Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
131
datasets available to search
ShareScore release 0.9.0
Dataset results
131 results for “habitat prediction”
Habitat suitability predictions for a boreal forest indicator species, the northern goshawk (Accipiter gentilis), in Central Finland
<p>This repository contains files that show optimal sites in Central Finland for the northern goshawk (<em>Accipiter gentilis</em>, hereafter goshawk), an indicator species of boreal forests with conservation values. The optimal sites were derived from the habitat suitability model outputs included in the following publication:</p> <p> </p> <p><strong>Björklund Heidi<sup>a</sup>, Parkkinen Anssi<sup>b</sup>, Hakkari Tomi<sup>c</sup>, Heikkinen Risto K.<sup>d</sup>, Virkkala Raimo<sup>d</sup>, Lensu Anssi<sup>b</sup> (2020): Predicting valuable forest habitats using an indicator species for biodiversity. Biological Conservation, </strong><a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> . </p> <p> </p> <p><sup>a</sup> Finnish Museum of Natural History Luomus, P.O. Box 17, FI-00014 University of Helsinki, Finland</p> <p><sup>b</sup> University of Jyvaskyla, Department of Biological and Environmental Science, P.O. Box 35, FI-40014 University of Jyvaskyla, Finland</p> <p><sup>c</sup> Centre for Economic Development, Transport and the Environment Central Finland, P.O. Box 250, FI-40101 Jyväskylä, Finland</p> <p><sup>d</sup> Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland</p> <p> </p> <p>The files are ArcGIS compatible shape files which indicate the spatial location of the 160 m × 160 m grid cells which include forest stands projected to be either highly suitable or suitable as a nesting site for the goshawk in Central Finland. The habitat suitability models and values were developed across the study area using Maxent software. The files show those 160-m grid cells from the study area which were included in one of the following two categories: (i) cells deemed as the most optimal (with high probability of suitable conditions) for goshawk nesting with suitability index values in Maxent outputs varying between 0.92–1.00 (‘best’ goshawk squares), and (ii) cells deemed as ‘good’ goshawk squares (with Maxent suitability index values of ≥ 0.69 and < 0.92). The coordinate system for the data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). </p> <p>Summarization of the key settings and elements of the study are provided below. A detailed treatment can now be found in the article published in Biological Conservation (Björklund et al.) for which the link is the following: <a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> .</p> <p> </p> <p><strong>Summary of the study</strong></p> <p>Intensive commercial use of boreal forests is an accelerating threat to forest biodiversity, highlighting the development of cost-effective tools to detect the locations valuable for conservation. We applied species distribution models (SDMs) in our study area, Central Finland, to locate the optimal nesting sites for the goshawk, an indicator bird species for biodiversity hotspots in mature boreal forests. The optimal sites (here, 160 x 160 m grid squares) for the goshawk were determined using the Maxent software. Optimal squares for the goshawk had forests with considerably high volumes of Norway spruce (<em>Picea abies</em>, hereafter spruce) covering only 3.4% of the boreal landscape, and they were located mostly outside protected areas. Many of the squares with optimal nesting forests appeared to be under threat due to recently intensified logging operations. Half of the squares were logged to some extent and 10% were already lost or notably deteriorated due to logging after 2015 for which our models were calibrated. Threats to biodiversity of mature boreal spruce forests are likely to accelerate with increasing logging pressures. Thus, there is an urgent need to secure the continuous supply of mature spruce forests in the landscape by developing a denser network of protected areas and applying measures that aid in sparing large entities of mature forest on privately-owned land. Our modelled optimal squares can be used for selection of potential areas with biodiversity values in conservation prioritization.</p> <p><strong>The study species</strong></p> <p>The goshawk is a raptor species which prefers mature forests for nesting in Europe. Old forests dominated by spruce are considered as important for the breeding success of the species particularly in northern latitudes. Thus, intensive forest management can impair the breeding possibilities of the goshawk, and changes in forest landscapes are likely to contribute to the decline of the species. For example, in Finland, the goshawk is classified as nearly threatened species. In our study, we used the goshawk as an indicator species to model the spatial locations of boreal forest with much potential for including biodiversity values. The indicator species status of the goshawk is based on earlier studies showing the close association of the goshawk with various taxa of mature spruce forest, as well as the reported declines of both the goshawk and associated species due to loggings.