Find research datasets worth reusing

Data from: Influence of device accuracy and choice of algorithm for species distribution modelling of seabirds: a case study using black-browed albatrosses

Species distribution models (SDM) based on tracking data from different devices are used increasingly to explain and predict seabird distributions. However, different tracking methods provide different data resolutions, ranging from < 10m to >100km. To better understand the implications of this variation, we modeled the potential distribution of black-browed albatrosses Thalassarche melanophris from South Georgia that were simultaneously equipped with a Platform Terminal Transmitter (PTT) (high resolution) and a Global Location Sensor (GLS) logger (coarse resolution), and measured the overlap of the respective potential distribution for a total of nine different SDM algorithms. We found slightly better model fits for the PTT than for GLS data (AUC values 0.958±0.048 vs. 0.95±0.05) across all algorithms. The overlaps of the predicted distributions were higher between device types for the same algorithm, than among algorithms for either device type. Uncertainty arising from coarse-resolution location data is therefore lower than that associated with the modeling technique. Consequently, the choice of an appropriate algorithm appears to be more important than device type when applying SDMs to seabird tracking data. Despite their low accuracy, GLS data appear to be effective for analyzing the habitat preferences and distribution patterns of pelagic species.

Data from: On the use of climate covariates in aquatic species distribution models: are we at risk of throwing the baby out?

Species distribution models (SDMs) in river ecosystems can incorporate climate information by using air temperature and precipitation as surrogate measures of instream conditions or by using independent models of water temperature and hydrology to link climate to instream habitat. The latter approach is preferable but constrained by the logistical burden of developing water temperature and hydrology models. We therefore assessed whether regional scale, freshwater SDM predictions are fundamentally different when climate data versus instream temperature and hydrology are used as covariates. Maximum Entropy (MaxEnt) SDMs were built for 15 freshwater fishes using one of two covariate sets: (1) air temperature and precipitation (climate variables) in combination with physical habitat variables; or (2) water temperature, hydrology (instream variables) and physical habitat. Three procedures were then used to compare results from climate vs. instream models. First, equivalence tests assessed average pairwise differences (site-specific comparisons throughout each species' range) among climate and instream models. Second, 'congruence' tests determined how often the same stream segments were assigned high habitat suitability by climate and instream models. Third, Schoener's D and Warren's I niche overlap statistics quantified range-wide similarity in predicted habitat suitability values from climate vs. instream models. Equivalence tests revealed small, pairwise differences in habitat suitability between climate and instream models (mean pairwise differences in MaxEnt raw scores for all species < 3×10-4). Congruence tests showed a strong tendency for climate and instream models to predict high habitat suitability at the same stream segments (median congruence = 68%). D and I statistics reflected a high margin of overlap among climate and instream models (median D = 0.78, median I = 0.96). Overall, we found little support for the hypothesis that SDM predictions are fundamentally different when climate versus instream covariates are used to model fish species' distributions at the scale of the Columbia Basin.

opencc-zeroDec 2017View details →

Data from: Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM)

A primary goal of ecology is to understand the fundamental processes underlying the geographic distributions of species. Two major strands of ecology – habitat modelling and community ecology – approach this problem differently. Habitat modellers often use species distribution models (SDMs) to quantify the relationship between species' and their environments without considering potential biotic interactions. Community ecologists, on the other hand, tend to focus on biotic interactions and, in observational studies, use co‐occurrence patterns to identify ecological processes. Here, we describe a joint species distribution model (JSDM) that integrates these distinct observational approaches by incorporating species co‐occurrence data into a SDM. JSDMs estimate distributions of multiple species simultaneously and allow decomposition of species co‐occurrence patterns into components describing shared environmental responses and residual patterns of co‐occurrence. We provide a general description of the model, a tutorial and code for fitting the model in R. We demonstrate this modelling approach using two case studies: frogs and eucalypt trees in Victoria, Australia. Overall, shared environmental correlations were stronger than residual correlations for both frogs and eucalypts, but there were cases of strong residual correlation. Frog species generally had positive residual correlations, possibly due to the fact these species occurred in similar habitats that were not fully described by the environmental variables included in the JSDM. Eucalypt species that interbreed had similar environmental responses but had negative residual co‐occurrence. One explanation is that interbreeding species may not form stable assemblages despite having similar environmental affinities. Environmental and residual correlations estimated from JSDMs can help indicate whether co‐occurrence is driven by shared environmental responses or other ecological or evolutionary process (e.g. biotic interactions), or if important predictor variables are missing. JSDMs take into account the fact that distributions of species might be related to each other and thus overcome a major limitation of modelling species distributions independently.

