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8,119 results for “species distribution”
Data from: Even low light pollution levels affect the spatial distribution and timing of activity of a "light tolerant" bat species
<p>Abstract of the related publication :</p> <p>By disrupting nocturnal landscapes worldwide, light pollution caused by Artificial Light At Night (ALAN) is recognised as a major threat to biodiversity. As even low light intensities might affect some taxa, concerns are arising about biological responses to widespread low light levels. We used data from a French citizen science bat monitoring program (1,894 full-nights monitored on 1,055 sites) to explore the landscape-scale effects of light on an open-space-foraging bat species, the Serotine bat (<em>Eptesicus serotinus</em>). We assessed this species' abundance and timing of night-time activity (median time of activity) at foraging sites. ALAN, and to a lesser extent moonlight, reduced <em>E. serotinus</em> abundance. ALAN delayed activity, and this delay was amplified during overcast nights. On the contrary, where there was no ALAN, the higher the cloud cover, the earlier the activity occurred. Cloud cover likely darkened the night sky in rural locations, whereas it amplified skyglow in light-polluted places, increasing ALAN effects on bats. Interestingly, moonlight also delayed activity but this effect was weakened where there was ALAN. Our study shows that even fine variations of light levels could affect the spatiotemporal distribution of a common species usually considered to be “light tolerant”, with potential cascading effects on individual fitness and population dynamics. It stresses how urgent it is to preserve and restore dark areas to protect biodiversity from light pollution while working on light intensity and directivity where ALAN is needed.</p>
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. 5. Partial dependence plot for terrain roughness index (tri).
Fig. 7 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. 7. Partial dependence plot for silt content (SLT).
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. 1 in The Amount And Distribution Of The Red Data Book Bird Wetland Species In The Azov-Black Sea Region Of Ukraine According To The Results Of August Counts 2004-2015
Fig. 1. Number of August Counts in the different wetlands of Azov-Black Sea coast of Ukraine.
Fig. 2 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. 2. Potential distribution of the East European vole (Microtus levis). Captions as in fig.1.
Fig. 2 in Distribution Of The Freshwater Snail Species Fagotia (Gastropoda, Melanopsidae) In Ukraine According To Climatic Factors. I. Fagotia Esperi
Fig. 2. Response curve of the mean temperature of the driest quater (bio 9).
Publication release: How well do species distribution models predict occurrences in exotic ranges?
<div class="record-description"> <p>Species distribution models (SDMs) are widely used predictive tools to forecast potential biological invasions. However, the reliability of SDMs extrapolated to exotic ranges remains understudied, with most analyses restricted to few species and equivocal results. We examined the spatial transferability of SDMs for 647 non-indigenous species extrapolated across 1,867 invaded ranges, and identify what factors may help differentiate predictive success from failure. We performed a large-scale assessment of the transferability of SDMs using two modelling approaches: generalized additive models (GAMs) and MaxEnt. We fitted SDMs on the native ranges of species and extrapolated them to exotic ranges. We examined the influence of general factors and factors related to biological invasions on spatial transferability.</p> <p>Here, we provide the code and data for publication in Global Ecology and Biogeography as part of Nguyen and Leung 2022 "How well do species distribution models predict occurrences in exotic ranges?". Provided are the files and scripts necessary to fit and validate the SDMs using distirbutional data from their native and exotic ranges, respectively, formulated as generalized additive models (GAMs) or MaxEnt models. Additionally, provided is a script to validate the SDMs on their native fitting range using 10-fold cross-validation, and to fit the transferability model, as a linear mixed model (LMM), with a provided cleaned data.frame. The dataset provided includes a full species list with GBIF occurrence records, target-group background (TGB) records to use with model fitting and validation, as well as environmental data associated with the sightings.</p> </div>
Marine ecoregions and subecoregions within Indo-West Australian waters: A statistical approach based on species distributions