</p> <p><strong>Developing Maxent models for the goshawk</strong></p> <p>The location data on occupied nests of the goshawk gathered in spring and summer 2015 and 2016 in Central Finland – as a part of the Finnish Common Birds of Prey Monitoring – were related to a set of environmental predictor variables using a maximum entropy method, Maxent software, which is considered particularly useful for modelling presence-only data (such as our goshawk nest site data). In our case, the data on forest stand and tree characteristics were related using Maxent to the known nesting sites to predict suitable conditions for the species across the Central Finland. The forest data used in the modelling were extracted from the multi-source national forest inventory (MS-NFI) data sources governed by the Natural Resources Institute Finland. The MS-NFI data used in our modelling are based on field data of the 11th and 12th NFIs from 2009 to 2016 and satellite images from 2015 and 2016.</p> <p>Prior modelling, Pearson correlations were calculated between the continuous environmental variables at the nest sites. Of the highly (|r| ≥ 0.7) correlated variables, we chose those variables which are known to be important for the goshawk, which are useful for generalization in other areas, or whose impact was of specific interest. Our final selected set of predictor variables included one class variable, site fertility class, and nine continuous variables: growing stock volume of the spruce, pine, birches and other hardwood, canopy cover, canopy cover of broad-leaved trees, saw timber of other broad-leaved trees than birches, pulpwood volume of the birches, and the biomass of the stem residual of the spruce. The original MS-NFI data recorded at the resolution of 16 × 16 m were resampled to the resolution of 160 × 160 m for the Maxent models, to represent one potential nesting forest stand.</p> <p>The accuracy of Maxent models were assessed with cross-validation and associated averaged AUC-values. The relative importance of the variables was measured by variable contribution and model deterioration measures provided by Maxent. The cloglog-transformed output index values ranging from 0 to 1 described the relative suitability of the 160-m squares to goshawk nesting. Based on the index values, the squares were classified as ‘optimal’ (with index values of 0.69–1.00), ‘typical’ (0.46– <0.69) and ‘poor’ (<0.46). In addition, we divided optimal squares into ‘best’ goshawk squares (index values of 0.92–1.00 corresponding to a high probability of suitable conditions), and ‘good’ goshawk squares (index values ≥ 0.69 and < 0.92).</p> <p><strong>Maxent model outputs</strong></p> <p>Spruce volume was the most important variable in defining habitat suitability for goshawk nesting, but hardwood cover, other hardwood logs and site fertility class contributed also to some extent to habitat suitability. In Maxent outputs, the set of 160-m squares deemed as optimal for goshawk nesting included 6 895 (cover 0.9% of the study area) best goshawk squares and 19 421 (cover 2.5%) good goshawk squares. The projected best and good goshawk squares were mostly located in unprotected areas: 95.0% of the best and 96.0% of the good goshawk squares occurred completely outside protected areas. For further details concerning the data and the model outputs, see the referred article Björklund et al. (2020).</p> <p><strong>State of the optimal goshawk squares</strong></p> <p>In total, 11% of best and over 9% of good goshawk squares were severely altered due to recent harvesting, typically clear-cutting, of the forests during the time period between 2015 and 2019. Altogether, some level of logging occurred in 3 062 (44%) of best goshawk and 9 846 (51%) of good goshawk squares during the recent years. However, many of the squares still included enough unlogged area for the goshawk in 2019.</p> <p>In our article, we conclude that while most of the optimal squares for the goshawk were still preserved in 2019, they are under risk as they are mainly situated outside protected area network. This stresses the importance of conserving biodiversity with complementary measures in privately-owned managed forests. In conclusion, a denser network with more PAs for forest-dwelling species should be secured in areas with intensive forestry, e.g. in southern Finland where PAs currently cover a smaller proportion of land compared to northern Finland.</p>
Dataset for: African manatee (Trichechus senegalensis) habitat suitability at Lake Ossa, Cameroon using trophic state models and predictions of submerged aquatic vegetation
