management and prevention of species invasions requires accurate prediction of where species of concern can arrive and persist. Species distribution models provide one way to identify potentially suitable habitat by developing the relationship between climate variables and species occurrence data. However, these models when applied to freshwater invasions are complicated by two factors. The first is that the range expansions that typically occur as part of the invasion process violate standard species distribution model assumptions of data stationarity. Second, predicting potential range of freshwater aquatic species is complicated by the reliance on terrestrial climate measurements to develop occurrence relationships for species that occur in aquatic environments. To overcome these obstacles, we combined a recently developed algorithm for species distribution modeling—range bagging—with newly available aquatic habitat-specific information from the North American Great Lakes region to predict suitable habitat for three potential invasive species: golden mussel, killer shrimp, and northern snakehead. Range bagging may more accurately predict relative suitability than other methods because it focuses on the limits of the species environmental tolerances rather than central tendency or "typical" cases. Overlaying the species distribution model output with aquatic habitat-specific data then allowed for more specific predictions of areas with high suitability. Our results indicate there is suitable habitat for northern snakehead in the Great Lakes, particularly shallow coastal habitats in the lower four Great Lakes where literature suggests they will favor areas of wetland and submerged aquatic vegetation. These coastal areas also offer the highest suitability for golden mussel, but our models suggest they are marginal habitats. Globally, the Great Lakes provide the closest match to the currently invaded range of killer shrimp, but they appear to pose an intermediate risk to the region. Range bagging provided reliable predictions when assessed either by a standard test set or by tests for spatial transferability, with golden mussel being the most difficult to accurately predict. Our approach illustrates the strength of combining multiple sources of data, while reiterating the need for increased measurement of freshwater habitat at high spatial resolutions to improve the ability to predict potential invasive species.
Data from: The critical role of local refugia in postglacial colonization of Chinese pine: joint inferences from DNA analyses, pollen records, and species distribution modeling
The importance of long-distance migration from low to high latitudes relative to local spread from northern refugia after the Last Glacial Maximum (LGM) remains a focus of debate for many temperate tree species. We assessed the dynamics of Chinese pine (Pinus tabulaeformis), a widespread species endemic to northern China, since the LGM by integrating cytoplasmic DNA data, mapped pollen records and ecological niche modeling. Genetic variation among 544 individuals from 50 populations spanning the entire natural species range revealed eight genetic clusters with distinct geographic distribution, indicating glacial lineages likely originating from multiple local microrefugia. Palynological evidence suggested that the northernmost part of the natural distribution originated from local postglacial spread. Niche modeling indicated high probability of the species being present in the area of the Loess Plateau and coastal areas north of the Yangtze River during the LGM. The three lines of evidence jointly suggest that the species persisted through the last glaciation in the mountains surrounding the Loess Plateau of northern China and that the current distribution of the species originated primarily from the spread of local refugial populations, instead of long-distance migration. These results cast doubt on the notion that Chinese pine migrated from areas south of the Yangtze River and underscore the importance of northern refugia.
Data from: The 'golden kelp' Laminaria ochroleuca under global change: integrating multiple eco-physiological responses with species distribution models
1. The loss of marine foundation species, in particular kelps at temperate latitudes, has been linked to climatic drivers and co-occurring human perturbations. Ocean temperature and nutrients typically co-vary over local and regional scales and play a crucial role on kelp dynamics. Examining their independent and interactive effects on kelp physiological performance is essential to understand and predict patterns of kelp distribution, particularly under scenarios of global change. 2. Crossed combinations of ocean temperatures and availability of nutrients were experimentally tested on juveniles of the 'golden kelp', Laminaria ochroleuca, from the northwestern Iberian Peninsula. Eco-physiological responses included: survival, growth and total N content. Results were embedded into a Species Distribution Model (SDM), which relates presence records and climatic and non-climatic data to forecast distribution patterns of L. ochroleuca under different climate change scenarios. 3. Temperatures above 24.6 ºC were lethal irrespective of nutrients. Optimal growth of juvenile sporophytes occurred between 12 °C and 18 ºC and no nutrient limitation. The SDM, where ocean temperature was the main predictor of kelp distribution in line with temperature thresholds given by eco-physiological responses, suggests a future expansion towards northern latitudes and a retreat from the southern limit/boundary of the current distribution. 4. Synthesis. Range-shifting of the golden kelp can have severe ecological impacts at regional and local scales. The expansion or retraction of the species along the European coast seems to be modulated mainly by temperature, but nutrient availability would be key to maintain optimal physiological performance. Our work highlights that the combination of empirical and modelling approaches is accessible to researchers and crucial to build more robust predictions of ecological and biogeographic responses of habitat-forming species to forecasted environmental change.
