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214 results for “suitable habitat”
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>
EUNIS habitat suitability maps at 100m resolution
<p>Habitat suitability maps of 203 <a href="https://eunis.eea.europa.eu/">EUNIS</a> level 3 classes have been modeled at 100m resolution with <a href="https://biodiversityinformatics.amnh.org/open_source/maxent/">Maxent</a>. For the modeling plot observation from the <a href="http://euroveg.org/eva-database">European Vegetation Archive</a> have been used as observations for training and testing. As predictors various climate layers, soil layers, topographic layers and RS-enables EBVs have been used. Detailed information on the predictors can be found <a href="https://www.synbiosys.alterra.nl/nextgeoss/docs/Description_Abiotic_and_RSEBVs.pdf">here</a>.</p> <p>The 203 EUNIS habitat types comprise 8 groups according to the revised typology:</p> <ul> <li>Salt marsh (MA)</li> <li>Coastal habitat (N)</li> <li>Wetland (Q)</li> <li>Grassland (R)</li> <li>Shrub (S)</li> <li>Forest (T)</li> <li>Sparsely vegetated habitat (U)</li> <li>Man-made habitat (V)</li> </ul> <p><br> </p> <p> </p> <p> </p> <p> </p>
Fig. 4 in Current and future suitable habitats of a range-restricted species group (Cyrtodactylus chauquangensis) in Vietnam
Fig. 4. The potential distribution of the C. chauquangensis species group under climate change scenarios.
Fig. 3 in Current and future suitable habitats of a range-restricted species group (Cyrtodactylus chauquangensis) in Vietnam
Fig. 3. The response curves of the top three highest contribution predictors for the C. chauquangensis species group model. The red line shows the mean response of 25 replicates in the Maxent model, and the blue band illustrates the standard deviation. A, Karst distance; B, Bio2 ‒ Mean diurnal range; C, Bio 14 ‒ Precipitation of Driest Month.
Fig. 2 in Current and future suitable habitats of a range-restricted species group (Cyrtodactylus chauquangensis) in Vietnam
Fig. 2. The potential distribution of the C. chauquangensis species group under present conditions generated by MaxEnt, A, Minimum training presence logistic threshold; B, 10 percentile training presence logistic threshold.
Predicting the Habitat Suitability of Asian Elephants in Madhesh Landscape of Southern Nepal
<p>This is the dataset used during my thesis entitled " Predicting the Habitat Suitability of Asian Elephants (<em>Elephas maximas</em>) in Madhesh landscape of southern Nepal.</p>
Data from: Large carnivores persisting in a human-dominated landscape: Suitable habitat and connectivity for Asiatic black bears in China
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Data for: Predicting habitat suitability for Townsend’s big-eared bats across California in relation to climate change
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Expanded distribution and predicted suitable habitat for the critically endangered yellow-tailed woolly monkey (Lagothrix flavicauda) in Peru
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Environmental DNA data of aquatic insects for habitat suitability models
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Urbanization drives habitat suitability of the invasive Cuban Knight Anole (<em>Anolis equestris</em>) in Florida, USA
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Habitat suitability and the distribution of species: Polygonatum biflorum demography data from the Coweeta Hydrologic Laboratory from 1998 to 2006
Metapopulation theory posits that suitable habitat may frequently be unoccupied because it is isolated and has never been colonized or has been colonized followed by local extinction and has not yet been recolonized. This research addresses the question of how to identify suitable, unoccupied habitat and distinguish it from unsuitable habitat. We are studying a group of six species of forest understory herbs chosen to represent a broad range of habitat distribution and dispersal characteristics. Our aim is to quantify the fundamental niche of these species (sensu Hutchinson 1957), in terms of variables such as soil moisture and temperature, by developing a set of habitat specific demographic stage transition models (i.e. conditional on such environmental variables) for these species. These models, in combination with data from field surveys of the local distribution of the species, will be used to develop testable predictive maps of the distribution of suitable habitat which can be compared to the observed distribution of the plants. We hypothesize that both dispersal ability and the distribution of suitable habitat are important determinants of the actual distribution of species. The goal of this research is both to further our conceptual understanding of the relationships between habitat requirements and species distributions, and to provide a practical approach to operationalizing the concept of "suitable habitat."
McMurdo Dry Valleys Habitat Suitability - Soil Moisture
Investigation of the variation in soil biota and soil properties across the McMurdo Dry Valleys as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project. The moisture content of soil samples which are collected for organism extraction and identification is determined. This study was carried out in the austral summer 1993/1994
McMurdo Dry Valleys Habitat Suitability - Soil Biota
Investigation of the variation in soil biota and soil properties across the McMurdo Dry Valleys as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project. The identification and abundance of soil biota are reported.
