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312 results for “change in distribution”
Cover crops improve soil structure and change OC distribution in aggregate fractions
<p>Data set and R script on the statistic evaluation of soil data. The data derived from a long-term field trial at the Asendorf field station 70 km north of Hanover, Germany (49 m above sea level, 52°45′48.4′′N 9°01′24.3′′E). Data include soil data (OC, TN, bulk density, texture) as well as data from soil aggregate fractionation and evaluation of their aggregate stability. All methods and data will be described in an upcoming journal article (DOI will be provided soon).</p>
Climate-change-driven cooling can kill marine megafauna at their distributional limits
<p>The impacts on marine species from secular warming and heatwaves are well demonstrated, however the impacts of extreme cold events are poorly understood. Here, we link the death of organisms from 81 species to an intense cold upwelling event in the Agulhas Current, and show trends of increasing frequency and intensification of upwelling in the Agulhas Current and East Australian Current. Using electronic tagging, we illustrate potential impacts of upwelling events on the movement behaviour of bull sharks, Carcharhinus leucas including alterations of migratory patterns and maintenance of shallower dive profiles when transiting through upwelling cells. Increasing upwelling could result in "bait-and-switch" situations, where climate change expands subtropical species' distribution, while simultaneously exposing climate-migrants to increased risk of cold-stun/mortality events at poleward distributional limits. This shows the potential impacts of increased cold events, an understudied aspect of climate-change research, and highlights the complexities of climate change effects on marine ecosystems.</p>
Data from: Climate change is predicted to impact the global distribution and richness of pines (genus Pinus) by 2070
<p>Aim: Climate change is altering habitat suitability for many organisms and modifying species ranges at a global scale. Here we explored the impact of climate change on 112 pine species (<em>Pinus</em>), fundamental elements of Northern terrestrial ecosystems.</p> <p>Location: Global.</p> <p>Methods: We applied a novel methodology for species distribution modelling that considers uncertainty in climatic projections and taxon sampling, and incorporates elements of species' recent evolutionary history. We based our niche calculations on climate and soil data and computed projections across multiple algorithms and IPCC scenarios, which were ensembled into one single suitability map. We then used phylogenetic methods to account for recent evolution in climatic requirements by estimating the evolution of climatic niche. Edaphoclimatic and evolutionary analyses were then combined to calibrate the projections in areas showing high uncertainty. We validated our models using naturalized occurrences of invasive pine species.</p> <p>Results: Our models predicted that by 2070 most pine species (58%) might face important reductions of habitat suitability, potentially leading to range losses and a decrease in species richness, particularly in some regions such as the Mediterranean Basin and South North America, albeit migration might mitigate these shifts in some cases. In contrast, our projections showed increased habitat suitability for approx. 20% of species, which may undergo range expansions under climate change. Moreover, the consideration of recent evolutionary trends modified projected scenarios, decreasing range loss and increasing range expansion for some species. The independent validation endorsed our models for many species and the influence of recent evolution in some cases.</p> <p>Conclusions: We predict that climate change will impose drastic changes in pine distribution and diversity across biogeographical regions, but the magnitude and direction of change will vary significantly across regions and taxa. Species-level responses are likely to be influenced by regional conditions and the recent evolutionary history of each taxon.</p>
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>
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>
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>
Dataset: Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation
<p>This repository contains the files associated with the following article:</p> <p>Jesús Sandoval-Martínez, Ernesto I. Badano, Francisco A. Guerra-Coss, Jorge A. Flores Cano, Joel Flores, Sandra Milena Gelviz-Gelvez, Felipe Barragán-Torres, “Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation”, submitted to <em>Journal of Environmental Management</em>.</p> <p><strong>Supplementary material 01 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. A file correspond to <em>Vachellia pennatula</em> and the another file correspond to <em>Prosopis laevigata</em>. In both files, the first spreadsheet shows the occurrence data (latitude and longitude) used to calibrate the distribution model (SDM) of the corresponding species, the current values of the 19 bioclimatic variables associated with these coordinates and the Spearman correlation coefficients used to select the variables included in the SDM (selected variables are indicated in green). The second spreadsheet shows the current habitat occupancy probabilities of the target species estimated with the SDM at the geographic coordinates of occurrence points, while the table on the side shows the fraction of true presences dropping at the following probability categories: (1) habitat occupancy probabilities below 0.1 = unsuitable spatial units for the species, (2) habitat occupancy probabilities between 0.1 and 0.4 = barely suitable spatial units for the species, (3) habitat occupancy probabilities between 0.4 and 0.7 = moderately suitable spatial units for the species, and (4) habitat occupancy probabilities above 0.7 = highly suitable spatial units for the species. The third spreadsheet shows the one-thousand random geographic coordinates and the corresponding current and future habitat occupancy probabilities of each species. Future habitat occupancy probabilities are provided for three time periods (2041-2060, 2061-2080 and 2081-2100) at four radiative forcing levels each (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>).