<p>See research article here: https://onlinelibrary.wiley.com/doi/epdf/10.1002/ece3.8202</p> <p>Aim: The present study aims at investigating the past and current trophic status of Lake Ossa and evaluating its potential impact on African manatee health.</p> <p>Location: Lake Ossa is known as a refuge for the threatened African manatees in Cameroon. Little information exists on the water quality and health of the ecosystem as reflected by its chemical and biological characteristics.</p> <p>Methods: Aquatic biotic and abiotic parameters including water clarity, nitrogen, phosphorous and chlorophyll concentrations were measured monthly during four months at each of 18 water sampling stations evenly distributed across the lake. These parameters were then compared with historical values obtained from the literature to examine the dynamic trophic state of Lake Ossa.</p> <p>Results: Results indicate that Lake Ossa’s trophic state parameters doubled in only three decades (from 1985 to 2016), moving from a mesotrophic to a eutrophic state. The decreasing nutrient gradient moving from the mouth of the lake (in the south) to the north indicates that the flow of the adjacent Sanaga River is the primary source of nutrient input. Further analysis suggests that the poor transparency of the lake is not associated with chlorophyll concentrations but rather with the suspended sediments brought-in by the Sanaga River. Consequently, our model demonstrated that despite nutrient enrichment, less than 5% of the lake bottom surface sustained submerged aquatic vegetation. Thus, shoreline emergent vegetation is the primary food available for the local manatee population. During the dry season, water recedes drastically and disconnects from the dominant shoreline emergent vegetation, decreasing accessibility for manatees.</p> <p>Main conclusions: The current study revealed major environmental concerns (eutrophication and sedimentation) that may negatively impact habitat quality for manatees. Efficient land use and water management across the entire watershed may be necessary to mitigate such issues.</p>
Latitudinal core habitat prediction data for the manuscript: "Seascape topography slows predicted range shifts in fish under climate change"
<p>Latitudinal locations of core environmental habitat for yellowtail kingfish (<em>Seriola lalandi</em>), Australian bonito (<em>Sarda australis</em>), Australian spotted mackerel (<em>Scomberomorus munroi</em>), narrow-barred Spanish mackerel (<em>Scomberomorus commerson</em>) and common dolphinfish (<em>Coryphaena hippurus</em>) nearshore of the continental shelf break (i.e. 200-m isobath) within 145 – 160°E, 15 – 45°S and between years 1998 – 2018.</p>
Dataset of habitat quality does not predict animal population abundance on frequently disturbed landscapes
<p>The data presented here are related to the research article entitled "Habitat quality does not predict animal population abundance on frequently disturbed landscapes". Using an individual-based model, we simulated movement of theoretical individuals in a dynamically disturbed landscape and quantified the error of predicting population spatial relative abundance using an habitat model. This dataset provides the Earth Mover's Distance (EMD) as prediction error measure obtained in simulations with varying individual step length and disturbance frequency.</p>
Future seasonal changes in habitat for Arctic whales during predicted ocean warming
<p><span>Ocean warming is causing shifts in the distributions of marine species, but the location of suitable habitats in the future is unknown, especially in remote regions such as the Arctic. Using satellite tracking data from a 28-year long period, covering all three endemic Arctic cetaceans (227 individuals) in the Atlantic sector of the Arctic, together with climate models under two emission scenarios, species distributions were projected to assess responses of these whales to climate change by the end of the century. While contrasting responses were observed across species and seasons, long-term predictions suggest northward shifts (243 km in summer vs. 121 km in winter) in distribution to cope with climate change. Current summer habitats will decline (mean loss: </span><span>-</span><span>25%), while some expansion into new winter areas (mean gain: +3%) is likely. However, comparing gains vs. losses raises serious concerns about the ability of these polar species to deal with the disappearance of traditional colder habitats.</span></p>
Code and data for "Global warming generates predictable extinctions of warm- and cold-water marine benthic invertebrates via thermal habitat loss"
<pre>This repository contains the following information: Datasets S1 to S4 can all be loaded, manipulated, and analysed in R using script provided in Data S5 to obtain the results of the paper, Reddin et al. 2022, "Global warming generates predictable extinctions of warm and cold-water marine benthic invertebrates via thermal habitat loss". Data S1. (separate file) The original downloaded PaleoDB dataset. Data S2. (separate file) The pre-prepared dataset of occurrences. Data S3. (separate file) The finished environmental dataset. Data S4. (separate file) Additional environmental dataset. Data S5. (separate file) The R-code for the main analysis. Data S6. (compressed directory) Output data and code from the simulations. Table S7 (separate file). List of data source publications for PaleoDB data used in our study. Listed are the data source author list (ref_author), year (ref_pubyr), and reference number as appears in the PaleoDB (reference_no). </pre>
