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860 results for “distribution ecology”
FIGURE 1 in Taxonomy, ecology and distribution of the mosquitoes (Diptera: Culicidae) of the Dutch Leeward Islands, with a key to the adults and fourth instar larvae
FIGURE 1 Sample sites of mosquitoes on Sint Eustatius, Saba and Dutch Sint Maarten during the 2018 Naturalis expeditions. The sampling methods referred to as "Other" comprise of different manual methods to sample adults and larvae. Sites with a BG-sentinel trap or other sampling method that was within 50 m of a Mosquito Magnet site are not separately shown on this map. Map created with QGIS, basemap. © OpenStreetMap contributors and the GIS user community.
Distribution System Environmental and Sequencing Datasets for Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System
<p>The dataset of environmental parameters and sequence fastqs used to create figures and do analysis in the paper <strong>Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System </strong>submitted to Environmental Science & Technology</p>
Data from: Inferring ecological selection from multidimensional community trait distributions along environmental gradients
<p>Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. Ecological selection ubiquitously shapes communities by selecting for individuals with most suitable trait combinations. Detecting selection types on key traits across environmental gradients and over time has the potential to reveal underlying abiotic and biotic drivers of community dynamics. Here we present a model-based predictive framework to quantify multidimensional trait distributions of communities (community trait niches), which we use to identify ecological selection types shaping communities along environmental gradients. We apply the framework to over 3600 boreal forest understory plant communities with results indicating that directional, stabilizing, and divergent selection all modify community trait niches and that the selection type acting on individual traits may change over time. Our results provide novel and rare empirical evidence for divergent selection within a natural system. Our approach provides a framework for identifying key traits under selection and facilitates the detection of processes underlying community dynamics.</p>
Data from: Inferring ecological selection from multidimensional community trait distributions along environmental gradients
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Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
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Data from: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range
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Data and code from: Coordinated distributed experiments in ecology do not consistently reduce heterogeneity in effect size
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Data from: Species distribution models of the Spotted Wing Drosophila (Drosophila suzukii, Diptera: Drosophilidae) in its native and invasive range reveal an ecological niche shift
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Spatial distribution of benthic algae in the South China Sea: responses to gradually changing environmental factors and ecological impacts on coral communities
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Ecological niche modeling and first records from Namibia and Zimbabwe validate the amphi-equatorial distribution of Byrsinus pseudosyriacus (Hemiptera: Cydnidae) - Supplementary data
<p><em>Byrsinus pseudosyriacus (</em>Linnavuori, 1977), the most widely distributed Afrotropical species of the genus <em>Byrsinus</em> Fieber, 1860 known hitherto only from the Sudano-Eremian area is for the first time reported in two countries south of the Equator. The species potential distribution map was generated using the ecological niche modelling (ENM) methods that allowed this species to be regarded as amphi-equatorial in distribution<strong>.</strong></p>
Data from: Form–function relationships in a marine foundation species depend on scale: a shoot to global perspective from a distributed ecological experiment
Form-function relationships in plants underlie their ecosystem roles in supporting higher trophic levels through primary production, detrital pathways, and habitat provision. For widespread, phenotypically-variable plants, productivity may differ not only across abiotic conditions, but also from distinct morphological or demographic traits. A single foundation species, eelgrass (Zostera marina), typically dominates north temperate seagrass meadows, which we studied across 14 sites spanning 32-61° N latitude and two ocean basins. Body size varied by nearly two orders of magnitude through this range, and was largest at mid-latitudes and in the Pacific Ocean. At the global scale, neither latitude, site-level environmental conditions, nor body size helped predict productivity (relative growth rate 1-2% d-1 at most sites), suggesting a remarkable capacity of Z. marina to achieve similar productivity in summer. Furthermore, among a suite of stressors applied within sites, only ambient leaf damage reduced productivity; grazer reduction and nutrient addition had no effect on eelgrass size or growth. Scale-dependence was evident in different allometric relationships within and across sites for productivity and for modules (leaf count) relative to size. Z. marina provides a range of ecosystem functions related to both body size (habitat provision, water flow) and growth rates (food, carbon dynamics). Our observed decoupling of body size and maximum production suggests that geographic variation in these ecosystem functions may be independent, with a future need to resolve how local adaptation or plasticity of body size might actually enable more consistent peak productivity across disparate environmental conditions.