opencc-zeroDec 2017View details →

Data from: Input matters matter: bioclimatic consistency to map more reliable species distribution models

1. Accuracy of global bioclimatic databases is essential to understand biodiversity-environment relationships. Many studies have explored biases and uncertainties related to species distribution models (SDMs) but the effect of choosing a specific database among the different alternatives has not been previously assessed. 2. The lack of bioclimatic congruence (degree of agreement) between different databases is a main concern in distribution modelling and it is critical in single-source models, for which the database choice is decisive. In order to prevent unreliable predictions derived from distorted input data, SDMs accuracy can be assessed by mapping model predictions according to a bioclimatic congruence measure derived from the comparison of multiple databases, which can be achieved with the bioclimatic consistency maps that we propose in this study. Here, i) we present the first global-scale bioclimatic congruence map to analyse environmental mismatches between recently updated bioclimatic databases. We also test the importance of input matters on the reliability of distribution models of sixteen mammals, by addressing ii) inconsistencies among species response curves (temperature and precipitation), and iii) discrepancies among SDMs predictions depending on the chosen bioclimatic database. Finally, iv) we propose a strategy to assess bioclimatic consistency of model predictions, showing its application to the specific case of Litocranius walleri. 3. Our results confirm that the single-source modelling approach greatly influences the estimation of species-environment relationship and consequently, bias spatial predictions derived from SDMs. This is especially true for studies conducted in polar and mountainous regions which showed the smallest bioclimatic congruence. We show that by adding bioclimatic congruence to SDMs projections, we can build a bioclimatic consistency map that enables the detection of both risky and consistent areas, as revealed for the case of L. walleri. 4. Assessing uncertainty in bioclimatic input data is key to avoid erroneous conclusions in macroecological and biogeographical studies. The spatial characterisation of bioclimatic consistency provides an adequate empirical framework which effectively illustrates bioclimatic data limitations. We strongly recommend that this new strategy should be formally and systematically incorporated into distribution modelling to build more reliable SDMs, which are essential to develop successful biodiversity conservation programmes.

opencc-zeroDec 2018View details →

Data from: Testing species assemblage predictions from stacked and joint species distribution models

Aim: Predicting the spatial distribution of species assemblages remains an important challenge in biogeography. Recently, it has been proposed to extend correlative species distribution models (SDMs) by taking into account (a) covariance between species occurrences in so-called joint species distribution models (JSDMs) and (b) ecological assembly rules within the SESAM (spatially explicit species assemblage modelling) framework. Yet, little guidance exists on how these approaches could be combined. We, thus, aim to compare the accuracy of assemblage predictions derived from stacked and from joint SDMs. Location: Switzerland Taxon Birds, tree species Methods: Based on two monitoring schemes (national forest inventory and Swiss breeding bird atlas), we built SDMs and JSDMs for tree species (at 100m resolution) and forest birds (at 1km resolution). We tested accuracy of species assemblage and richness predictions on holdout data using different stacking procedures and ecological assembly rules. Results Despite minor differences, results were consistent between birds and tree species. Cross-validated species-level model performance was generally higher in SDMs than JSDMs. Differences in species richness and assemblage predictions were larger between stacking procedures and ecological assembly rules than between stacked SDMs and JSDMs. On average, predictions were slightly better for stacked SDMs compared to JSDMs, probabilistic stacks outperformed binary stacks, and ecological assembly rules yielded best predictions. Main conclusions: When predicting the composition of species assemblages, the choice of stacking procedure and ecological assembly rule seems more decisive than differences in underlying model type (SDM vs. JSDM). JSDMs do not seem to improve community predictions compared to SDMs or improve predictions for rare species. Still, JSDMs may provide additional insights into community assembly and may help deriving hypotheses about prevailing biotic interactions in the system. We provide simple rules of thumb for choosing appropriate modelling pathways. Future studies should test these preliminary guidelines for other taxa and biogeographic realms as well as for other JSDM algorithms.

opencc-zeroDec 2011View details →

Data from: Distribution models and a dated phylogeny for Chilean Oxalis species reveal occupation of new habitats by different lineages, not rapid adaptive radiation