Habitat suitability maps for boreal-breeding passerine species
<p><strong>About the Maps</strong></p> <p>Maps depict model-predicted species distribution and provide information about relative habitat suitability based on current climate and landcover. The suitability ranking of any mapped grid cell is the sum of the probabilities of that grid cell and all other grid cells with equal or lower probability, multiplied by 100 to give a percentage. This value represents the % of grid cells with a lower suitability value within the boreal/hemiboreal study region. Higher value pixels represent higher habitat suitability for a given species. Models do not account for physiographic barriers that may prevent colonization of otherwise suitable habitat, e.g., the Canadian Cordillera (<a href="#_ENREF_4">Erskine 1977</a>). Therefore actual species distributions may be over-estimated in certain regions, particularly in Alaska. </p> <p><strong>Modeling Overview</strong></p> <p>The maximum entropy (Maxent) method (<a href="#_ENREF_9">Phillips et al. 2006</a>, <a href="#_ENREF_10">Phillips and Dudik 2008</a>) was used to develop species distribution models (SDM) for passerine species during the breeding season. Maxent is a powerful machine-learning algorithm with demonstrated high predictive accuracy compared to other SDM methods (<a href="#_ENREF_3">Elith et al. 2006</a>). Although Maxent was developed for presence-only data (e.g., museum records), it is also appropriate for datasets compiled from disparate sources with varying levels of effort, such that information on species absence varies across model units. Although resulting predictions cannot be interpreted as probability of occurrence, they are robust representations of rank-order suitability. The power of Maxent lies in the complexity of relationships (e.g., non-linear, threshold, multiplicative) that it can readily handle, producing detailed, high-accuracy predictions.</p> <p>The key consideration in development of Maxent models, as with traditional resource selection functions (<a href="#_ENREF_8">Manly 1993</a>), is the selection of appropriate “background” data (<a href="#_ENREF_11">Phillips et al. 2009</a>). Otherwise, sample bias can lead to biased predictions. As recommended (<a href="#_ENREF_11">Phillips et al. 2009</a>), we constrained our background to all locations surveyed for birds. Due to high spatial aggregation of survey locations and the resulting potential for bias, we aggregated occurrence records at the level of 4-km grid cells corresponding with the resolution of our climate data. A species was considered present in a grid cell if at least one individual had been counted over all point-count surveys contained in the grid cell. Model background was thus defined as all surveyed 4-km grid cells (n = 29,059).</p> <p><strong>Climate Data</strong></p> <p>Climate variables were derived from 4-km monthly climate normals (1961-1990) based on a combination of PRISM (<a href="#_ENREF_2">Daly et al. 2002</a>) and WorldClim (<a href="#_ENREF_6">Hijmans et al. 2005</a>) climate data. The western North America portion of these data are described in Wang et al. (<a href="#_ENREF_12">2011</a>). We used a set of 17 derived bioclimatic variables presumed to adequately summarize climate conditions within the boreal forest region (Table 1 in report). We were not concerned with high correlation among these covariates because models were developed for prediction purposes only and our goal for this particular exercise was not to interpret the importance of individual covariates.</p> <p><strong>Landcover Data</strong></p> <p>Landcover data consisted of a 2005 classified landcover map of North America developed by the Council on Economic Development (<a href="http://www.cec.org/Page.asp?PageID=924&ContentID=2819">http://www.cec.org/Page.asp?PageID=924&ContentID=2819</a>). We used 15 of the 19 landcover classifications as inputs to bird models (Table 2 in report). Because landcover was mapped at a 250-m resolution, we summarized the proportion of each landcover type within a 4-km grid cell for prediction purposes. For model-building purposes, we summarized landcover proportions according to the distribution of survey locations within the 4-km grid cell. This was based on the landcover type at the point-count center, reflecting the dominant type surveyed.</p> <p><strong>Avian Data</strong></p> <p>Distribution models were developed for all passerine species (+ 2 non-passerine landbird species) with at least 100 occurrence records in separate 4-km grid cells (n=94). Avian occurrence records were obtained from two major datasets: (1) the Boreal Avian Modelling (BAM) point-count dataset (Cumming et al. 2010) and the North American Breeding Bird Survey (BBS) point-count dataset from USGS (<a href="http://www.pwrc.usgs.gov/bbs/">http://www.pwrc.usgs.gov/bbs/</a>). Due to large discrepancies in survey characteristics and species detectability, which have already been addressed for the purpose of density estimation (Sólymos et al. 2013), the focus here was on the occurrence portion of the dataset only. Future efforts will integrate detectability offsets into bioclimatic density models that can be used to generate regional abundance estimates.