<p>Aim: The Marine Ecoregions of the World (MEOW) system delineates the oceans into 232 ecoregions. Here, we aimed to evaluate the suitability of this system to represent species distributions within Indo-West Australian waters, explore alternative ecoregions and new subecoregions, and investigate environmental variables that are correlated with species distributions within those waters.</p> <p>Location: Indo-West Australia</p> <p>Taxa: Vertebrates, invertebrates, marine plants</p> <p>Methods: We downloaded occurrence data for 14,513 marine species from the Ocean Biogeographic Information System. We analysed differences in species composition among nine ecoregions within Indo-West Australian waters using pairwise permutational multivariate analysis of variance to evaluate how well the MEOW system represents species distributions within those waters. We delineated subecoregions within each distinct ecoregion using hierarchical cluster analysis with the unweighted pair-group method using arithmetic averages. We analysed relationships between environmental variables and species composition using distance-based linear models.</p> <p>Results: Species composition was significantly different among ecoregions, except for three adjacent regions, which were combined into a single large ecoregion. Hence, seven distinct ecoregions were further analysed. Our study identified 13 subecoregions within these ecoregions that each separate into 'inshore' and 'offshore' zones. Depth explained the most variation in species composition of the combined taxa and sea surface temperature was the most important parameter in explaining the variability in most taxa.</p> <p>Main conclusion: The MEOW system did not represent well the distribution of marine species within Indo-West Australian waters. Alternatively, we show that those waters encompass seven distinct ecoregions with 13 subecoregions. The main environmental drivers of species distributions could be depth and sea surface temperature. The proposed ecoregions and subecoregions allow us to improve the biogeographic hypotheses for understanding the evolution of marine species and identify representative marine habitats and species composition for the setting of Marine Protected Area networks within Indo-West Australian waters.</p>
Soil chemical variables improve models of understory plant species distributions
<div class="page"> <div class="section"> <div class="layoutArea"> <div class="column"><strong>Aim</strong></div> <div class="column">To determine the importance of soil variables relative to more commonly used topo-climatic or remotely sensed variables in species distribution models (SDMs) for understory plants.</div> <div class="column"> </div> <div class="column"><strong>Location</strong></div> <div class="column">White Mountain National Forest, New Hampshire, U.S.A.</div> <div class="column"> </div> <div class="column"><strong>Methods</strong></div> <div class="column">We fit models for presence of 41 forest understory plant species across 158 plots using soil, topographic, and spectral predictors to determine the relative contribution of different predictor types. We determined (a) if the potential importance of soil variables is greater than generally described in SDM literature, (b) which predictors are most important, and (c) if a standard subset of predictors can be used to effectively model all species.</div> <div class="column"> </div> <div class="column"><strong>Results</strong></div> <div class="column">Models containing all three predictor types performed best. Soil and topographic variables had comparable importance; spectral variables were of lesser importance. The best predictor variable was B horizon carbon to nitrogen ratio (B C:N), followed by topographic position index, elevation, and B horizon exchangeable calcium (B Ca). No standard subset effectively modeled all species.</div> <div class="column"> </div> <div class="column"><strong>Main conclusions</strong></div> <div class="column"> Our results and those of other SDMs that include in-situ soil geochemical data suggest that soil variables are increasingly important with more detailed descriptions of soils. Soil fertility data, such as B C:N and B Ca, are particularly important in acidic, forest soils where pH is a poor indicator of fertility. Commonly used topo-climatic variables provide meaningful predictions but are limited by their use of indirect predictor variables, inhibiting transferability and interpretability. The poor performance of models created using standard subsets of variables highlights the uniqueness of each species' niche and the need to combine flexible model building techniques with a variety of predictor variables.</div> </div> </div> </div>
Fig. 1 in Review of the genus Caucaseuma Strasser, 1970, with the description of a new cavernicolous species from the Western Caucasus and an updated key and distribution (Diplopoda, Chordeumatida, Anthroleucosomatidae)
Fig. 1. Distribution of the genus Caucaseuma Strasser, 1970.