Among the World's most challenging environments for plant life is the Atacama Desert, an arid zone extending over 1300 km and from sea level to 2000/3000 m along the southwestern Andean foothills. Plants there and in the adjacent Mediterranean zone exhibit striking adaptations, and we here use a species-rich such group to address the question whether adaptations arose in parallel, at different times, or simultaneously. Answering this type of question has been a major concern of evolutionary biology over the past few years, with a growing consensus that lineages tend to be conservative in their vegetative traits and niche requirements. Combined nuclear and chloroplast DNA sequences for 112 species of Oxalidales (4900 aligned nucleotides) yielded a fossil-calibrated phylogeny that includes 43 of the 54 species of Oxalis occurring in Chile. Distribution models (SDMs) for these species that included precipitation, temperature, fog and/or vegetation types and the phylogeny were used to reconstruct ancestral habitat preferences, relying on likelihood and Bayesian techniques. Since uneven collecting can reduce the power of SDMs, we used a background sample from 1224 Chilean Oxalis collections to correct models for collecting effort. Models with just 10 of 19 bioclim parameters did as well as more parameter-rich models. Results reveal that the Oxalis flora of Chile consists of seven distant lineages that originated at different times prior to the last Andean uplift pulse and some of which had features pre-adapting them to seasonally arid or xeric conditions. The Mediterranean core zone, south of the Atacama, offered an ecological refuge for insufficiently arid-adapted species and harbors a mix of ancient and young groups. There is no evidence of rapid adaptive radiation.

opencc-zeroDec 2013View details →

Data from: Phylogeography in continuous space: coupling species distribution models and circuit theory to assess the effect of contiguous migration at different climatic periods on genetic differentiation in Busseola fusca (Lepidoptera: Noctuidae)

Current population genetic models fail to cope with genetic differentiation for species with large, contiguous and heterogeneous distribution. We show that in such a case, genetic differentiation can be predicted at equilibrium by circuit theory, where conductance corresponds to abundance in species distribution models (SDM). Circuit-SDM approach was used for the phylogeographic study of the lepidopteran cereal stemborer Busseola fusca Füller (Noctuidae) across sub-Saharan Africa. Species abundance was surveyed across its distribution range. SDM models were optimized and selected by cross validation. Relationship between observed matrices of genetic differentiation between individuals, and matrices of resistance distance was assessed through Mantel tests and redundancy discriminant analyses (RDA). A total of 628 individuals from 130 localities in 17 countries were genotyped at 7 microsatellite loci. Six population clusters were found based on a Bayesian analysis. The eastern margin of Dahomey Gap between East and West Africa was the main factor of genetic differentiation. The SDM projections at present, last interglacial and last glacial maximum periods were used for estimation of circuit resistance between locations of genotyped individuals. For all periods of time, when using either all individuals or only East-African individuals, partial Mantel r and RDA analyses conditioning on geographic distance were found significant. Under future projections (year 2080), partial r and RDA significance were different. From this study, it is concluded that analytical solutions provided by circuit theory are useful for the evolutionary management of populations and for phylogeographic analysis when coalescence times are not accessible by approximate Bayesian simulations.

opencc-zeroDec 2015View details →

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.

opencc-zeroDec 2013View details →

Data from: Historical species distribution models predict species limits in western Plethodon salamanders

Allopatry is commonly used to predict boundaries in species delimitation investigations under the assumption that currently allopatric distributions are indicative of reproductive isolation; however, species ranges are known to change over time. Incorporating a temporal perspective of geographic distributions should improve species delimitation; to explore this, we investigate three species of western Plethodon salamanders that have shifted their ranges since the end of the Pleistocene. We generate species distribution models (SDM) of the current range, hindcast these models onto a climatic model 21 Ka, and use three molecular approaches to delimit species in an integrated fashion. In contrast to expectations based on the current distribution, we detect no independent lineages in species with allopatric and patchy distributions (Plethodon vandykei and Plethodon larselli). The SDMs indicate that probable habitat is more expansive than their current range, especially during the last glacial maximum (LGM) (21 Ka). However, with a contiguous distribution, two independent lineages were detected in Plethodon idahoensis, possibly due to isolation in multiple glacial refugia. Results indicate that historical SDMs are a better predictor of species boundaries than current distributions, and strongly imply that researchers should incorporate SDM and hindcasting into their investigations and the development of species hypotheses.

opencc-zeroDec 2017View details →

Data from: Comparing the prediction of joint species distribution models with respect to characteristics of sampling data