</p> <p><strong>Supplementary material 02 </strong>is a compressed file that contains a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>. Each of these folders contains the summaries of the MaxEnt outputs that support the results of the corresponding SDM.</p> <p><strong>Supplementary material 03 </strong>is a compressed Keyhole Markup Language file (KMZ) that contains interactive maps that are optimized for the desktop version of Google Earth. To accelerate visualization of maps, we recommend installing this software in a computer meeting the following requirements: CPU Intel Core i5 9<sup>th</sup> generation or higher, CPU clock speed 1.8 GHz or higher, random-access memory (RAM) 8 GB or higher, and video random access memory (VRAM) 1 GB or higher. Otherwise, opening this file may take several minutes. These maps are organized in a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>, which must be expanded for accessing the following information (click on the arrow on the left of folders to expand them):</p> <ul> <li><strong>Current climate </strong>– Activating this folder (click the fox on the left of the folder) display the map of habitat occupancy probabilities of species across Mexico under the current climate.</li> <li><strong>Period 2041-2060, 2061-2080 and 2081-2100 </strong>– Expanding each of these folders (click on the arrow on the left of folders) shows four subfolders that correspond to different radiative forcing levels (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>). Activating each of these sub folders (click the fox on the left of subfolders) display the map of habitat occupancy probabilities of species across Mexico expected on the corresponding time period and radiative forcing level. These maps also show the areas classified as climatically unsuitable in the multivariate environmental similarity surface (MESS) analysis. Clicking on the names of subfolders displays a figure showing the relationship between current and future habitat occupancy probabilities of the species on the corresponding time period and radiative forcing level. In these figures, the red line is the empirical relationship between these variables and the solid blue line is the theoretical relationship with intercept = 0 and slope = 1. The statistical results that support these relationships are also shown in these figures.</li> </ul> <p><strong>Supplementary material 04 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. the file labeled as “Microclimate data” contains two spreadsheets, which correspond to the temperature and rainfall values measured in controls under the current climate and climate change simulation plots located of the field experiments. The file levelled as “Seedling emergence and survival” contains a spreadsheet for <em>Vachellia pennatula</em> and another one for <em>Prosopis laevigata</em>, which contains the data used to estimate the seedling emergence and survival rates in controls and climate change simulation plots.</p>
Shifts in the composition and distribution of Pacific Arctic larval fish assemblages in response to rapid ecosystem change
<p>The Pacific Arctic marine ecosystem has undergone rapid changes in recent years due to ocean warming, sea ice loss, and increased northward transport of Pacific‐origin waters into the Arctic. These climate‐mediated changes have been linked to range shifts of juvenile and adult subarctic (boreal) and Arctic fish populations, though it is unclear whether distributional changes are also occurring during the early life stages. We analyzed larval fish abundance and distribution data sampled in late summer from 2010 to 2019 in two interconnected Pacific Arctic ecosystems: the northern Bering Sea and Chukchi Sea, to determine whether recent warming and loss of sea ice have restricted habitat for Arctic species and altered larval fish assemblage composition from Arctic‐ to boreal‐associated taxa. Multivariate analyses revealed the presence of three distinct multi‐species assemblages across all years: (1) a boreal assemblage dominated by yellowfin sole (<em>Limanda aspera</em>), capelin (<em>Mallotus catervarius</em>), and walleye pollock (<em>Gadus chalcogrammus</em>); (2) an Arctic assemblage composed of Arctic cod (<em>Boreogadus saida</em>) and other common Arctic species; and (3) a mixed assemblage composed of the dominant species from the other two assemblages. We found that the wind‐ and current‐driven northward advection of warmer, subarctic waters and the unprecedented low‐ice conditions observed in the northern Bering and Chukchi seas beginning in 2017 and persisting into 2018 and 2019 have precipitated community‐wide shifts, with the boreal larval fish assemblage expanding northward and offshore and the Arctic assemblage retreating poleward. We conclude that Arctic warming is most significantly driving changes in abundance at the leading and trailing edges of the Chukchi Sea larval fish community as boreal species increase in abundance and Arctic species decline. Our analyses document how quickly larval fish assemblages respond to environmental change and reveal that the impacts of Arctic borealization on fish community composition spans multiple life stages over large spatial scales.</p>
Fig. 2 in Predicting the potential distribution of the subalpine broad-leaved tree species, Betula ermanii Cham. under climate change in South Korea
Fig. 2. ROC curve and AUC value in the current period (1970- 2000).