Data from: Habitat-based species distribution modelling of the Hawaiian deepwater snapper-grouper complex
Deepwater snappers and groupers are valuable components of many subtropical and tropical fisheries globally and understanding the habitat associations of these species is important for spatial fisheries management. Habitat-based species distribution models were developed for the deepwater snapper-grouper complex in the main Hawaiian Islands (MHI). Six eteline snappers (Pristipomoides spp., Aphareus rutilans, and Etelis spp.) and one endemic grouper (Hyporthodus quernus) comprise the species complex known as the Hawaiian Deep Seven Bottomfishes. Species occurrence was recorded using baited remote underwater video stations deployed between 30 and 365 m (n = 2381) and was modeled with 12 geomorphological covariates using GLMs, GAMs, and BRTs. Depth was the most important predictor across species, along with ridge-like features, rugosity, and slope. In particular, ridge-like features were important habitat predictors for E. coruscans and P. filamentosus. Bottom hardness was an important predictor especially for the two Etelis species. Along with depth, rugosity and slope were the most important habitat predictors for A. rutilans and P. zonatus, respectively. Models built using GAMs and BRTs generally had the highest predictive performance. Finally, using the BRT model output, we created species-specific distribution maps and demonstrated that areas with high predicted probabilities of occurrence were positively related to fishery catch rates.
Data from: Multiresponse algorithms for community-level modeling: review of theory, applications, and comparison to species distribution models
1.Community-level models (CLMs) consider multiple, co-occurring species in model fitting and are lesser known alternatives to species distribution models (SDMs) for analyzing and predicting biodiversity patterns. CLMs simultaneously model multiple species, including rare species, while reducing overfitting and implicitly considering drivers of co-occurrence. Many CLMs are direct extensions of well-known SDMs and therefore should be familiar to ecologists. However, CLMs remain underutilized, and there have been few tests of their potential benefits and no systematic reviews of their assumptions and implementations. Here we review this emerging field and provide examples in R to fit common CLMs. Our goal is to introduce CLMs to a broader audience, and discuss their attributes, benefits, and limitations relative to SDMs. 2.We review i) statistical implementations and applications of CLMs, ii) their advantages and limitations, and iii) comparative analyses of CLMs and SDMs. We also suggest directions for future research. 3.We identify seven CLM algorithms with similar data structures and predictive outputs as SDMs that should be most accessible to ecologists familiar with species-level modeling, including five methods that predict assemblage composition and individual species distributions and two methods that model compositional turnover along environmental gradients. CLMs have been applied to numerous taxa, regions, and spatial scales, and a variety of topics (e.g., studying drivers of community structure or assessing relationships between community composition and functional traits). Studies suggest that the relative benefits of CLMs and SDMs may be case specific, especially in terms of predicting species distributions and community composition. However, CLMs may offer advantages in terms of computational efficiency, modeling rare species, and projecting to no-analog climates. A major shortcoming of CLMs is their reliance on presence-absence community composition data. 4.Studies are needed to assess the relative merits of SDMs and CLMs, and different CLM algorithms, with a focus on three key areas: i) under which circumstances CLMs improve predictions for rare species, ii) how CLMs perform under different community compositions (e.g. relative abundance of rare vs. common species), including the extent to which co-occurrence patterns are structured by biotic interactions, and iii) ability to project across time/space.