Data from: Semi-natural habitat, but not aphid amount or continuity, predicts lady beetle abundance across agricultural landscapes
<p>The amount of semi-natural habitat surrounding farm fields is a common but inconsistent predictor of natural enemy populations and predation services. Standard land cover metrics may not accurately capture the actual availability of limiting resources for natural enemies and can miss important dynamics across space and time. Theory from animal movement and landscape ecology predicts that regions with more, spatio-temporally continuous resources (i.e. food, shelter) should have larger predator populations and enhanced biological control. To test these predictions empirically, we designed a study measuring aphids, lady beetles, and predation services in agricultural landscapes in Wisconsin, USA. In two study years, we sampled lady beetles and aphids in 336 crop fields (corn, soybean, alfalfa, and small grains) and adjacent semi-natural habitat patches (grasslands and woodlands) across 24 1.5 km buffer landscapes at 4–7 time points each, and in one year we assessed predation rates with sentinel egg cards. We used aphid counts to model habitat-specific aphid phenologies, from which we calculated landscape indices of prey amount and continuity. These indices, along with semi-natural habitat area, were used to predict lady beetle abundance. While there were strong differences in the abundance and timing of aphids by habitat, semi-natural habitat amount was still a better predictor of lady beetle counts and sentinel egg predation than either aphid amount or continuity indices in these landscapes.</p> <p>Synthesis and application: Our findings confirm the robust relationship between lady beetles and semi-natural habitat in agricultural landscapes, and highlight the complexities of measuring fine-scale resource heterogeneity in real landscapes. Retaining or adding woodland and grassland patches in agricultural landscapes is likely to support larger lady beetle populations and enhance predation in crop fields. Our results suggest that these habitats may be more important for shelter than prey continuity, though this mechanism warrants further investigation. Future work should continue to refine experimental methods for the successful integration of landscape ecology and animal behavior to support conservation goals.</p>
Figure 2 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 2. Maxent predictions of suitable zebra mussel (Dreissena polymorpha) habitat in Texas. Shading indicates the logistic output of the Maxent model. Polygons represent state and national borders as well as major river basins within Texas.
Figure 1 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 1. Physicochemical data survey lakes. Sites categorized by TPWD (at the time of this study in 2016) as "infested" (the water body has an established, reproducing population) or "positive" (zebra mussels or their larvae have been detected on more than one occasion despite lack of evidence of a fully established, reproducing population) are indicated by red triangles and included: Lakes Austin, Belton, Bridgeport, Dean Gilbert, Lavon, Lewisville, Ray Roberts, Stillhouse Hollow, Texoma, Travis, and Waco. Sites categorized by TPWD as zebra mussel "negative" are indicated by green circles and included: Lakes Aquilla, Buchanan, Georgetown, Granbury, Granger, Hubbard Creek, Inks, Lady Bird, LBJ, Limestone, Marble Falls, Palo Pinto, Pflugerville, Possum Kingdom, Proctor, and Whitney.
Figure 4 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 4. Biplot of components 1 and 2 (top) and 1 and 3 (bottom) from Principal Component Analysis of water quality variables in 27 study lakes. Variables that predominated in each component (|factor loading| ≥ 0.50) are shown on the appropriate axes. Individual lake data are represented by symbols, with open circles representing lakes without previously reported incidences of zebra mussels (absent, 16 lakes), and solid circles those known to harbor the invasive species (present, 11 lakes) at the time of sampling (October 2016). No separation between the two lake groups is evident in either of the biplots. Ca, calcium, N, nitrogen; P, phosphorous.
Figure 3 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics
Figure 3. Maxent predictions of suitable quagga mussel (Dreissena bugensis) habitat in Texas. Shading indicates the logistic output of the Maxent model. Polygons represent state and national borders as well as major river basins within Texas.