</p> <p>In order to improve model predictive power within the boreal forest region, data from neighboring hemiboreal regions were also incorporated (as well as data from arctic and mountain regions where possible). Because the core BAM dataset is largely restricted to the boreal forest region, ancillary data consisted primarily of point-level BBS data (breeding bird atlas datasets were notable exceptions). BBS data were obtained for the level 3 ecoregions (<a href="http://www.epa.gov/wed/pages/ecoregions/na_eco.htm#Level III">http://www.epa.gov/wed/pages/ecoregions/na_eco.htm#Level III</a>) that intersected the boundary of the combined Brandt (<a href="#_ENREF_1">2009</a>) boreal/hemiboreal boundary. This additional data improved coverage of climate and landcover conditions at species’ range limits, thereby providing more opportunities to detect differences in habitat suitability. A total of 117,179 point-count locations were used to summarize species occurrence within 29,059 surveyed grid cells. See Table 3 in report for numbers of individual species occurrence records.</p> <p><strong>Maxent Model Details and Accuracy Assessment</strong></p> <p>Models were developed using Maxent version 3.3.3e. We used the cumulative probability output format, allowed all feature types except hinge features, and used a regularization multiplier of 1. We ran the model 10 times using bootstrapped subsamples of the BAM/BBS dataset, each time holding out a random 50% for validation purposes (test data). Model predictions were averaged across the 10 bootstrap replicates. Although models were developed using data from outside of the Brandt boreal/hemiboreal boundary, predictions were constrained to this region..</p> <p>The accuracy of each model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic plot (<a href="#_ENREF_5">Fielding and Bell 1997</a>) based on test data. AUC values were also averaged across the 10 replicates. The AUC value can be interpreted as the likelihood that a randomly-selected presence location will have a higher suitability score than a randomly-selected background location. </p> <p>In general, models were reasonably accurate in their prediction of species’ distributions. Average AUC scores ranged from 0.56 for American Robin to 0.97 for American Tree Sparrow (Table 3). Across all 94 species, AUC scores averaged 0.81 ±0.09 (SD). Models for 31 species were considered acceptable 0.7 ≤ AUC < 0.8), 31 were excellent (0.8 ≤ AUC < 0.9), and 17 had outstanding discrimination ability (AUC ≥ 0.9) (<a href="#_ENREF_7">Hosmer and Lemeshow 1989</a>). AUC scores reflected the ability to discriminate among different levels of habitat suitability within the greater boreal/hemiboreal region. Thus, species with distinct range limits within this region were more accurately predicted.</p> <p><strong>NatureServe Comparisons</strong></p> <p>Models and occurrence records were overlaid with published range maps from NatureServe (<a href="http://datazone.birdlife.org/species/requestdis">http://datazone.birdlife.org/species/requestdis</a>) for comparison purposes. For all but three species, the BAM/BBS dataset contained occurrence records outside of NatureServe range map limits. This discrepancy is reflected in the Maxent model predictions. Thus, both occurrence records and model predictions may be used to refine the range limits for several species. All but nine species had data observations north of their published range limits. Although better range maps may exist for many species (e.g., in recently revised Birds of North America volumes, <a href="http://bna.birds.cornell.edu/bna/">http://bna.birds.cornell.edu/bna/</a>), digital versions are not generally available for comparison.</p> <p><strong>Literature Cited</strong></p> <p>Brandt, J. P. 2009. The extent of the North American boreal zone. Environmental Reviews <strong>17</strong>:101–161.</p> <p>Cumming, S. G., K. L. Lefevre, E. Bayne, T. Fontaine, F. K. A. Schmiegelow, and S. J. Song. 2010. Toward conservation of Canada's boreal forest avifauna: design and application of ecological models at continental extents. Avian Conservation and Ecology<strong> 5</strong>(2):8.</p> <p>Daly, C., W. P. Gibson, G. H. Taylor, G. L. Johnson, and P. Pasteris. 2002. A knowledge-based approach to the statistical mapping of climate. Climate Research <strong>22</strong>:99-113.</p> <p>Elith, J., C. H. Graham, R. P. Anderson, M. Dudik, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J. McC. M. Overton, A. Townsend Peterson, S. J. Phillips, K. Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberón, S. Williams, M. S. Wisz, and N. E. Zimmermann. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography <strong>29</strong>:129-151.</p> <p>Erskine, A. J. 1977. Birds in boreal Canada: communities, densities, and adaptations. Ottawa, Canada.</p> <p>Fielding, A. H. and J. F. Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation <strong>24</strong>:38-49.</p> <p>Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology <strong>25</strong>:1965-1978.</p> <p>Hosmer, D. W. and S. Lemeshow. 1989. Applied logistic regression. John Wiley and Sons, New York.</p> <p>Manly, B. F. J. 1993. Resource Selection by Animals: Statistical Design and Analysis for Field Studies. Chapman and Hall, London.</p> <p>Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling <strong>190</strong>:231-259.</p> <p>Phillips, S. J. and M. Dudik. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography <strong>31</strong>:161-175.</p> <p>Phillips, S. J., M. Dudik, J. Elith, C. H. Graham, A. Lehmann, J. Leathwick, and S. Ferrier. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications <strong>19</strong>:181-197.</p> <p>Sólymos, P., S. M. Matsuoka, E. M. Bayne, S. R. Lele, P. Fontaine, S. G. Cumming, D. Stralberg, F. K. A. Schmiegelow, and S. J. Song. 2013. Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. Methods in Ecology and Evolution <strong>4</strong>:1047-1058.</p> <p>Wang, T., A. Hamann, D. L. Spittlehouse, and T. Q. Murdock. 2011. ClimateWNA-High-Resolution Spatial Climate Data for Western North America. Journal of Applied Meteorology and Climatology <strong>51</strong>.</p> <p> </p> <p> </p>