Data from: A new null model approach to quantify performance and significance for ecological niche models of species distributions
Aim: Ecological niche modelling requires robust estimation of model performance and significance, but common evaluation approaches often yield biased estimates. Null models provide a solution but are rarely used in this field. We implemented an important modification to existing null-model tests, evaluating null models with the same withheld records that were used to evaluate the real model. We built and evaluated models across a range of modelling scenarios and for various performance measures using the algorithm Maxent and the monk parakeet (Myiopsitta monachus). Location: Native range in Southern America and global invasions predominantly in North/Central America and Europe Methods: We tested the ability of models built under 15 scenarios (five sets of calibration records and three settings that varied the level of model complexity) to predict spatially independent evaluation data in the invaded range (in effect, testing the models under spatial transfer). We quantified performance with measures of discriminatory ability and overfitting based on AUC and the omission error rate. We estimated null distributions of these measures and calculated effect size and significance. We determined how these estimates varied across modelling scenarios, comparing with two tests existing in the literature. Results: Performance varied starkly across modelling scenarios. As expected, the measures of overfitting agreed with each other and provided different information than that of discriminatory ability. However, high performance per se did not show strong association with high effect size and significance. Main Conclusions: Ecological niche models should be assessed with measures of effect size and significance based on appropriate null distributions, in contrast to several approaches existing in the literature. The proposed approach using independent evaluation data, implemented with our accompanying code, allows such estimates for either the same or a different region/time period, and it merits use and continued development.
Figure 2 in Distribution and roosting ecology of the lesser mouse-tailed bat, Rhinopoma hardwickii Gray, 1831 (Chiroptera: Rhinopomatidae)
Figure 2. The fatty abdominal tissue of Rhinopoma hardwickii.
Figure 1 in Distribution and roosting ecology of the lesser mouse-tailed bat, Rhinopoma hardwickii Gray, 1831 (Chiroptera: Rhinopomatidae)
Figure 1. The lesser mouse-tailed bat, Rhinopoma hardwickii.
Figure 1. The map illustrates 142 in Taxonomy, distribution, and ecology of crustacean zooplankton in trough waters of Ankara (Turkey)
Figure 1. The map illustrates 142 trough sites sampled in Ankara.
Figure 5. a in Taxonomy, distribution, and ecology of crustacean zooplankton in trough waters of Ankara (Turkey)
Figure 5. a) trough with 4 segments made of cement (photo taken 23 June 2011).
Figure 5. c in Taxonomy, distribution, and ecology of crustacean zooplankton in trough waters of Ankara (Turkey)
Figure 5. c) trough made of cement (photo taken 24 June 2011).
Data & codes for "Changes in abundance and distribution of European forest bird populations depend on biome, ecological specialisation and traits"
<h1>1. Selection of European forest bird species and classification of their biome preferences</h1> <p>We selected all species that are related to forest and woodland based on two data sources: Storchová & Hořák (2018) and Tobias et al. (2022), resulting in 107 bird species studied (Data S1). We defined forest bird species as those using environments ranging from closed-canopy forests to more open-canopy woodlands (A. Lehikoinen & Virkkala, 2018; Storchová & Hořák, 2018; Tobias et al., 2022). We determined their biome specialisation using breeding distribution centroids and the overall breeding distribution of each of the species, using the global map of terrestrial ecoregions from Olson et al. (2001) and range data from European Breeding Bird Atlas 1 and 2 (Hagemeijer & Blair, 1997; Keller et al., 2020). We categorised species as Mediterranean, temperate, or boreal based on their predominant biogeographic region. We considered species commonly occurring over several biomes as “generalists”. For instance, we reclassified the two typically boreal species Glaucidium passerinum Linnaeus and Strix uralensis Pallas as “generalists” due to significant range expansions into central and southern Europe in recent decades, therefore no longer restricted to the boreal region. For the complete list of species, biome specialisation, traits, and specialisation indices, refer to Data S1.</p> <h1>2. Changes in abundance and distribution of European forest bird species</h1> <p>We assessed long-term changes in European forest bird populations through two approaches: (i) changes in estimated total European-level species abundance over a 40-year timeframe; and (ii) changes in species spatial distribution over a 30-year timeframe (Fig. 1).