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</p>
Data for "Sounding out Ecoacoustic Metrics: Avian species richness is predicted by acoustic indices in temperate but not tropical habitats"
<p>This deposit contains the data for the paper <strong>A Multi-habitat, Comparative Evaluation of Ecoacoustic Indices for Biodiversity Monitoring: Acoustic Indices Predict Avian Species Richness in Temperate but not Tropical Habitats. (Ecological Indicators) </strong>The dataset contains a series of 1 min wav files recorded across UK and Ecuadorian habitats. Each one has 26 acoustic indices calculated on it, and a full list of avian species and abundances and GPS data for each sample site.</p> <p>Abstract</p> <p>Affordable, autonomous recording devices facilitate large scale acoustic monitoring and Rapid Acoustic Survey is emerging as a cost-effective approach to ecological monitoring; the success of the approach rests on the development of computational methods by which biodiversity metrics can be automatically derived from remotely collected audio data. Dozens of indices have been proposed to date, but systematic validation against classical, in situ diversity measures. This study conducted the most comprehensive comparative evaluation to date of the relationship between avian species diversity and a suite of acoustic indices across a wide range of ecological conditions. Acoustic surveys were carried out across habitat gradients in temperate and tropical biomes. Baseline avian species richness and subjective multi-taxa biophonic density estimates were established through aural counting by expert ornithologists. 26 acoustic indices were calculated and compared to observed variations in species diversity. Five acoustic diversity indices (Bioacoustic Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy, and the Normalised Difference Sound Index) were assessed as well as three simple acoustic descriptors (root-mean-square, spectral centroid and zero-crossing rate). Highly significant correlations, of up to 65%, between acoustic indices and avian species richness were observed across temperate habitats, supporting the use of automated acoustic indices in biodiversity monitoring where a single vocal taxon dominates. Significant, weaker correlations were observed in neotropical habitats which host multiple non-avian vocalizing species. Multivariate classification analyses suggest that AIs also track observed differences in habitat-dependent community composition and that each habitat has a distinct soundscape. Multivariate analyses of the relative predictive power of AIs show that compound indices are more powerful predictors of avian species richness than any single index and simple descriptors contribute to predicting avian diversity in multi-taxa tropical environments. Our results support the use of community level acoustic indices as a proxy for species richness and point to the potential for tracking of habitat-dependent changes in community composition. Recommendations for the design of compound indices for multi-taxa community composition appraisal are put forward, with consideration for the requirements of next generation, low power remote monitoring networks.</p> <p> </p> <p><strong>Sampling Methods (extract from paper)</strong></p> <p>Acoustic surveys were carried out along a gradient of habitat degradation (1 forested, 2 regenerating forest and 3 agricultural land) in South East (SE) England and North Western (NW) Ecuador. The six sites (UK1, UK2, UK3, EC1, EC2, EC3) were sampled consecutively from May 6th - Aug 25th 2015.</p> <p>All UK sites were in the county of Sussex, in SE England, an area of weald clays (Fig. 2, left) and included ancient woodland (UK1), regenerating farmland with patches of woodland (UK2) and a downland barley farm (UK3).1 min mono audio recordings made every 15 minutes at three different habitats in the UK</p> <p>Ten day acoustic surveys were carried out consecutively at each study site using 15 Wildlife Acoustics Song Meter audio field recorders. Sampling points were arranged in a grid at a minimum distance of 200 m to minimise pseudo replication (the sound of most species being attenuated over this distance in all biomes). Altitudinal range of sample points across sites was minimised in order to prevent introduction of extraneous, confounding gradients (UK varied between 10 m – 50 m and Ecuador 130 m – 390 m). Recording schedules captured 1 min every 15 min around the clock for 10 days at each site, resulting in 960 recordings at each of 15 sample points for 3 habitat types in 2 different climates (86,400 1 minute recordings in total). Data across the 15 sample points was pooled; inter-site variation was not explored in the current analyses. In the UK 3½ hours of each dawn chorus was sampled starting at 1 hour before sunrise. This range was determined to capture the onset, progression and peak of the dawn chorus, creating a temporal gradient. The equatorial dawn chorus is more compact and was sampled for 2¼ hours starting 15 mins before sunrise, capturing a comparable chorus onset and peak.</p> <p> </p> <p> </p>
Figure 2. Predicted habitat suitability classification for N in Current and suitable habitat of the Critically endangered Northern white-cheeked gibbon (Nomascus leucogenys) in Lao PDR
Figure 2. Predicted habitat suitability classification for N. leucogenys (A, D) 2022, (B, E) 2050 and (C, F) 2070.
