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29 results for “environmental suitability”
Data from: Is there a correlation between abundance and environmental suitability derived from ecological niche modelling? A meta-analysis
It is thought that species abundance is correlated with environmental suitability and that environmental variables, scale, and type of model fitting can confound this relationship. We performed a meta-analysis to (i) test whether species abundance is positively correlated with environmental suitability derived from correlative ecological niche models (ENM), (ii) test whether studies encompassing large areas within a species range (>50%) exhibited higher AS correlations than studies encompassing small areas within a species range (<50%), (iii) assess which modelling method provided higher AS correlation, and (iv) compare strength of the AS relationship between studies using only climatic variables and those that used both climatic and other environmental variables to derive suitability. We used correlation coefficients to measure the relationship between abundance and environmental suitability derived from ENM. Each correlation coefficient was considered an effect size in a random-effects multivariate meta-analysis. In all cases we found a significantly positive relationship between abundance and suitability. This relationship was consistent regardless of scale of study, ENM method, or set of variables used to derive suitability. There was no difference in strength of correlation between studies focusing on large or small areas within a species' range or among ENM methods. Studies using other variables in combination with climate exhibited higher AS correlations than studies using only climatic variables. We conclude that occurrence data can be a reasonable proxy for abundance, especially for vertebrates, and the use of local variables increases the strength of the AS relationship. Use of ENMs can significantly decrease survey costs and allow the study of large-scale abundance patterns using less information. Including only climatic variables in ENM may confound the relationship between abundance and suitability when compared to studies including variables taken locally. However, modelers and conservationists must be aware that high environmental suitability does not always indicate high abundance.
Data from: Estimating environmental suitability
Methods for modeling species' distributions in nature are typically evaluated empirically with respect to data from observations of species occurrence and, occasionally, absence at surveyed locations. Such models are relatively "theory‐free." In contrast, theories for explaining species' distributions draw on concepts like fitness, niche, and environmental suitability. This paper proposes that environmental suitability be defined as the conditional probability of occurrence of a species given the state of the environment at a location. Any quantity that is proportional to this probability is a measure of relative suitability and the support of this probability is the niche. This formulation suggests new methods for presence‐background modeling of species distributions that unify statistical methodology with the conceptual framework of niche theory. One method, the plug‐and‐play approach, is introduced for the first time. Variations on the plug‐and‐play approach were studied with respect to their numerical performance on 106 species from an exhaustively sampled presence–absence survey of vegetation in the Canton of Vaud, Switzerland. Additionally, we looked at the robustness of these methods to the presence of irrelevant information and sample size. Although irrelevant variables eroded the predictive performance of all methods, these methods were found to be both numerically and statistically robust.
Data from: Correlation between genetic diversity and environmental suitability: taking uncertainty from ecological niche models into account
The hindcast of shifts in the geographical ranges of species as estimated by ecological niche modelling (ENM) has been coupled with phylogeographical patterns, allowing the inference of past processes that drove population differentiation and genetic variability. However, more recently, some studies have suggested that maps of environmental suitability estimated by ENM may be correlated to species' abundance, raising the possibility of using environmental suitability to infer processes related to population demographic dynamics and genetic variability. In both cases, one of the main problems is that there is a wide variation in ENM development methods and climatic models. In this study, we analyse the relationship between heterozygosity (He) and environmental suitability from multiple ENMs for 25 population estimates for Dipteryx alata, a widely distributed, endemic tree species of the Cerrado region of central Brazil. We propose a new approach for generating a statistical distribution of correlations under randomly generated ENM. The confidence intervals from these distributions indicate how model selection with different properties affects the ability to detect a correlation of interest (e.g. the correlation between He and suitability). Additionally, our approach allows us to explore which particular ensemble of ENMs produces the better result for finding an association between environmental suitability and He. Caution is necessary when choosing a method or a climatic data set for modelling geographical distributions, but the new approach proposed here provides a conservative way to evaluate the ability of ensembles to detect patterns of interest.