Dispersal patterns and potential distribution prediction of three rice planthopper species in China based on the ensemble model
<p>Emergence of three rice planthopper species in China from 1993 to 2022.</p>
Performance results from species distribution models considering historical occurrences and variables of varying persistency
<p>Occurrence data used to build species distribution models often include historical records from locations in which the species no longer exists. When these records are paired with contemporary environmental values that no longer represent the conditions the species experienced, the model creates false associations that hurt predictive performance. The extent of mismatching increases with the number of historical occurrences and with inclusion of environmental variables that are prone to change over time. Indeed, the mismatch between occurrence data and contemporaneous environmental variables is a common dilemma when modeling rare or cryptic species, especially those of conservation concern that were once more abundant. Herein, we assess (1) the impact of historical occurrences on model performance across three sets of environmental variables of increasing persistency, and (2) the performance of models built using selected-historical occurrences from locations that showed evidence of limited environmental change over time. Concepts are tested on federally listed flatwoods salamanders, reflecting real-world conservation management efforts. We predicted that, compared to other occurrence sets, (1) historical occurrences would perform best with environmental variables that were more persistent, (2) recent occurrences would perform best when the environmental variables were more impersistent, and that (3) our selected-historical occurrences would perform best with a combination of persistent and impersistent variables. Our results showed the expected inversion of model performance of recent and historical occurrences across environmental variables of increasing persistency when evaluated by correct predictions. However, the inversion was not seen in AUC performance, in which historical occurrences outperformed recent occurrence models across all variable sets. Selected-historical occurrences did not notably improve performance over all-historical occurrences in any metric or variable set. To maximize utility and performance, modelers could acknowledge potential tradeoffs from inclusion of historical occurrences and consider number and age of recent and historical occurrences available, the persistency of environmental variables considered, and how their conservation goals are reflected in model design and evaluation, particularly with respect to sensitivity vs. specificity. Our study lends support for inclusion of historical occurrences, with the potential exception of mostly impersistent variables when sensitivity is the highest priority.</p>
Conservation status assessment of banana crop wild relatives using species distribution modelling
<p><strong>Aim: </strong>Crop wild relatives (CWR) are an essential source of genetic material for the improvement of certain traits in related crop species. Despite their importance, increasing public, scientific and political support, large gaps exist in the amount of genetic material collected and conserved of many CWR. Here, we construct a dataset on the distribution of wild banana species (Musa spp.) and assess their risk and conservation status. We deal with the questions (1) what areas are potentially suitable for wild banana species (2) how much of the wild banana diversity is currently at risk or insufficiently conserved ex- and in-situ.</p> <p><span><strong>Location: </strong>Native distribution area of wild banana species, ranging from the northeastern states of India to northeastern Australia.</span></p> <p><span><strong>Methods:</strong> We assessed the potential environmental range of wild species using a species distribution modelling approach with MaxEnt. Extinction risk was evaluated following IUCN criterion B and the <i>ex- </i>and <i>in-situ</i> conservation status was assessed using an indicator for biodiversity and sustainable development targets.</span></p> <p><span><strong>Results:</strong> We found that 11 out of 59 assessed species can be considered as vulnerable and nine as endangered. Highest species richness was found along the border of south China and northern Vietnam, in the northeastern states of India and on the Malayan peninsula. Our distribution modelling approach indicates that the northern Indo-Burmese region has the highest environmental suitability for most wild banana species and that lowland rainforests in general are highly suitable for bananas. Assessment of <i>in</i>- and <i>ex-situ</i> conservation status indicates that 56 out of 59 assessed species are currently insufficiently conserved <i>ex-situ</i> and that 49 are of high priority for further conservation. Additional <i>in-situ</i> conservation is of high priority for six species and of medium priority for 40 species.</span></p> <p><span><strong>Main Conclusions:</strong> To date, little of the banana CWR are sufficiently conserved both <i>in- </i>and <i>ex-situ</i>.</span></p>