Airflow modelling predicts seabird breeding habitat across islands
<p>Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots (<em>Uria aalge</em>) breeding on Skomer island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/ wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions, and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions.</p>
Data for: Predicting habitat suitability for Townsend's big-eared bats across California in relation to climate change
<p>Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend's big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions.</p> <p>Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species' distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts.</p> <p>Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species' responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions.</p>
Fig. 4. Predicted suitable habitats for S in Discovery of a new crocodile lizard population in Vietnam: Population trends, future prognoses and identification of key habitats for conservation
Fig. 4. Predicted suitable habitats for S. crocodilurus in the period between 2020 to 2080, based on bioclimatic data and elevation. Habitat suitability increases from yellow to dark brown.
PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS.
<p>This is the data for "PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS."</p>
Expanded distribution and predicted suitable habitat for the critically endangered yellow-tailed woolly monkey (Lagothrix flavicauda) in Peru
<p><span>The Tropical Andes Biodiversity Hotspot holds a remarkable number of species at risk of extinction due to anthropogenic habitat loss, hunting and climate change. One of these species, the Critically Endangered yellow-tailed woolly monkey (<em>Lagothrix flavicauda</em>), was recently sighted in Junín region, 206 kilometres south of its previously known distribution. The range extension, combined with continued habitat loss, calls for a re-evaluation of the species' distribution and available suitable habitat. Here, we present novel data from surveys at 53 sites in the regions of Junín, Cerro de Pasco, Ayacucho and Cusco. We encountered <em>L. flavicauda </em>at 9 sites, all in Junín, and the congeneric <em>L. l. tschudii</em> at 20 sites, but never in sympatry. Using these new localities along with all previous geographic localities for the species, we made predictive Species Distribution Models based on Ecological Niche Modelling using a generalized linear model and maximum entropy. Each model incorporated bioclimatic variables, forest cover, vegetation measurements, and elevation as predictor variables. Model evaluation showed >80% accuracy for all measures. Precipitation was the strongest predicter of species presence. Habitat suitability maps illustrate potential corridors for gene flow between the southern and northern populations, although much of this area is inhabited by <em>L. l. tschudii</em>. An analysis of the current protected area (PA) network showed ~47% of remaining suitable habitat is unprotected. With this, we suggest priority areas for new protected areas or expansions to existing reserves that would conserve potential corridors between <em>L. flavicauda</em> populations. Further surveys and characterization of the distribution in intermediate areas, combined with studies on genetic flow, are still needed to protect this species.</span></p>
Transgenerational effect on sexual reproduction in rotifer populations in relation to the environmental predictability of their habitats
<p>Understanding the processes that enable adaptation of organisms to time-varying environments is critically relevant in evolutionary ecology. A way to cope with environmental fluctuations where predictable conditions affect several generations of individuals is through non-genetic transgenerational effects. The phenotype of ancestors affects the phenotype of their descendants matching it with the expected environment of the latter. Facultatively sexual rotifers inhabiting water bodies that cover a wide gradient of environmental predictability in Eastern Spain are a good study model for this topic. In their life cycle sex is linked to diapausing-egg production that enables survival between growing seasons. In several rotifer species, sexual reproduction is inhibited in several generations after diapausing-egg hatching. We hypothesized that in ponds where the growing season length is more predictable, rotifer clones proliferate asexually longer, hence allowing a fuller exploitation of the growing season and therefore maximize diapausing-egg production by the end of the season. We tested this prediction by estimating the proportion of sexual females produced by eight clones of the rotifer <em>Brachionus plicatilis</em> inhabiting eight ponds (8x8= 64 clones) from our study system. Here, we present the raw data gathered from the experiment. </p>
ScienceDex guides
Understand access before you commit
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.