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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).

opencc-by-4.0Jan 2021View details →

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).

opencc-zeroMar 2022View details →

Habitats as predictors in species distribution models: Shall we use continuous or binary data?

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.

opencc-by-4.0Jan 2021View details →

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).

opencc-by-4.0Oct 2017View details →

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.

opencc-zeroApr 2022View details →

Factors influencing transferability in species distribution models

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 (MTI). 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.

opencc-zeroJul 2022View details →

Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models

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. Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane (Grus grus) 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. 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. 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.

opencc-by-4.0Mar 2022View details →

Data for: "Dynamic species distribution modeling reveals the pivotal role of human-mediated long-distance dispersal in plant invasion"

All the data needed to reproduce the results and Figures of our article: 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. Biology, 11(9), 1293. <a href="https://doi.org/10.3390/biology11091293">https://doi.org/10.3390/biology11091293</a> Please, find the R scripts and guidelines to reproduce our results on the article's Github repository : <a href="https://github.com/ChrisBotella/plectranthus_barbatus/tree/main">https://github.com/ChrisBotella/plectranthus_barbatus/tree/main</a>

opencc-zeroAug 2022View details →

Spatial confounding in Bayesian species distribution modeling

<ol> <li>Species distribution models (SDMs) are currently the main tools to derive species niche estimates and spatially explicit predictions for species geographical distribution. However, unobserved environmental conditions and ecological processes may confound the model estimates if they have a direct impact on the species and, at the same time, they are correlated with the observed environmental covariates. This, so-called spatial confounding, is a general property of spatial models but it has not been studied in the context of SDMs before.</li> <li>Here we examine how the estimation accuracy of SDMs depends on the type of spatial confounding. We construct two simulation studies where we alter spatial structures of the observed and unobserved covariates and the level of dependence between them. We fit generalized linear models with and without spatial random effects applying Bayesian inference and record the bias induced to model estimates by spatial confounding. After this, we examine spatial confounding also with real vegetation data from northern Norway.</li> <li>Our results show that model estimates for coarse-scale covariates, such as climate covariates, are likely to be biased if a species distribution depends also on an unobserved covariate operating on a finer spatial scale. Pushing higher probability for a relatively weak and spatially smoothly varying spatial random effect compared to the observed covariates improved estimation accuracy. The improvement was independent of the actual spatial structure of the unobserved covariate.</li> <li>Our study addresses the major factors of spatial confounding in SDMs and provides a list of recommendations for pre-inference assessment of spatial confounding and for inference-based methods to decrease the chance of biased model estimates.</li> </ol>

opencc-by-4.0Jan 2014View details →

Supplementary material 1 from: Motloung R, Robertson M, Rouget M, Wilson J (2014) Forestry trial data can be used to evaluate climate-based species distribution models in predicting tree invasions. NeoBiota 20: 31-48. https://doi.org/10.3897/neobiota.20.5778

Current and potential distributions of sixteen species that are not widespread in southern Africa arranged on the basis of their suitable range size : a) Acacia paradoxa, b) A. cultriformis, c) A. falciformis, d) A. pendula, e) A. rubida, f) A. stricta, g) A. retinodes, h) A. fimbriata, i) A. aneura, j) A. viscidula, k) A. acuminata, l) A. adunca, m) A. binervata, n) A. schinoides, o) A. prominens, p) A. mangium. The grey shading indicates areas that SDMs have identified as suitable by SDMs while the white ones are unsuitable.

opencc-by-4.0May 2024View details →

High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling - Datasets

Datasets and notebooks used in the publication High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling

figure 5 Lineage through time plot within G. subgutturosa with cytb. The 95 in Unraveling goitered gazelle (Gazella subgutturosa) diversification: insights from phylogeography and species distribution modeling

figure 5 Lineage through time plot within G. subgutturosa with cytb. The 95% highest posterior density interval is shown in blue.

figure 3 Mismatch distributions within the G in Unraveling goitered gazelle (Gazella subgutturosa) diversification: insights from phylogeography and species distribution modeling

figure 3 Mismatch distributions within the G. subgutturosa. The expected line (green color) compared with the observed frequencies under the sudden expansion model using cytb. (A) the mmd diagram for the Asiatic population shows a recent expansion. (B) the mmd diagram for the Middle Eastern population and (C) the mmd diagram for the Central Iranian population.

figure 8 Potential distribution modeling for G. subgutturosa across different time periods, including a in Unraveling goitered gazelle (Gazella subgutturosa) diversification: insights from phylogeography and species distribution modeling

figure 8 Potential distribution modeling for G. subgutturosa across different time periods, including a) the Last Glacial Maximum (lgm; 21 Kya) and b) mid-Holocene (6 kya) as past scenarios, c) the present as a current scenario, and future climatic projections for 2070 are based on specific climate models (d: bcc-csm 1, rcp: 4.5; e: bcc-csm1, rcp: 6; f: ccsm 4, rcp: 4.5; g: ccsm 4, rcp: 6.0). Habitat suitability is visualized using color gradients, with blue representing the highest suitability Downloaded from Brill.com 06/21/2024 06:25:06PM and green representing the via lowestOpensuitability Access..This The is presence an openof access article distributed under the terms G. subgutturosa is denoted by a red dot. of the CC BY 4.0 license. https://creativecommons.org/licenses/by/4.0/

figure 2 The dated phylogenetic trees using the cytb gene for G. subgutturosa. Blue bars show 95 in Unraveling goitered gazelle (Gazella subgutturosa) diversification: insights from phylogeography and species distribution modeling

figure 2 The dated phylogenetic trees using the cytb gene for G. subgutturosa. Blue bars show 95% highest posterior density intervals of the estimated node ages; numbers next to the nodes are mean node ages (Mya). The red and green lines show new haplotypes from this study.