Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,042
datasets available to search
ShareScore release 0.9.0
Dataset results
1,042 results for “model species”
Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
<p>1. Patterns in, and the underlying dynamics of, species cooccurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterisations have been published that could be used with the same fundamental modelling framework that accounts for imperfect detection, although each parameterisation has different advantages and disadvantages.</p> <p>2. We propose a parameterisation based on log-linear modelling that does not require a species hierarchy to be defined (in terms of dominance), and enables a numerically robust approach for estimating covariate effects.</p> <p>3. Conceptually the parameterisation is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, 'symmetric', interpretations of parameter estimates.</p> <p>4. The parameterisation can be applied to many species, in either a maximum-likelihood or Bayesian estimation framework. We illustrate the method using camera trapping data collected on three mesocarnivore species in South Texas.</p>
Using species distribution models and decision tools to direct surveys and identify potential translocation sites for a critically endangered species
<p>Aim: Occurrence records for cryptic species are typically limited or highly uncertain, leaving their distributions poorly resolved and hampering conservation. This can apply to well‐studied species, and increased survey effort and/or novel methods are required to improve distribution data. Here, we paired species distribution modelling (SDM) with decision tools to direct surveys for the critically endangered Leadbeater's possum (Gymnobelideus leadbeateri) outside its current restricted range. We also assessed survey areas for their suitability to host translocations.</p> <p>Location: Victoria, Australia.</p> <p>Method: We used both recent and historic records (now out of range and spatially uncertain) of Leadbeater's possum to build SDMs using MaxEnt. The SDMs informed an initial multi‐criteria decision analysis (MCDA) that enabled prioritization of 80 survey sites across seven forest patches (13–145 km outside the known range), which we surveyed using camera traps. Site and vegetation data were used in a post‐survey MCDA to rank their potential translocation suitability.</p> <p>Results: The SDM predictions were consistent with the species' ecology, identifying cold areas with high rainfall that had not recently burnt as suitable. The spatial uncertainty of records did not exert a strong influence on either model predictions or the ranking of patches for surveys. Camera trap surveys yielded records of 19 native species, with Leadbeater's possum detected in only one survey patch, 13 km outside of its previously known range. The post‐survey MCDA identified three forest patches as potentially suitable for conservation translocations, and these priorities were not sensitive to the decision criteria used.</p> <p>Main conclusions: The approach outlined here prioritized survey effort over a large area, resulting in detection of Leadbeater's possum in one new patch. The potential translocation sites identified could present an important risk‐spreading measure for the species given the threat posed by bushfire. Combining SDMs and decision tools can help target surveys and guide subsequent conservation strategies.</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.
Fig. 6 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 6. Result of the analysis of Binomial tests (CliMond 2090 (2081–2100)): A — T. graeca; B — T. hermanni.
Fig. 3 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 3. Niche clustering (Geographic space, CliMond 1975 (1970–2000)) from: A — T. graeca (1. T. g. ibera, 2. T. nikolskii, 3. T. g. anamurensis, 4. T. g. floweri, 5. T. g. antakyensis, 6. T. g. pallasi, 7. T. g. armenica, 8. T. g. perses, buxtoni, 9. T. g. terrestris); B — T. hermanni (1. T. h. hermanni, 2. T. h. hervegovinensis, 3. T. h. boettgeri), red circles showing the approximate ranges of subspecies according to "Turtles…, 2017" World" (2017).
Fig. 2 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 2. The "Ecological envelope" — relationship bio01 "Annual mean temperature", °C & bio12 "Annual precipitation", mm (DivaGis): A — T. graeca; B — T. hermanni.
Fig. 5 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 5. Potential (probabilistic) model of T. hermanni world expansion built in the Maxent program based on the CliMond: A — 1975 (1970–2000); B — 2090 (2081–2100)) climatic data and GBIF data (2021). Areas of the highest habitat suitability (> 0.3–0.5) are colored in red and areas of the lowest (<0.2) — in blue (SAGA GIS).