Fig. 1 in Predicting the potential distribution of the subalpine broad-leaved tree species, Betula ermanii Cham. under climate change in South Korea
Fig. 1. The location of occurrence points of Betula ermanii in South Korea (N = 162).
The effects of progressive land use changes on the distribution, abundance and behavior of vector mosquitoes in Sabah, Malaysia
<b>Description: </b><p>The objectives of this study were:1) To investigate the effects of progressive land use change from pre-development forest, through forest clearing and cultivation to plantation maintenance on occurrence of vector mosquitoes.<br>2) To determine the status of Anopheles donaldi as a vector of malaria in changing land uses.<br>3) To study the seasonality, abundance and behaviour of vector mosquitoes in study areas.<br><br>Methods<br>Study sites:<br>Study areas were located at The SAFE Project field site:<br>1. areas between Maliau Basin Conservation Area (old growth site),<br>2. logged forest sites in the Benta Wawasan area (area undergoing clearing),<br>3. oil palm plantation sites in Benta Wawasan's Silangan Batu Estate (oil palm site)<br><br>Mosquito collection<br>Mosquito samplings (adults and immature stages) were taken at all 3 study areas every alternate month from January 2017 until December 2018. Every sampling month, 2 collectors (n=2) spent 1 night at each study area where all-night human landing collection were carried out at 3 different sampling points for each collector. Collectors performed outdoor landing catches from 18:00 to 06:00. They collected mosquitoes that landed on naked legs with aspirators. Collectors were given prophylaxis prior to the sampling activities. Collected mosquitoes were then placed at hourly intervals inside glass vials. Mosquitoes were morphologically identified using available dichotomous keys the following morning. In every sampling period, meteorological data such as air temperature, relative humidity, atmospheric pressure and wind speed was recorded on hourly basis using a handheld weather station.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/180"><b>The effects of progressive land use changes on the distribution, abundance and behavior of vector mosquitoes in Sabah, Malaysia</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Universiti Malaysia Sabah (Studentship)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (SaBC) (Research licence Local)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3475408">here</a></p><p><b>Files: </b>This consists of 1 file: Evyen_Mosquito_data.xlsx</p><p><b>Evyen_Mosquito_data.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Mosquito_count</b> (described in worksheet Mosquito_count)</p><p>Description: The taxonomic identification of mosquitos caught</p><p>Number of fields: 6</p><p>Number of data rows: 40</p><p>Fields: </p><ul><li><b>Month</b>: month the mosquitos (Field type: categorical)</li><li><b>Species</b>: species ID of mosquitos caught (Field type: taxa)</li><li><b>MB</b>: Number of species caught in the Maliau Basin (Field type: numeric)</li><li><b>LFE</b>: Number of species caught in the LFE safe plot (Field type: numeric)</li><li><b>B_862</b>: Number of species caught in the B fragment SAFE (Field type: numeric)</li><li><b>Total</b>: Total caught per month (Field type: numeric)</li></ul></li><li><p><b>Mosquito_weather</b> (described in worksheet Mosquito_weather)</p><p>Description: The weather conditions of the mosquito samplings days</p><p>Number of fields: 9</p><p>Number of data rows: 203</p><p>Fields: </p><ul><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Time</b>: Time of sampling (Field type: time)</li><li><b>Location</b>: Location of sampling (Field type: location)</li><li><b>Temperature</b>: Air tempreture (Field type: numeric)</li><li><b>Humidity</b>: Air humidity (Field type: numeric)</li><li><b>Wind Speed</b>: Wind (Field type: numeric)</li><li><b>Pressure</b>: Atomspheric pressure (Field type: numeric)</li><li><b>No.mosquito collected</b>: Number of mosquitos caught (Field type: numeric)</li><li><b>Notes</b>: Species (Field type: comments)</li></ul></li></ol><p><b>Date range: </b>2017-07-16 to 2018-08-21</p><p><b>Latitudinal extent: </b>4.4300 to 5.0700</p><p><b>Longitudinal extent: </b>116.5800 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Arthropoda <br> -  -  -  Insecta <br> -  -  -  -  Diptera <br> -  -  -  -  -  Culicidae <br> -  -  -  -  -  -  <i>Heizmannia</i> <br> -  -  -  -  -  -  <i>Anopheles</i> <br> -  -  -  -  -  -  -  <i>Anopheles balabacensis</i> <br> -  -  -  -  -  -  -  <i>Anopheles latens</i> <br> -  -  -  -  -  -  <i>Culex</i> <br> -  -  -  -  -  -  -  <i>Culex sitiens</i> <br> -  -  -  -  -  -  -  <i>Culex vishnui</i> <br> -  -  -  -  -  -  <i>Aedes</i> <br> -  -  -  -  -  -  -  <i>Aedes albopictus</i> <br> -  -  -  -  -  -  -  <i>Aedes ganapathi</i> <br></div><p></p>
Figure 2 in Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1874), in Iran: an insight into the impact of climate change
Figure 2. The habitat suitability map of the Mesopotamian spiny-tailed lizard in southwestern Iran.
Impact of climate change on the potential distribution of a rare species of planthopper in China: Caliscelis chinensis Melichar, 1906 and its conservation implications
<p>This information is from the manuscript called "Impact of climate change on the potential distribution of a rare species of planthopper in China: Caliscelis chinensis Melichar, 1906, and its conservation implications." It was added to help with the review process and is extra information.</p>
Fig. 1 in South American Sea Lions Otaria flavescens, a good indicator of relative spatial and temporal changes in the distribution and abundance of marine resources?
Fig. 1. Study area showing the location of the rookeries analysed at RÍo Negro Province, Argentina.
Data output from Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean
<p>Data output from Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean:<br>Rasters, R models and scripts</p>
Data for: Exploring Transition States of Protein Conformational Changes via Out-of-Distribution Detection in the Hyperspherical Latent Space
<p>This contains all the TS-DART training results as well as the raw MD simulation data reported in the preprint "Exploring Transition States of Protein Conformational Changes via Out-of-Distribution Detection in the Hyperspherical Latent Space".</p>
Data from: Recurrent sublethal warming reduces embryonic survival, inhibits juvenile growth, and alters species distribution projections under climate change
The capacity to tolerate climate change often varies across ontogeny in organisms with complex life cycles. Recently developed species distribution models incorporate traits across life stages; however, these life-cycle models primarily evaluate effects of lethal change. Here, we examine impacts of recurrent sublethal warming on development and survival in ecological projections of climate change. We reared lizard embryos in the laboratory under temperature cycles that simulated contemporary conditions and warming scenarios. We also artificially warmed natural nests to mimic laboratory treatments. In both cases, recurrent sublethal warming decreased embryonic survival and hatchling sizes. Incorporating survivorship results into a mechanistic species distribution model reduced annual survival by up to 24% compared to models that did not incorporate sublethal warming. Contrary to models without sublethal effects, our model suggests that modest increases in developmental temperatures influence species ranges due to effects on survivorship.