</p> <p>We utilized the estimated trends in European-level population size (i.e., the total number of individuals) for each common native European bird species from 1980 to 2017, as reported by Burns et al. (2021). Three species out of the 107 studied forest species were missing in the original manuscript and we used data generated with the same method from 1980 to 2018 from the European assessment, Article 12 (https://nature-art12.eionet.europa.eu/article12/). These abundance trends were calculated by Burns et al. (2021) using multi-sourced annual times series. For each species, they gathered population estimates and trends from each European country as well as European Union (EU)-level population trends. They analysed these data with a Bayesian hierarchical model to reconstruct EU-level smoothed species population time series. The model outputs include an average annual rate of abundance change and an associated 95% credible interval (Burns et al., 2021). Therefore, we did not directly use the average annual rate of abundance change, as this would have led us to consider species with low uncertainty as similar to those with high uncertainty. To account for the uncertainty, we categorised species as (i) declining, i.e., annual rates below one, (ii) increasing, i.e., annual rates above one and (iii) stable, i.e., annual rate whose 95% CI overlap one, i.e., no significant change. To better acknowledge the magnitude of the abundance change, significant changes with rates below 0.98 were labelled as “strongly declining” (i.e., 6.5% of the 107 species), while those above 1.02 were labelled as “strongly increasing” (i.e., 11% of the 107 species). To evaluate the sensitivity of the decision to categorised abundance change data, we also analysed abundance trend as continuous variable (see Supporting Information Fig. S8).</p> <p>To determine changes in species distributions, we used a comparison of species distributions between two periods (i.e., 1985-1988 and 2013-2017) using the European Breeding Bird Atlas 1 and 2 (EBBA 1 & 2; Hagemeijer & Blair, 1997; Howard et al., 2023; Keller et al., 2020). Howard et al. (2023) provided calculations of observed colonisation and extinction areas at a 50 x 50 km resolution across Europe. We measured changes in range as the difference between colonisations and extinctions of each species, with negative values indicating contracting ranges and positive values indicating expanding ranges. Additionally, we calculated the shift in the centre of gravity of the distribution range between the two periods, as a distance (km) along the south-north gradient for each species (Howard et al., 2023).</p> <h1>3. Trait and specialisation data for European forest bird species</h1> <p>We extracted data for six functional traits from several sources (Table 1). (i) The species temperature index (STI)represents the long-term average temperature within the species’ breeding range (A. Lehikoinen et al., 2021). (ii) Diet data during the breeding season were obtained from Storchová & Hořák (2018), classifying species into binary variables as vertebrate carnivorous, invertebrate carnivorous, and herbivores (combining the leaf and seed eaters). Storchová & Hořák (2018) classified species into a diet category when the corresponding food resource represented at least 10% of the species diet throughout the breeding season. Therefore, one species can be in several categories (i.e., omnivores). (iii) We obtained nesting site data from Pearman et al. (2014), classifying species into binary variables as ground nesters, tree hole nesters, or elevated nesters (> 1 m in a tree or shrub). We also included data on (iv) species dependence on old-growth forests (Data S1; mostly from Fraixedas et al. (2015) and Mönkkönen et al. (2014), if present on both references, we classified them as “1” and if only in one reference as “0.5”), (v) migration distance (Howard et al., 2023), and (vi) body mass (Tobias et al., 2022).</p> <p>Finally, we extracted and developed seven species specialisation indices. (i) We used an overall specialisation index based on multiple traits (i.e., temperature, diet, foraging behaviour and substrate, habitat, and nesting site), and (ii) a nesting specialisation index, both obtained from Morelli et al. (2019). Both indices represent species specialization based on the dispersion of trait preferences for each species: e.g., nesting specialism equal 0 for species that nest in all habitat type and equal 1 for species that nest in only one habitat type). They are both calculated using the Gini index of inequality, which measures overall dispersion across, e.g., all traits for the overall specialization, based on data from Pearman et al. (2014) and Storchová & Hořák (2018). For additional information, see Morelli et al. (2019). We also used (iii) the diet specialisation index, (iv) the species distribution range during the breeding season (hereafter “breeding range area”) and (v) the climatic niche breadth from Reif et al. (2016). The diet specialisation index was calculated as the coefficient of variation for diet preferences for each species, where high values denotes specialized species (Reif et al., 2016). The breeding range area was evaluated as the number of 50-km squares in the distribution maps in Europe occupied by each species during the reproduction period, and is based on EBBA 1 (Hagemeijer & Blair, 1997). The climatic niche breadth was calculated as the difference between the 5% hottest and the 5% coldest mean temperature between April and June in which each species occurs, using EBBA 1 (Hagemeijer & Blair, 1997; Reif et al., 2016).</p> <p>Additionally, (vi) we calculated a broadleaf forest specialisation index based on binary forest habitat preferences (Storchová & Hořák, 2018), assigning values of one for species found only in broadleaf forests; zero for those in coniferous forests, and 0.5 for those found in both. Lastly, (vii) we created a forest specialisation index based on the species habitat preferences (Storchová & Hořák, 2018). The forest specialisation index was calculated as the mean of species affinity across habitats. We used increasing habitat weights along a gradient of tree dominance: open habitats as 1, shrubland as 1.5, woodland as 2 (i.e., species associated with habitats structured by trees in lower density than in forest), forest generalist (found in both coniferous and broadleaf dense forests) as 3, and forest specialist (found only either in coniferous or broadleaf dense forests) as 4. For instance, the index value for species occurring either in shrubland, woodland or both broadleaf and coniferous forests is 2.167.