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25 results for “integrated species distribution models”
Data from: Integrated species distribution models to account for sampling biases and improve range wide occurrence predictions
<p><strong><span>Aim</span></strong></p> <p><span>Species distribution models (SDMs) that integrate presence-only and presence-absence data offer a promising avenue to improve information on species' geographic distributions. The use of such 'integrated SDMs' on a species range-wide extent has been constrained by the often-limited presence-absence data and by the heterogeneous sampling of the presence-only data. Here, we evaluate integrated SDMs for studying species ranges with a novel expert range map-based evaluation. We build a new understanding about how integrated SDMs address issues of estimation accuracy and data deficiency and thereby offer advantages over traditional SDMs.</span></p> <p><strong><span>Location</span></strong></p> <p><span>South and Central America.</span></p> <p><strong><span>Time period</span></strong></p> <p><span>1979-2017.</span></p> <p><strong><span>Major taxa studied</span></strong></p> <p><span>Hummingbirds.</span></p> <p><strong><span>Methods</span></strong></p> <p><span>We build integrated SDMs by linking two observation models – one for each data type – to the same underlying spatial process.</span> <span>We validate SDMs with two schemes: i) cross-validation with presence-absence data and ii) comparison with respect to the species' whole range as defined with IUCN range maps. We also compare models relative to the estimated response curves and compute the association between the benefit of the data integration and the number of presence records in each data set.</span></p> <p><strong><span>Results</span></strong></p> <p><span>The integrated SDM accounting for the spatially varying sampling intensity of the presence-only data was one of the top-performing models in both model validation schemes. Presence-only data alleviated overly large niche estimates, and data integration was beneficial compared to modelling solely presence-only data for species that had few presence points when predicting the species' whole range. On the community level, integrated models improved the species richness prediction.</span></p> <p><strong><span>Main conclusions</span></strong></p> <p><span>Integrated SDMs combining presence-only and presence-absence data are successfully able to borrow strengths from both data types and offer improved predictions of species' ranges. Integrated SDMs can potentially alleviate the impacts of taxonomically and geographically uneven sampling and to leverage the detailed sampling information in presence-absence data.</span></p>
Resources for: Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts
<p><strong>Aim</strong>: Climate change and habitat loss or degradation are some of the greatest threats that species face today, often resulting in range shifts. Species traits have been discussed as important predictors of range shifts, with the identification of general trends being of great interest for conservation efforts. However, studies reviewing relationships between traits and range shifts have questioned the existence of such generalized trends, due to mixed results and weak correlations, as well as analytical shortcomings. The aim of this study was to test this relationship empirically, using analytical approaches that account for common sources of bias when assessing range trends.<br><strong>Location</strong>: Tanzania, East Africa.<br><strong>Time period</strong>: 1980-1999 and 2000-2020.<br><strong>Major taxa studied</strong>: 57 savannah specialist birds found in Tanzania, belonging to 26 families and 11 orders.<br><strong>Methods</strong>: We applied recently developed integrated spatio-temporal species distribution models in R-INLA, combining citizen science and bird atlas data to estimate ranges of species, quantify range shifts, and test the predictive power of traditional trait groups, as well as exposure-related and sensitivity traits. We based our study on 40 years of bird observations in East African savannahs, a biome that has experienced increasing climatic and non-climatic pressures over recent decades. We correlated patterns of change with species traits.<br><strong>Results</strong>: We find indications of relationships identified by previous research, but low average explanatory power of traits from an ecological perspective, confirming the lack of meaningful general associations. However, our analysis finds compelling species-specific results.<br><strong>Main conclusions</strong>: We highlight the importance of individual assessments, while demonstrating the usefulness of our analytical approach for analyses of range shifts.</p>
Dataset for Integrated Species Distribution Model for pikeperch larvae in the Porvoo-Sipoo archipelago