Data from: Disentangling the effects of geographic peripherality and habitat suitability on neutral and adaptive genetic variation in Swiss stone pine
<p><span><span><span><span><span><span><span><span><span><span><span>It is generally accepted that the spatial distribution of neutral genetic diversity within a species' native range mostly depends on effective population size, demographic history, and geographic position. However, it is unclear how genetic diversity at adaptive loci correlates with geographic peripherality or with habitat suitability within the ecological niche. Using exome-wide genomic data and distribution maps of the Alpine range, we first tested whether geographic peripherality correlates with four measures of population genetic diversity at >17,000 SNP loci in 24 Alpine populations (480 individuals) of Swiss stone pine (<i>Pinus cembra</i>) from Switzerland. To distinguish between neutral and adaptive SNP sets, we used four approaches (two gene diversity estimates, <i>F</i><sub>ST</sub> outlier test, and environmental association analysis) that search for signatures of selection. Second, we established ecological niche models for <i>P. cembra</i> in the study range and investigated how habitat suitability correlates with genetic diversity at neutral and adaptive loci. All estimates of neutral genetic diversity decreased with geographic peripherality, but were uncorrelated with habitat suitability. However, heterozygosity (<i>H</i><sub>e</sub>) at adaptive loci based on Tajima's <i>D</i> declined significantly with increasingly suitable conditions. No other diversity estimates at adaptive loci were correlated with habitat suitability. Our findings suggest that populations at the edge of a species' geographic distribution harbour limited neutral genetic diversity due to demographic properties. Moreover, we argue that populations from suitable habitats went through strong selection processes, are thus well adapted to local conditions, and therefore exhibit reduced genetic diversity at adaptive loci compared to populations at niche margins.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Testing range-limit hypotheses using range-wide habitat suitability and occupancy for the scarlet monkeyflower (Erythranthe cardinalis)
Determining the causes of geographic range limits is a fundamental problem in ecology, evolution, and conservation biology. Range limits arise due to fitness and dispersal limitation, which yield contrasting predictions about habitat suitability and occupancy of suitable habitat across geographic ranges. If a range edge is limited primarily by fitness, occupancy of suitable habitat should be high, habitat suitability should decline towards the edge, and no suitable habitat should exist beyond it. In contrast, a range edge limited primarily by dispersal should have unoccupied but suitable habitat at and beyond the edge. We built ecological niche models relating occurrence records for the scarlet monkeyflower (Erythranthe cardinalis) to climatic variables, and applied these models to independent data from systematic, range-wide surveys of presence and absence to estimate the availability and occupancy of climatically suitable habitat. We found that fitness limitation predominated over dispersal limitation, but dispersal limitation also played a role at the poleward edge. These results are consistent with the hypothesis that dispersal limitation is more important along shallow environmental gradients and also suggest that synergy between dispersal and fitness limitation can contribute to colonization failure. The framework used here is validated by independent data and could be readily applied to inferring causes of range limits in many other species.
Image 2 in Habitat suitability, threats and conservation strategies of Hump-nosed Pit Viper Hypnale hypnale Merrem (Reptilia: Viperidae) found in Western Ghats, Goa, India
Image 2. Gravid female killed in cashew plantation during weed clearance
Figure 1 in Habitat suitability, threats and conservation strategies of Hump-nosed Pit Viper Hypnale hypnale Merrem (Reptilia: Viperidae) found in Western Ghats, Goa, India
Figure 1. Distribution of Hypnale hypnale in the study sites and cashew plantations.
Image 1 in Habitat suitability, threats and conservation strategies of Hump-nosed Pit Viper Hypnale hypnale Merrem (Reptilia: Viperidae) found in Western Ghats, Goa, India
Image 1. Hypnale hypnale in natural habitat
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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.