Occurence data for species distribution modelling of wild Coffea canephora
<p><span>The assessment of population vulnerability under climate change is crucial for planning conservation as well as for ensuring food security. <em>Coffea canephora</em> is, in its native habitat, an understory tree that is mainly distributed in the lowland rainforests of tropical Africa. Also known as Robusta, its commercial value constitutes a significant revenue for many human populations in tropical countries. Comparing ecological and genomic vulnerabilities within the species' native range can provide valuable insights about habitat loss and the species' adaptive potential, allowing to identify genotypes that may be act as a resource for varietal improvement. By applying species distribution models, we assessed ecological vulnerability as the decrease in climatic suitability under future climatic conditions from 492 occurrences. We then quantified genomic vulnerability (or risk of maladaptation) as the allelic composition change required to keep pace with predicted climate change. Genomic vulnerability was estimated from genomic environmental correlations throughout the native range. Suitable habitat was predicted to diminish to half its size by 2050, with populations near coastlines and around the Congo River being the most vulnerable. Whole-genome sequencing revealed 165 candidate SNPs associated to climatic adaptation in <em>C. canephora</em>, which were located in genes involved in plant response to biotic and abiotic stressors. Genomic vulnerability was higher for populations in West Africa and in the region at the border between DRC and Uganda. Despite an overall low correlation between genomic and ecological vulnerability at broad scale, these two components of vulnerability overlap spatially in ways that may become damaging. Genomic vulnerability was estimated to be 23% higher in populations where habitat will be lost in 2050 compared to regions where habitat will remain suitable. These results highlight how ecological and genomic vulnerabilities are relevant when planning on how to cope with climate change regarding an economically important species.</span></p>
Supplementary material 3 from: Cosandey V, Broennimann O, Guisan A (2022) Modeling the distribution of coprophagous beetle species in the Western Swiss Alps. Alpine Entomology 6: 25-38. https://doi.org/10.3897/alpento.6.83730
Figure S3
Supplementary material 2 from: Cosandey V, Broennimann O, Guisan A (2022) Modeling the distribution of coprophagous beetle species in the Western Swiss Alps. Alpine Entomology 6: 25-38. https://doi.org/10.3897/alpento.6.83730
Figure S2
Supplementary material 1 from: Cosandey V, Broennimann O, Guisan A (2022) Modeling the distribution of coprophagous beetle species in the Western Swiss Alps. Alpine Entomology 6: 25-38. https://doi.org/10.3897/alpento.6.83730
Figure S1
Supplementary material 3 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235
Weighted mean SDMs for individual algorithms and evaluation statistics (biomod2) :
Supplementary material 2 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235
Full readout for the MaxEnt northern quoll SDM :
Supplementary material 1 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235
GIS data sets used in variable assessments and map of Pilbara vegetation systems :
Figure 2 from: Guillaumot C, Martin A, Fabri-Ruiz S, Eléaume M, Saucède T (2016) Echinoids of the Kerguelen Plateau – occurrence data and environmental setting for past, present, and future species distribution modelling. ZooKeys 630: 1-17. https://doi.org/10.3897/zookeys.630.9856
Figure 2 - Distribution of the 12 echinoid species based on the specimens collected since 1872 on the Kerguelen Plateau.
Figure 1 from: Guillaumot C, Martin A, Fabri-Ruiz S, Eléaume M, Saucède T (2016) Echinoids of the Kerguelen Plateau – occurrence data and environmental setting for past, present, and future species distribution modelling. ZooKeys 630: 1-17. https://doi.org/10.3897/zookeys.630.9856
Figure 1 - Sampling effort. Red dots depict echinoid occurrences. Black squares correspond to visited sites at which no echinoid was sampled.
Fig. 1 A in Phylogeography and potential glacial refugia of terrestrial gastropod Faustina faustina (Rossmässler, 1835) (Gastropoda: Eupulmonata: Helicidae) inferred from molecular data and species distribution models
Fig. 1 A phylogenetic tree of mitochondrial COI sequences of F. faustina associated with the map of the haplotype distribution in the studied area. Only topology of Bayesian tree is shown. Numbers at nodes of each clade
Fig. 6 in Phylogeography and potential glacial refugia of terrestrial gastropod Faustina faustina (Rossmässler, 1835) (Gastropoda: Eupulmonata: Helicidae) inferred from molecular data and species distribution models
Fig. 6 Maps of predicted climatic suitability for F. faustina under present (a) and paleo periods (b–g). Warmer colors indicate higher climatic suitability. Black dots indicate present records used in species distribution
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.