<p>This record contains the data required to run the code for fitting the Integrated Species Distribution Model described in <a href="https://arxiv.org/abs/2206.08817">arXiv:2206.08817 [stat.ME].</a></p> <h1>Files in this record</h1> <ul> <li><strong>transect_data.csv</strong> Line transect observations from Porvoo-Sipoo archipelago, Finland on June 2017.</li> <li><strong>expert_assessments.tif</strong> Rasterized, anonymous expert assessments. Categorical values denoting how likely a given location is to be a spawning location for pikeperch. 4 categories, with smaller values corresponding to higher probabilities.</li> <li><strong>covariate_raster_example.tif</strong> Rasterized example environmental covariate values. These are similarly structured as the covariate data used in the study and compatible with the analysis code. However, since we do not have the permission to release the original data set, these values are instead generated based on the projected planar coordinates such that they have roughly similar spatial gradients as the original covariates.</li> </ul> <h1>Detailed descriptions</h1> <h2>Transect data</h2> <h3>Location and replicate identifiers</h3> <ul> <li> <p><strong>id</strong> : transect identifier. Replicates of the same transect have the same identifier.</p> </li> <li> <p><strong>id2</strong> : alternate transect identifier, unique for each transect.</p> </li> <li> <p><strong>repeated</strong> : whether transect was replicated or not.</p> </li> <li> <p><strong>X_euref</strong> : easting coordinate, EUREF_FIN_TM35FIN, for the transect starting location in [meters]</p> </li> <li> <p><strong>Y_euref</strong> : northing coordinate, EUREF_FIN_TM35FIN, for the transect starting location in [meters]</p> </li> <li><strong>date</strong> : date of the measurement, DD/MM/YYYY</li> <li><strong>week</strong> : week number of the measurement date</li> </ul> <h3>In situ measurements</h3> <ul> <li> <p><strong>volume</strong> : Transect water volume [m^3]. Transect length (500m) multiplied by sampler surface area. Used as survey effort.</p> </li> <li> <p><strong>heading</strong> : compass heading (direction) for the transect, in [degrees].</p> </li> <li> <p><strong>SumKUHA</strong> : total pikeperch (<em>Sander lucioperca</em>, kuha in Finnish) larvae count in each transect [scalar]</p> </li> </ul> <h2>Expert assessments</h2> <p>The raster contains assessments from 10 local experts encoded as separate raster layers (Expert_1, Expert_2, ..., Expert_10). Raster resolution is 50m x 50m and the planar coordinates are based on the same coordinate reference system as the transect observations (UTM zone 35).</p> <p>The assessments are coded as integers with values between 1 and 4, with smaller values corresponding to higher probabilities.</p> <h2>Covariate raster example</h2> <p>This raster has the same spatial dimensions and uses the same coordinate reference system as the expert assessment raster and has three layers, one for each covariate. The covariate values are generated based on the spatial coordinates such that each covariate has similar spatial gradient as the original covariate. The covarites have the same names as in the original covariate data (<strong>dptLUKE</strong>, <strong>dist10m</strong> and <strong>lined3km</strong>).</p> <h1>Creators</h1> <p>Transect data collected and curated by Sanna Kuningas.</p> <p>Original covariate rasters curated by Sanna Kuningas from data sets collected by the Finnish Environment Institute and the Natural Resources Institute Finland.</p> <p>Expert assessments originally digitized and rasterized by Jussi Mäkinen. Additional refinement to assessment rasters by Karel Kaurila.</p> <p>Preparation for publishing on Zenodo for all of the data sets by Karel Kaurila.</p> <h2>Change log</h2> <ul> <li> 2025 Jan 31: Included columns <strong>date</strong> and <strong>week</strong> for <strong>transect_data.csv</strong>.</li> </ul>
Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
<p><span>1. Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently-occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an <i>integrated SDM database (iSDMdb)</i>.</span></p> <p><span>2. The<i> iSDMdb</i> is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility<i>,</i> we used the endangered New Zealand sea lion (<i>Phocarctos hookeri</i>). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management. </span></p> <p><span>3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the <i>iSDMdb</i>'s additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected.</span></p> <p><span>4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The <i>iSDMdb</i> addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an <i>iSDMdb</i> using open-source tools, and how to make diverse, complex assessments more accessible for end-users.</span></p>
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Data from: Integrated species distribution models to account for sampling biases and improve range wide occurrence predictions
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Resources for: Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts
Open the record for dataset details and reuse information.