Climate change threatens the distribution of major woody species and ecosystem services provision in southern Africa
<p>Species occurrence points used in this study to investigate the effect of climate change on the distribution of eight tree species in 18 countries in southern Africa, covering 36% of the continent. We proposed a loser/winner ranking of the species based on the changes in climatic suitability within their historical distributions as well as future gains and losses of suitable areas. We interpreted these findings in terms of changes in the provision of key ES (timber, food, energy, and medicine), and identified hotspots of ES provision decline. We used species presence data from the Global Biodiversity Information Facility, climatic data from the AfriClim dataset, and the MaXent algorithm to project the changes in species' land suitability. </p>
Failed despots and the equitable distribution of fitness in a subsidized species
<p>Territorial species are often predicted to adhere to an ideal despotic distribution and under-match local food resources, meaning that individuals in high-quality habitat achieve higher fitness than those in low-quality habitat. However, conditions such as high density, territory compression, and frequent territorial disputes in high-quality habitat are expected to cause habitat quality to decline as population density increases and, instead, promote resource matching. We studied a highly human-subsidized and under-matched population of Steller's jays (Cyanocitta stelleri) to determine how under-matching is maintained despite high densities, compressed territories, and frequent agonistic behaviors, which should promote resource matching. We examined the distribution of fitness among individuals in high-quality, subsidized habitat, by categorizing jays into dominance classes and characterizing individual consumption of human food, body condition, fecundity, and core area size and spatial distribution. Individuals of all dominance classes consumed similar amounts of human food and had similar body condition and fecundity. However, the most dominant individuals maintained smaller core areas that had greater overlap with subsidized habitat than those of subordinates. Thus, we found that 1) jays attain high densities in subsidized areas because dominant individuals do not exclude subordinates from human food subsidies and 2) jay densities do not reach the level necessary to facilitate resource matching because dominant individuals monopolize space in subsidized areas. Our results suggest that human-modified landscapes may decouple dominance from fitness and that incomplete exclusion of subordinates may be a common mechanism underpinning high densities and creating source populations of synanthropic species in subsidized environments.</p>
Positional errors in species distribution modelling are not overcome by the coarser grains of analysis
<p>The performance of species distribution models is known to be affected by the analysis grain and the positional error of species occurrences. Coarsening of the spatial analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in predictive models developed for conservation and climate change studies it is increasingly important to test this assumption. Species distribution models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate species occurrences might easier end up in unsuitable environment, which can result in inappropriate conservation actions.</p> <p>Here, we examine the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated virtual species using tree canopy height, topography wetness index, and altitude derived from LiDAR point clouds at 5 x 5 m fine-resolution. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three common modelling techniques (MaxEnt, BRT and GLM) and four discrimination metrics to evaluate model performance (Sørensen index, overprediction and underprediction rate, AUC and TSS).</p> <p>We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance.</p> <p>We recommend fitting models with the finest possible analysis grain (i.e., depending on data availablity) even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrence data, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences.</p>
Global warming pushes the distribution range of the two alpine 'glasshouse' Rheum species north- and upwards in the Eastern Himalayas (EH) and the Hengduan Mountains (HM)
<p><span>Alpine plants' distribution is being pushed higher towards mountaintops due to global warming, finally diminishing their range and thereby increasing the risk of extinction. Plants with specialized 'glasshouse' structures have adapted well to harsh alpine environments, notably to the extremely low temperatures, which makes them vulnerable to global warming. </span><span>How</span><span>ever, their response to global warming is quite unexplored. Therefore, by compiling occurrences and several environmental strata, we utilized multiple ensemble species distribution modeling (eSDM) to estimate the historical, present-day, and future distribution of two alpine 'glasshouse' species <em>Rheum nobile</em> Hook. f. & Thomson and <em>R. alexandrae</em> Batalin. <em>Rheum nobile</em> was predicted to extend its distribution from the Eastern Himalaya (EH) to the Hengduan Mountains (HM), whereas <em>R. alexandrae</em> was restricted exclusively in the HM. Both species witnessed a northward expansion of suitable habitats followed by a southerly retreat in the HM region. Our findings reveal that both species have a considerable range shift under different climate change scenarios, mainly triggered by precipitation rather than temperature. The model predicted northward and upward migration for both species since the last glacial period which is mainly due to expected future climate change scenarios. Further, the observed niche overlap between the two species presented that they are more divergent depending on their habitat, except for certain regions in the HM. However, relocating appropriate habitats to the north and high elevation may not ensure the species' survival, as it needs to adapt to the extreme climatic circumstances in alpine habitats. Therefore, we advocate for more conservation efforts in these biodiversity hotspots.</span></p>