Potential distributional shifts in North America of allelopathic invasive plant species under climate change models
<p>Occurrence data for invaive species used in ecological niche modeling for predictive studies. These data are cleaned to removed data with duplicates, incomplete coordinates, unlikely coordinates (e.g., 0,0), or those lacking environmental data were removed using the scrubr v.0.1.1 package in R (Chamberlain, 2016). Points falling outside of the respective training region for each species were also removed. These data represent downloads from iDigBio and GBIF.</p>
Data from: Current distributions and future climate‐driven changes in diatoms, insects and fish in U.S. streams
<span>Aim</span> <p class="abstract_para">Biodiversity on Earth is threatened by climate change. Despite the vulnerability of freshwater habitats to human impacts, most climate change projections have focused on terrestrial systems. Here, we examined how the current distributions and biodiversity of stream taxa might change under mitigated, stabilizing and increasing greenhouse gas emissions.</p> <span>Location</span> <p class="abstract_para">Conterminous USA.</p> <span>Time period</span> <p class="abstract_para">Present day to 2070.</p> <span>Major taxa studied</span> <p class="abstract_para">Stream diatoms, insects and fish.</p> <span>Methods</span> <p class="abstract_para">We developed species distribution models for 336 freshwater taxa from 1,227 distinct stream localities using water chemistry, watershed and climatic variables. Models based only on climate were used to project changes in the distributions and biodiversity of cold‐ versus warm‐water taxa under representative concentration pathways (RCPs) ranging from 2.6 to 8.5 W/m<sup>2</sup>.</p> <span>Results</span> <p class="abstract_para">In all three organismal groups, climate emerged as the strongest predictor of species distributions, providing comparable explanatory power to water chemistry and watershed variables combined. The RCP‐based projections suggested a widespread expansion of warm‐water taxa, outpacing the decline of cold‐water taxa. Consequently, overall species richness would increase, but beta diversity would decrease drastically with the severity of climate change. A closer look at individual taxa and functional guilds revealed that vulnerable cold‐water taxa included: (a) diatom guilds forming the base and bulk of the biofilm; (b) environmentally sensitive insects, characteristic of unimpacted streams; and (c) ecologically and recreationally important salmonids, which were forecast to diminish dramatically in source habitats. Warm‐water fish projected to increase their distributions include bait bucket release minnows and dominant predators.</p> <span>Main conclusions</span> <p class="abstract_para">Our results suggest potentially devastating impacts of climate change on stream ecosystems, with the restructuring of diatom, insect and fish communities, diminished distributions of functionally important taxa and widespread expansion of warm‐water taxa, giving rise to biotic homogenization. Given that the magnitude of these biotic shifts depends on the severity of climate change, appropriate current policy decisions are necessary to preserve freshwater ecosystems.</p>
Niche dynamics of Memecylon in Sri Lanka: distribution patterns, climate change effects, and conservation priorities
<p><b>Aim</b>: Recent climate projections have shown that the distribution of organisms in island biotas is highly affected by climate change. Here, we present the results of the analysis of niche dynamics of a plant group, <i>Memecylon</i> on Sri Lanka, an island, using species occurrences and climate data. We aim to determine which climate variables explain current distribution, model how climate change impacts the availability of suitable habitat for <i>Memecylon,</i> and determine conservation priority areas for Sri Lankan <i>Memecylon</i>.</p> <p><b>Location</b>: Sri Lanka</p> <p><b>Methods</b>: We used georeferenced occurrence data of Sri Lankan <i>Memecylon</i> to develop ecological niche models and assess both current and future potential distributions under six climate change scenarios in 2041-2060 and 2061-2080. We also overlaid land-cover, and protected area maps and performed a gap analysis to understand the impacts of land-cover changes on <i>Memecylon</i> distributions and propose new areas for conservation.</p> <p><b>Results</b>: Differences among suitable habitats of <i>Memecylon</i> were found to be related to patterns of endemism. Under varying future climate scenarios, endemic groups were predicted to experience habitat shifts, gains, or losses. The narrow endemic <i>Memecylon </i>restricted to the montane zone were predicted to be the most impacted by climate change. Projections also indicated that changes in species' habitats can be expected as early as 2041-2060. Gap analysis showed that while narrow endemic categories are considerably protected as demonstrated by their overlap with protected areas, more conservation efforts in Sri Lankan forests containing wide endemic and non-endemic <i>Memecylon</i> are needed.</p> <p><b>Main conclusions</b>: This research helped clarify general patterns of responses of Sri Lankan <i>Memecylon </i>to global climate change. Data from this study are useful for designing measures aimed at filling the gaps in forest conservation on this island.</p>
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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.