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figure 1 in Unraveling goitered gazelle (Gazella subgutturosa) diversification: insights from phylogeography and species distribution modeling
figure 1 Sampling locations of new specimens of G. subgutturosa from four locations in the present study. Parvar Protected Area, Sorkheh-Hesar National Park, Bashgol Protected Area, and Sohrein Protected Area. Hatched areas on the map indicate the provinces where each location is situated.
figure 6 in Unraveling goitered gazelle (Gazella subgutturosa) diversification: insights from phylogeography and species distribution modeling
figure 6 Median-joining haplotype network of G. subgutturosa using the cytb gene. The blue color Haplogroup refers to the Asiatic clade, the pink color Haplogroup is assigned to the Middle Eastern clade and the yellow color Haplogroup demonstrates the Central Iranian clade.
figure 7 in Unraveling goitered gazelle (Gazella subgutturosa) diversification: insights from phylogeography and species distribution modeling
figure 7 The biogeographic analysis of G. subgutturosa using s-diva (1) and bbm (2) based on cytb. For these analyses, three clades were considered: the Asiatic distribution (A), the Middle Eastern distribution (B), and the central Iranian distribution (C). The green and red circles around the nodes show vicariance and dispersal events, respectively.
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>
Fig. 5 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. 5 BEAST phylogenetic tree based on the COI sequences. Node values indicate divergence estimated in MYA
Fig. 7 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. 7 Areas of climatic stability over time periods from the LGM through the present, based on summed climatic suitability models for the LGM, mid-Holocene, and present day for three differed GCMs. Stability increase from red to yellow color. White-filled areas show the
РИС. 4. ROC-кривые и плоЩадь под ROC-кривой (AUC) для раЗных моделей. А. РеЗультаты для Xeropicta derbentina. B. РеЗультаты для Brephulopsis cylindrica. Красная кривая иллюстрирует покаЗатели модели для обучаюЩего набора, бледно-голубые линии — для тестовых наборов и отдельных моделей, толстая синяя линия — обЩие покаЗатели всех моделей. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 4. ROC-кривые и плоЩадь под ROC-кривой (AUC) для раЗных моделей. А. РеЗультаты для Xeropicta derbentina. B. РеЗультаты для Brephulopsis cylindrica. Красная кривая иллюстрирует покаЗатели модели для обучаюЩего набора, бледно-голубые линии — для тестовых наборов и отдельных моделей, толстая синяя линия — обЩие покаЗатели всех моделей.
FIG. 3 in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
FIG. 3. Principal component analysis (PCA). The dimensionality reduction technique represents multivariate data on the 2D plane thus showing their structure. Datapoint colors are the same as in Fig. 2.
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017]. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017].
Figure. The phylogenetic tree showing the relationship among Brevibacillus parabrevis strains SA2.2 and TJ2.3, Bacillus licheniformis MG4.2, and their phylogenetically closest type strains. The GenBank accession numbers of the type strains and studied strains are shown following species names. Distance matrix was calculated by Kimura's 2-parameter model. The scale bar indicates 0.02 substitutions per nucleotide position. Alicyclobacillus pohliae AJ564766 served as an out-group. in Distribution of extracellular enzyme-producing bacteria in the digestive tracts of 4 brackish water fish species
Figure. The phylogenetic tree showing the relationship among Brevibacillus parabrevis strains SA2.2 and TJ2.3, Bacillus licheniformis MG4.2, and their phylogenetically closest type strains. The GenBank accession numbers of the type strains and studied strains are shown following species names. Distance matrix was calculated by Kimura's 2-parameter model. The scale bar indicates 0.02 substitutions per nucleotide position. Alicyclobacillus pohliae AJ564766 served as an out-group.
Fig. 2 in Parasite species co-occurrence patterns on Peromyscus: Joint species distribution modelling
Fig. 2. Results of variance partitioning for variation in ectoparasite prevalence explained by fixed and random effects for each ectoparasite species (columns). Explained variance presented for the constrained model for deer mice (n = 229 individuals). DM, deer mice; RBV, southern red-backed vole; WJM, woodland jumping mouse; PA, population abundance. Population abundance of small mammal species measured as captures per 100 trap nights. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Species distribution and abundance modelling with dynamicSDM: a case study analysis of the red-billed quelea (Quelea quelea).
<p><strong>GBIF_all_aves_2000_2020.csv</strong><br> A dataset containing e-Bird sampling events for all bird species across southern Africa between 2000-2020 (Fink et al., 2021, GBIF, 2021). <br> <br> Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, O. Robinson, S. Ligocki, W. Hochachka, L. Jaromczyk, C. Wood, I. Davies, M. Iliff, L. Seitz. 2021. eBird Status and Trends, Data Version: 2020; Released: 2021. Cornell Lab of Ornithology, Ithaca, New York. \doi{10.2173/ebirdst.2020}<br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}</p> <p><strong>RBQ_full_analysis.R</strong></p> <p>An R script for the generation of dynamic species distribution and abundance models for nomadic bird, the red-billed quelea (<em>Quelea quelea</em>) using dynamicSDM package functions. </p> <p><strong>Unfiltered_quelea_occurrence.csv</strong><br> A dataset containing species occurrence and abundance records for the bird species, the red-billed quelea (<em>Quelea quelea</em>) between 1976-2021 (GBIF 2021 & GBIF 2022 & sources listed in Table 1). <br> <br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}<br> <br> GBIF.org (25 July 2022) GBIF Occurrence Download \doi{10.15468/dl.k2kftv}<br> </p> <p><strong>Table S1. </strong>Red-billed quelea (<em>Quelea quelea</em>) occurrence and abundance data sources.</p> <table align="left"> <tbody> <tr> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Sources</strong></p> </td> </tr> <tr> <td> <p><strong>Control operation </strong></p> </td> <td> <ul> <li>Information Core for Southern African Migrant Pests (ICOSAMP, 2001-2005).</li> <li>Centre for Overseas Pest Research (COPR), Natural History Museum, Tring.</li> <li>Botswana Ministries of Agriculture.</li> <li>Mozambique Ministry of Agriculture</li> </ul> </td> </tr> <tr> <td> <p><strong>Citizen science</strong></p> </td> <td> <ul> <li>Global Biodiversity Information Facility, including iNaturalist, eBird, South Africa Bird Atlas Project (SABAP) and South Africa Bird Ringing Unit (SAFRING) sources.</li> </ul> </td> </tr> <tr> <td> <p><strong>Independent research</strong></p> </td> <td> <ul> <li>EXCEL File "NfA-yearposRAC" (unpublished data set complied by R. A. Cheke, 2010).</li> </ul> </td> </tr> </tbody> </table>
Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance
<p>Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (<em>Prionace</em> <em>glauca</em>), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases. </p>
The dataset of Liquidambar orientalis for species distribution models
<p>The primary objective of this study was to predict the existing geographic range of <em>Liquidambar</em> <em>orientalis</em>, commonly known as the oriental sweetgum. To gain insights into the potential effects of climate change on the oriental sweetgum, the study employed species distribution models to project the model to future periods. Considering two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5), the ensemble modeling approach utilized the <em>biomod2</em> package in the R programming language to analyze the alterations in the spatial distribution of the species in forthcoming periods (namely, for the years 2035s, 2055s, and 2070s). </p>
Factors influencing transferability in species distribution models
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Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance
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The dataset of Liquidambar orientalis for species distribution models
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Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
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Habitats as predictors in species distribution models: Shall we use continuous or binary data?
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Using species distribution models and decision tools to direct surveys and identify potential translocation sites for a critically endangered species
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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.