Fig. 4 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 4. Potential (probabilistic) model of T. graeca expansion built in the Maxent program based on the CliMond: A — 1975 (1970–2000); B — 2090 (2081–2100)) climatic data and GBIF data (2021 a). Areas of the highest habitat suitability (> 0.3–0.5) are colored in red and areas of the lowest (<0.2) — in blue (SAGA GIS).
Fig. 1 in Interspecific Interactions as a Factor of Limitation of Geographical Distribution: Evidence Obtained by Modeling Home Ranges of Vole Twin Species Microtus Arvalis – M. Levis (Rodentia, Microtidae)
Fig. 1. Potential distribution of the Common vole Microtus arvalis. White circles are georeferenced occurrences of genetically identified individuals; black indicates areas of maximum habitat suitability, white are areas of lowest suitability.
Fig. 1 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 1. Spatial distribution of ignitions in 2010–2014 on studied area (dotted line is ATO zone's limits in 1.06– 30.09.2014).
Fig. 5 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 5. Distribution of two snake species, H. caspius and E. dione, in Ukrainian East (ATO zone is indicated by dotted line, burnt area marked inside zone).
Fig. 3 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 3. Spatial local distribution of ignitions in 2010–2014 in the outskirts of Slavyanoserbsk, Luhansk Region.
Factors influencing transferability in species distribution models
<p>Species distribution models (SDMs) provide insights into species' ecology and distributions and are frequently used to guide conservation priorities. However, many uses of SDMs require model transferability, which refers to the degree to which a model built in one place or time can successfully predict distributions in a different place or time. If a species' model has high spatial transferability, the relationship between abundance and predictor variables should be consistent across a geographical distribution. We used Breeding Bird Surveys, climate and remote sensing data, and a novel method for quantifying model transferability to test whether SDMs can be transferred across the geographic ranges of 129 species of North American birds. We also assessed whether species' traits are correlated with model transferability. We expected that prediction accuracy between modeled regions should decrease with 1) geographical distance, 2) degree of extrapolation, and 3) were affected by a 'core-boundary' effect, which assesses distances to the boundary of a distribution. Our results suggest that very few species have a high model transferability index (<em>MTI</em>). Species with large distributions, with distributions located in areas with low topographic relief, and with short lifespans are more likely to exhibit low transferability. Transferability between modeled regions also decreased with geographical distance and degree of extrapolation. We expect that low transferability in SDMs potentially resulted from both ecological non-stationarity (i.e., biological differences within a species across its range) and over-extrapolation. Accounting for non-stationarity and extrapolation should substantially increase prediction success of species distribution models, therefore enhancing the success of conservation efforts.</p>
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
<p>The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. </p> <p>Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane (<em>Grus grus</em>) in a spatio-temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. </p> <p>Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio-temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity.</p> <p>Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact-focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability, but can be a useful approach to improving insight gained from model outputs. </p>
Data for: "Dynamic species distribution modeling reveals the pivotal role of human-mediated long-distance dispersal in plant invasion"
<p>All the data needed to reproduce the results and Figures of our article:</p> <p>Botella, C., Bonnet, P., Hui, C., Joly, A., & Richardson, D. M. (2022). Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion. <em>Biology</em>, <em>11</em>(9), 1293. <a href="https://doi.org/10.3390/biology11091293">https://doi.org/10.3390/biology11091293</a></p> <p>Please, find the R scripts and guidelines to reproduce our results on the article's Github repository :</p> <p><a href="https://github.com/ChrisBotella/plectranthus_barbatus/tree/main">https://github.com/ChrisBotella/plectranthus_barbatus/tree/main</a></p>
FIGURE 1 in Comparisons of two cryptic Ampedus species (Coleoptera: Elateridae) by using classical systematics, ecological niche modeling, and DNA barcoding
FIGURE 1. Habitus photos and aedeagi drawings of examined species. A-B. Ampedus platiai, C-D. A. samedovi, E-F. A. pomonae (Aedeagi of A. platiai and A. samedovi are redrawn from Kabalak 2010 and aedeagus of A. pomonae is redrawn from Platia 1994.). BML: Basal struts of median lobe, BP: Basal piece, ML: Median Lobe, PDT: Paramere distal tooth, PR: Paramere.
ScienceDex guides
Understand access before you commit
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.