</p> <h1>4. Data analysis</h1> <p>Data analyses were conducted with R software version 4.4.1. (R Core Team, 2024). Given the non-independence of species due to their genetic relatedness, we accounted for interspecific phylogenetic distance in all models. We constructed the phylogenetic tree for the 107 European forest bird species using ‘rotl’ and ‘ape’ R-packages (Michonneau et al., 2022; Paradis et al., 2023). We used rotl as an interface with the "Open Tree of Life", employing tol_induced_subtree R-function to generate the phylogenetic tree and compute.brlen R-function to set branch lengths using Grafen’s computation. We generated separate phylogenetic trees for boreal (17), temperate (15), Mediterranean (16) and “generalist” (59) species to perform biome-specific analysis (see Supplementary Information, Figs. S1 & S2).</p> <p>To investigate the effects of functional traits and specialisation indices on abundance, range changes, and distribution shift, we used two regression methods. All methods were based on the relationships between a measure of change and a functional trait or specialisation index. Our sample unit is an individual forest bird species (i.e., one value for each species, either abundance or range change, or distribution shift). Abundance change was a categorical variable (i.e., strong decline – decline – stable – increase – strong increase), while range change (i.e., difference between colonisation and extinction) and distribution shift (i.e., south-north shift) were continuous variables. Therefore, to study abundance changes, we used proportional-odds linear mixed effects model using (Phylo)clmm R-function from the ‘ordinal’ R-package (Christensen, 2022). Interspecific phylogenetic relatedness was included as a random effect, reflecting the correlation between species based on phylogenetic distances (see also Hagge et al. (2021) and Seibold et al. (2015)). For distribution changes, we employed phylogenetic generalised least squares regression (PGLS) using the gls R-function from the ‘nlme’ R-package (Pinheiro et al., 2023). The phylogenetic correlation structure was integrated into PGLS using Pagel’s lambda parameter (λ; Pagel (1999)) a widely used measured of phylogenetic signal strength (see, e.g., Hagge et al., 2021; Triviño et al., 2013).</p> <p>Furthermore, we included latitude, a key driver of bird communities at broad scales (Luoto et al., 2007), as a fixed covariable (centroid latitude of the species’ breeding distribution) in all global models (i.e., species from all biomes together), except for the STI model due to strong correlation. For biome-specific analysis, we included latitude only in boreal species models for range change and distribution shift, as it significantly improved model fit (ΔAIC < -2). We did not add latitude for models specific to temperate, Mediterranean, and generalist species since it did not improve model fits (ΔAIC > -2). Additionally, we included breeding range area in range change and distribution shift models, assuming that species with larger ranges would exhibit larger shifts. We scaled predictors to a mean of 0 and standard deviation of 1 to facilitate effect size comparisons. We adjusted p-values using the Holm method (for n=3) to account for multiple testing of traits and specialisation indices on three response variables.</p>
Urban Stream Environmental and Sequencing Datasets for Exploring the Impacts of Full-Scale Distribution System Orthophosphate Corrosion Control Implementation on the Microbial Ecology of Hydrologically Connected Urban Streams
<p>The dataset of environmental parameters and sequence fastqs used to create figures and do analysis in the paper <strong>Exploring the Impacts of Full-Scale Distribution System Orthophosphate Corrosion Control Implementation on the Microbial Ecology of Hydrologically Connected Urban Streams </strong>submitted to Applied and Environmental Microbiology. </p>
WorldClim, elevation and distribution data for all palms from: The ecology of palm genomes: Repeat-associated genome size expansion is constrained by aridity
<p>Genome size varies 2,400-fold across plants, influencing their evolution through changes in cell size and cell division rates which impact plants' environmental stress tolerance. Repetitive element expansion explains much genome size diversity, and the processes structuring repeat 'communities' are analogous to those structuring ecological communities. However, which environmental stressors influence repeat community dynamics has not yet been examined from an ecological perspective.</p> <p>We measured genome size and leveraged climatic data for 91% of genera within the ecologically diverse palm family (Arecaceae). We then generated genomic repeat profiles for 141 palm species, and analysed repeats using phylogenetically-informed linear models to explore relationships between repeat dynamics and environmental factors.</p> <p>We show that palm genome size and repeat 'community' composition are best explained by aridity. Specifically, <em>Ty3-gypsy</em> and <em>TIR </em>elements were more abundant in palm species from wetter environments, which generally had larger genomes, suggesting amplification. In contrast, <em>Ty1-copia</em> and <em>LINE </em>elements were more abundant in drier environments.</p> <p>Our results suggest that water stress inhibits repeat expansion through selection on upper genome size limits. However, elements which may associate with stress-response genes (e.g., <em>Ty1-copia</em>) have amplified in arid-adapted palm species. Overall, we provide novel evidence of climate influencing the assembly of repeat 'communities'. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.