Response of distribution patterns of two closely related species in Taxus genus to climate change since last inter-glacial
<p>Climate change affects species' spatio-temporal distribution deeply. However, how climate affects the spatio-temporal distribution pattern of related species on the large scale remains largely unclear. Here, we selected two closely related species in the <em>Taxus</em> genus, <em>Taxus chinensis</em> and <em>Taxus mairei,</em> to explore their distribution pattern. Four environmental variables were employed to simulate the distribution patterns using the optimized Maxent model. The results showed that the highly suitable area of <em>T. chinensis</em> and <em>T. mairei</em> in the current period was 1.616 × 10<sup>5</sup> km<sup>2</sup> and 3.093 × 10<sup>5</sup> km<sup>2</sup>, respectively. The distribution area of <em>T. chinensis</em> was smaller than that of <em>T. mairei</em> in different periods. Comparison of different periods shows that the distribution area of the two species was almost in stasis from LIG to the future periods. Temperature and precipitation were the main climate factors that determined the potential distribution of the two species. The centroids of <em>T. chinensis</em> and <em>T. mairei</em> were in Sichuan and Hunan provinces in current period, respectively. In the future, the centroid migration direction of the two species would shift towards the northeast. Our results revealed that the average elevation distribution of <em>T. chinensis</em> was higher than that of <em>T. mairei</em>. This study sheds new insights into the habitat preference and limiting environmental factors of the two related species and provides a valuable reference for the conservation of these two threatened species.</p>
Data from: Hindcast-validated species distribution models reveal future vulnerabilities of mangroves and salt marsh species
<p>Rapid climate change threatens biodiversity via habitat loss, range shifts, increases in invasive species, novel species interactions, and other unforeseen changes. Coastal and estuarine species are especially vulnerable to the impacts of climate change due to sea level rise and may be severely impacted in the next several decades. Species distribution modeling can project the potential future distributions of species under scenarios of climate change using bioclimatic data and georeferenced occurrence data. However, models projecting suitable habitat into the future are impossible to ground truth. One solution is to develop species distribution models for the present and project them to periods in the recent past where distributions are known to test model performance before making projections into the future. Here, we develop models using abiotic environmental variables to quantify the current suitable habitat available to eight Neotropical coastal species: four mangrove species and four salt marsh species. Using a novel model validation approach that leverages newly available monthly climatic data from 1960-2018, we project these niche models into two time periods in the recent past (i.e., within the past half-century) when either mangrove or salt marsh dominance was documented via other data sources. Models were hindcast-validated and then used to project the suitable habitat of all species at four time periods in the future under a model of climate change. For all future time periods, the projected suitable habitat of mangrove species decreased, and suitable habitat declined more severely in salt marsh species.</p>
Data from: Distributional trends and species richness of Maryland, USA stoneflies (Insecta: Plecoptera), with an emphasis on the Appalachian region
<p>Faunistic studies of regional biodiversity of aquatic insects are increasing in importance as declines are noted globally. Federal and state government conservation attempts for rare and threatened species are predicated upon the initial research of specialized taxonomists and trained field biologists. Reporting of aquatic insect occurrence data provides a baseline for conservation agencies to compare water quality monitoring studies. Updated fieldwork, literature reviews, and database queries for stoneflies from the mid-Atlantic USA state of Maryland necessitated an assessment of species diversity for the state. Seven new state records and one new literature record are presented, bringing the total number of species to 122. Chao1 estimates of species richness are presented for diversity hotspots and the state as a whole, indicating that increased sampling is still necessary to fully understand diversity patterns. Accompanying are assessments of elevation trends and adult presence patterns within nine families. Collections are predominantly restricted to the Appalachian region, herein we direct future efforts to focus on understudied regions. An outline of distribution knowledge for species is presented to inform upcoming State Wildlife Action Plans.</p>
Fig. 1. Distribution map for Bittacidae Handlirsch, 1906 in A review of Bittacidae (Mecoptera) in Guizhou, China with descriptions of three new species
Fig. 1. Distribution map for Bittacidae Handlirsch, 1906 in Guizhou, China.
Fig. 21 in A New Nalassus Mulsant, 1854 (Coleoptera: Tenebrionidae) From Transcaucasia With A Key To Species From The Greater Caucasus And Notes On The Taxonomy, Distribution, Bionomics And Trophic Relations
Fig. 21. Proportions of trophic and ecological groups of Nalassus in the Greater Caucasus
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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.