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
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Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
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Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance
<p>Using geospatial data of wildlife presence to predict a species distribution across a geographic area is among the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have access to larger amounts of low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually scarcer and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive presence-only datasets. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. Here, we apply this innovative approach to fit ISDMs to empirical data, using presence-absence and presence-only data for the three prevalent deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km<sup>2</sup> resolution across the island. Models' predictions were associated to spatial estimates of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we checked the performance of the three species-specific models using two datasets, independent deer hunting returns and deer densities based on faecal pellet counts. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unexplored and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.</p>
Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance
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The best of two worlds: using stacked generalisation for integrating expert range maps in species distribution models
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Integrating univariate niche dynamics in species distribution models: a step forward for marine research on biological invasions
<p>Aim The development of approaches to predict the distribution and potential expansion of invasive species is still an open challenge. Here our goal is to improve the modelling procedure for marine invaders by coupling Species Distribution Models (SDMs) with an analysis of their univariate niche dynamics. In particular, we tested for the first time whether choosing model predictors among the stable niche dimensions was effective in improving predictions of invasive species expansion.<br> Location Mediterranean Sea<br> Taxon Dusky spinefoot, Siganus luridus.<br> Methods We analysed the univariate niche dynamics for S. luridus across its native and invaded ranges, by applying a standardized framework that allowed the identification of cases of niche stability or shift. We compared inter-range transferability of SDMs fitted with different combinations of labile or stable predictors. Finally, we evaluated interactions in SDM settings (calibration area, model technique and predictors set) on models' predictive ability, using independent data from the most recent phase of invasion.<br> Results We detected a pattern of niche stability for several variables, especially salinity and bathymetry, which positively influenced model inter-ranges transferability: when the models calibrated in the native range include only stable niche axes, predictive ability is improved. We also identified a shift toward lower surface temperatures in the introduced range, which were almost never experienced by the species before invasion. The model calibrated within the combined ranges was the most ecologically congruent. Also, models calibrated in the invaded range allowed a correct prediction of range expansion, with the predicted suitable areas only slightly underestimated.<br> Main conclusions We provide the first evidence that using conserved predictors in SDMs improves inter-range projections of expanding invasive species. Variable selection, calibration area and modelling technique all matter when modelling invasive species, with important interaction effects. We provide guidelines on how to improve SDMs applications in biological invasion research.</p>
Data from: Tests of species-specific models reveal the importance of drought in postglacial range shifts of a Mediterranean-climate tree: insights from integrative distributional, demographic and coalescent modelling and ABC model selection
Past climate change has caused shifts in species distributions and undoubtedly impacted patterns of genetic variation, but the biological processes mediating responses to climate change, and their genetic signatures, are often poorly understood. We test six species-specific biologically informed hypotheses about such processes in canyon live oak (Quercus chrysolepis) from the California Floristic Province. These hypotheses encompass the potential roles of climatic niche, niche multidimensionality, physiological trade-offs in functional traits, and local-scale factors (microsites and local adaptation within ecoregions) in structuring genetic variation. Specifically, we use ecological niche models (ENMs) to construct temporally dynamic landscapes where the processes invoked by each hypothesis are reflected by differences in local habitat suitabilities. These landscapes are used to simulate expected patterns of genetic variation under each model and evaluate the fit of empirical data from 13 microsatellite loci genotyped in 226 individuals from across the species range. Using approximate Bayesian computation (ABC), we obtain very strong support for two statistically indistinguishable models: a trade-off model in which growth rate and drought tolerance drive habitat suitability and genetic structure, and a model based on the climatic niche estimated from a generic ENM, in which the variables found to make the most important contribution to the ENM have strong conceptual links to drought stress. The two most probable models for explaining the patterns of genetic variation thus share a common component, highlighting the potential importance of seasonal drought in driving historical range shifts in a temperate tree from a Mediterranean climate where summer drought is common.
Data from: Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation
<p class="MsoNormal">The ever-growing popularity of citizen science, as well as recent technological and digital developments, have allowed the collection of data on species' distributions at an extraordinary rate. In order to take advantage of these data, information of varying quantity and quality needs to be integrated. Point process models have been proposed as an elegant way to achieve this for estimates of species distributions. These models can be fitted efficiently using Bayesian methods based on integrated nested Laplace approximations (INLA) with stochastic partial differential equations (SPDE). This approach uses an efficient way to model spatial autocorrelation using a Gaussian random field and a triangular mesh over the spatial domain. The mesh is constructed by user-defined variables, so effectively represents a free parameter in the model. However, there is a lack of understanding about how to set these mesh parameters, and their effect on model performance. Here, we assess how mesh parameters affect predictions and model fit to estimate the distribution of the serotine bat, <em><span>Eptesicus serotinus</span></em><span>,<em> </em>in Great Britain. A Bayesian INLA model was fitted using five meshes of varying densities to a dataset comprising both structured observations from a national monitoring programme and opportunistic records. We demonstrate that mesh density impacted spatial predictions with a general loss of accuracy with increasing mesh coarseness</span>. However, we also show that the finest mesh was unable to overcome spatial biases in the data. In addition, the magnitude of the covariate effects differed markedly between meshes. This confirms that mesh parameterisation is an important and delicate process with implications for model inference. We discuss how species distribution modellers might adapt their use of INLA in light of these findings.</p>
Data from: Tests of species-specific models reveal the importance of drought in postglacial range shifts of a Mediterranean-climate tree: insights from integrative distributional, demographic and coalescent modelling and ABC model selection
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Data from: Genetic structure and post-glacial expansion of Cornus florida L. (Cornaceae): integrative evidence from phylogeography, population demographic history, and species distribution modeling
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Data from: Integrating soil properties into species distribution models enhances predictive accuracy for terricolous macrofungi
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Integrating univariate niche dynamics in species distribution models: a step forward for marine research on biological invasions
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Data from: Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation
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OpenNeuro
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