Biotic interactions have been rarely included in traditional species distribution models, wherein Joint Species Distribution Models (JSDMs) emerge as a feasible approach to incorporate environmental factors and interspecific interactions simultaneously, making it a powerful tool for analyzing the structure and assembly processes of biotic communities. However, the predictability and statistical robustness of JSDMs are largely unknown because of the lack of research efforts for those newly developed models. This study systematically evaluated the performances of five JSDMs in predicting the occurrence and biomass of multiple species, with a particular focus on diverse characteristics of sampling data, including type of response variables, number of sampling sites, and the number of species included in models. In general, most models yielded satisfactory performances on fitting to observed data and on the estimation of environmental effects; however, they showed less well performances in evaluating species associations, and their predictability had large variations. The JSDMs showed inconsistent performances between the goodness-of-fit and predictability in cross-validation, and the Boral model was relatively robust than others. The predictability of JSDMs was less influenced by sample sizes and substantially improved by incorporating rare species. This study contributes to an appropriate model selection and application of JSDMs.

Data from: Incorporating interspecific competition into species-distribution mapping by upward scaling of small-scale model projections to the landscape

There are a number of overarching questions and debate in the scientific community concerning the importance of biotic interactions in species distribution models at large spatial scales. In this paper, we present a framework for revising the potential distribution of tree species native to the Western Ecoregion of Nova Scotia, Canada, by integrating the long-term effects of interspecific competition into an existing abiotic-factor-based definition of potential species distribution (PSD). The PSD model is developed by combining spatially explicit data of individualistic species' response to normalized incident photosynthetically active radiation, soil water content, and growing degree days. A revised PSD model adds biomass output simulated over a 100-year timeframe with a robust forest gap model and scaled up to the landscape using a forestland classification technique. To demonstrate the method, we applied the calculation to the natural range of 16 target tree species as found in 1,240 provincial forest-inventory plots. The revised PSD model, with the long-term effects of interspecific competition accounted for, predicted that eastern hemlock (Tsuga canadensis), American beech (Fagus grandifolia), white birch (Betula papyrifera), red oak (Quercus rubra), sugar maple (Acer saccharum), and trembling aspen (Populus tremuloides) would experience a significant decline in their original distribution compared with balsam fir (Abies balsamea), black spruce (Picea mariana), red spruce (Picea rubens), red maple (Acer rubrum L.), and yellow birch (Betula alleghaniensis). True model accuracy improved from 64.2% with original PSD evaluations to 81.7% with revised PSD. Kappa statistics slightly increased from 0.26 (fair) to 0.41 (moderate) for original and revised PSDs, respectively.

Data from: Importance of antecedent environmental conditions in modeling species distributions

Although species distributions can change in an unexpectedly short period of time, most species distribution models (SDMs) use only long-term averaged environmental conditions to explain species distributions. We aimed to demonstrate the importance of incorporating antecedent environmental conditions into SDMs in comparison to long-term averaged environmental conditions. We modeled the presence/absence of 18 fish species captured across 108 sampling events along a 50-km length of the Sagami River in Japan throughout the 1990s (one to four times per site at 45 sites). We constructed and compared the two types of SDMs: (1) a conventional model that uses only long-term averaged (10-year) environmental conditions; and (2) a proposed model that incorporates environmental conditions 2 years prior to a sampling event (antecedent conditions) together with long-term averages linked to life-history stages. These models both included geomorphological, hydrological, and sampling conditions as predictors. A random forest algorithm was applied for modeling and quantifying the relative importance of the predictors. For seven species, antecedent hydrological conditions were more important than the long-term averaged hydrological conditions. Furthermore, the distributions of two species with low prevalence could not be predicted using long-term averaged hydrological conditions but only using antecedent hydrological conditions. In conclusion, incorporating antecedent environmental factors linked with life-history stages at appropriate time scales can better explain changes in species distribution through time.

opencc-zeroDec 2012View details →

Data from: Correction of location errors for presence-only species distribution models

1. Species distribution models (SDMs) for presence-only data depend on accurate and precise measurements of geographic and environmental covariates that influence presence and abundance of the species. Some data sets, however, may contain both systematic and random errors in the recorded location of the species. Environmental covariates at the recorded location may differ from those at the true location and result in biased parameter estimates and predictions from SDMs. 2. Regression calibration is a well-developed statistical method that can be used to correct the bias in estimated coefficients and predictions from SDMs when the recorded geographic location differ from the true location for some, but not all locations. We expand the application of regression calibration methods to SDMs and provide illustrative examples using simulated data and opportunistic records of whooping cranes (Grus americana). 3. We found we were able to successfully correct the bias in our SDM parameters estimated from simulated data and opportunistic records of whooping cranes using regression calibration. 4. When modeling species distributions with data that have geographic location errors, we recommend researchers consider the effect of location errors. Correcting for location errors requires that at least a portion of the data have locations recorded without error. Bias correction can result in an increase in variance; this increase in variance should be considered when evaluating the utility of bias correction.

opencc-zeroJun 2019View details →

Data from: Bunching up the background betters bias in species distribution models

Sets of presence records used to model species' distributions typically consist of observations collected opportunistically rather than systematically. As a result, sampling probability is geographically uneven, which may confound the model's characterization of the species' distribution. Modelers frequently address sampling bias by manipulating training data: either subsampling presence data or creating a similar spatial bias in non-presence background data. We tested a new method, which we call "background thickening," in the latter category. Background thickening entails concentrating background locations around presence locations in proportion to presence location density. We compared background thickening to two established sampling bias correction methods — target group background selection and presence thinning — using simulated data and data from a case study. In the case study, background thickening and presence thinning performed similarly well, both producing better model discrimination than target group background selection, and better model calibration than models without correction. In the simulation, background thickening performed better than presence thinning when the number of simulated presence locations was low, and vice versa. We discuss drawbacks to target group background selection, why background thickening and presence thinning are conservative but robust sampling bias correction methods, and why background thickening is better than presence thinning for small sample sizes. Particularly, background thickening is advantageous for treating sampling bias when data are scarce because it avoids discarding presence records.

opencc-zeroDec 2013View details →

Data from: Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy

Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.

Data from: Using citizen science monitoring data in species distribution models to inform isotopic assignment of migratory connectivity in wetland birds

Stable isotopes have been used to estimate migratory connectivity in many species. Estimates are often greatly improved when coupled with species distribution models (SDMs), which temper estimates in relation to occurrence. SDMs can be constructed using from point locality data from a variety of sources including extensive monitoring data typically collected by citizen scientists. However, one potential issue with SDM is that these data oven have sampling bias. To avoid this potential bias, an approach using SDMs based on marsh bird monitoring program data collected by citizen scientists and other participants following protocols specifically designed to maximize detections of species of interest at locations representative of the species range. We then used the SDMs to refine isotopic assignments of breeding areas of autumn-migrating and wintering Sora (Porzana carolina), Virginia Rails (Rallus limicola), and Yellow Rails (Coturnicops noveboracensis) based on feathers collected from individuals caught at various locations in the United States from Minnesota south to Louisiana and South Carolina. Sora were assigned to an area that included much of the western U.S. and prairie Canada, covering parts of the Pacific, Central, and Mississippi Flyways. Yellow Rails were assigned to a broad area along Hudson and James Bay in northern Manitoba and Ontario, as well as smaller parts of Quebec, Minnesota, Wisconsin, and Michigan, including parts of the Mississippi and Atlantic Flyways. Virginia Rails were from several discrete areas, including parts of Colorado, New Mexico, the central valley of California, and southern Saskatchewan and Manitoba in the Pacific and Central Flyways. Our study demonstrates extensive data from organized citizen science monitoring programs are especially useful for improving isotopic assignments of migratory connectivity in birds, which can ultimately lead to better informed management decisions and conservation actions.

opennotspecifiedDec 2010View details →

FIGURE 3 in Molecules meet macroecology—combining Species Distribution Models and phylogeographic studies

FIGURE 3. Potential distribution of Arthroleptis xenodactyloides under current climatic and two proposed Last Glacial Maximum palaeoclimatic scenarios (CCSM, MIROC) showing mean values obtained from 10 models computed with randomly selected 30 % of the 46 records (black dots) for model evaluation and the remaining 70 % for model training and corresponding standard deviations (SD).

opennotspecifiedDec 2010View details →

FIGURE 2 in Molecules meet macroecology—combining Species Distribution Models and phylogeographic studies

FIGURE 2. (A) Model performance and (B) presence/absence thresholds obtained from 10 models computed with randomly selected 30 % of the 46 records for model evaluation and the remaining 70 % for model training; as Maxent values the logistic model output is chosen.

opennotspecifiedDec 2010View details →

FIGURE 1 in Molecules meet macroecology—combining Species Distribution Models and phylogeographic studies

FIGURE 1. Elevation map of part of coastal eastern Africa showing records of Arthroleptis xenodactyloides (dots) processed in this study; those of Blackburn & Measey (2009) are indicated by name.