Projections of spatial distributions of suitable environmental conditions for key Baltic Sea zooplankton species - Data
<p>This repository contains seven occurrences dataset which represent the station where the species have been identified, ranging from 2000 to 2020. The environmental projections for the period 2010-2020, as well as future projection on two horizons: from 2040 to 2050 and from 2090 to 2100 on two different scenarios: SSP245 and SSP585. The occurrences have been exctracted from OBIS (https://obis.org) and the environmental projections from Bio-ORACLE (https://bio-oracle.org).</p> <p> </p> <p>Occurrences datasets: </p> <ul> <li><em>Temora longicornis</em></li> <li><em>Centropages hamatus</em></li> <li><em>Limnocalanus macrurus macrurus</em></li> <li><em>Evadne nordmanni</em></li> <li><em>Acartia tonsa</em></li> <li><em>Acartia longiremis</em></li> <li><em>Acartia bifilosa</em></li> </ul> <p> </p> <p>Projections:</p> <ul> <li>Projection Baseline 2010-2020</li> <li>Projection 2040-2050 SSP245</li> <li>Projection 2090-2100 SSP245</li> <li>Projection 2040-2050 SSP585</li> <li>Projection 2090-2100 SSP585</li> </ul> <p> </p> <p> </p> <p> </p>
Data from: Correlation between genetic diversity and environmental suitability: taking uncertainty from ecological niche models into account
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Data from: Is there a correlation between abundance and environmental suitability derived from ecological niche modelling? A meta-analysis
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Data from: Estimating environmental suitability
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Global Habitat Suitability for Framework-Forming Cold-Water Corals - Environmental Data
<p>Environmental data layers used in the production of global habitat suitability models for several species of Scleractinian corals from the publication:</p> <p><strong>Davies, A.J. & Guinotte, J.M. (2011) Global Habitat Suitability for Framework-Forming Cold-Water Corals. PLoS ONE 6(4): e18483. doi:10.1371/journal.pone.0018483</strong></p> <p>Files are ASCII raster layers, zipped using 7zip. Each raster has a WGS 1984 coordinate system.</p> <p>Brief methodology and outline of variables provided are presented below, for full details refer to the above manuscript in PLoS ONE:</p> <p><strong><em>Terrain variables</em></strong></p> <p>Several terrain attributes were extracted from the SRTM30 data (Table 1) following techniques and algorithms described in Wilson et al (2007). Individual approaches are described within the footnote of Table 1, however, briefly the extraction process and description of each variable is described here. Bathymetric position index (BPI) is an approach to determine topographical features based on their relative position within a neighbourhood, and can be calculated over fine or broad scales to capture smaller or larger terrain features respectively. This calculation has been developed into an ArcGIS tool by Wright et al. (2005). Slope was calculated using DEM Tools for ArcGIS developed by Jenness (2012), in particular the 4-cell method of calculating slope, which is accepted as the most accurate approach (Jones 1998). In this manuscript, slope is defined as the gradient in the direction of the maximum slope. Curvature attempts to describe terrain features and may provide an indication of how water would interact with the terrain. In this manuscript, plan and tangential curvature can describe how water would converge or diverge as it flows over relief, whilst profile curvature describes how water would accelerate or decelerate as it flows over relief (Jenness 2012). Aspect is defined as the direction of maximum slope and was converted to continuous radians following Wilson et al. (2007). Rugosity, terrain ruggedness index and roughness all generally describe the variability of the relief of the seafloor (Wilson et al. 2007). Rugosity is defined as the ratio of the surface area to the planar area across a neighbourhood of a central pixel (Jenness 2012). Terrain ruggedness index is defined as the mean difference between a central pixel and its surrounding cells and roughness which is the largest inter-cell difference of a central pixel and its surrounding cell (Wilson et al. 2007).</p> <p><strong><em>Environmental variables</em></strong></p> <p>Environmental variables were created using the variable up-scaling approach presented within (Davies & Guinotte 2011). This approach takes gridded layers of an environmental variable and drapes them over bathymetry to provide an indication of conditions near the seabed, it has been proven to work well over global and regional scales (Davies & Guinotte 2011, Guinotte & Davies 2012). In this manuscript, these environmental layers must be considered as representations of general conditions, as likely, the highly variable topography of the canyon will not yield a good prediction of environmental variables at such a small spatial scale. Limited CTD profiles were collected using a SeaBird 911+, collating data for turbidity (Seapoint, formazin turbidity units), dissolved oxygen (mg L<sup>-1</sup>), depth (m), conductivity (Siemens/m), temperature (°C), salinity, and pH. These casts were compared with the modelled layers (specifically dissolved oxygen, salinity and temperature) to determine their relative accuracy for certain areas of the seafloor</p> <p><strong>Variable name // Filename // Units // Reference (see original paper)</strong></p> <p>Terrain Variables</p> <p>Aspect // aspect // Degree // Jenness (2012)<br> Aspect – Eastness // eastness // // Wilson et al. (2007)<br> Aspect – Northness // northness // // Wilson et al. (2007)<br> Bathymetry // srtm30 // m // Becker et al. (2009)<br> Curvature – Profile // profilecurve // // Jenness (2012)<br> Curvature – Plan // plancurve // // Jenness (2012)<br> Curvature – Tangential // tangcurve // // Jenness (2012)<br> Roughness // roughness // // Wilson et al. (2007)<br> Rugosity // rugosity // // Jenness (2012)<br> Slope // slope // Degrees // Jenness (2012)<br> Terrain Ruggedness Index // tri // // Wilson et al. (2007)<br> Topographic Position Index // tpi // // Wilson et al. (2007) </p> <p>Environmental variables<br> Alkalinity // alk_stein // μmol l-1 // Steinacher et al. (2009)<br> Apparent oxygen utilisation // woaaoxu // mol O2 m-3 // Garcia et al. (2006b)<br> Chlorophyll a // modismin, modismean, modismax // mg m-3 // NASA Ocean Color<br> Dissolved inorganic carbon // dic_stein // μmol l-1 // Steinacher et al. (2009)<br> Dissolved oxygen // woadiso2 // ml l-1 // Garcia et al.(2006a)<br> Nitrate // woanit // μmol l-1 // Garcia et al. (2006b)<br> Omega aragonite // arag_stein // ΩARAG // Steinacher et al. (2009)<br> Omega aragonite // arag_orr // ΩARAG // Orr et al. (2005)<br> Omega calcite // calc_stein // ΩCALC // Steinacher et al. (2009)<br> Omega calcite // calc_orr // ΩCALC // Orr et al. (2005)<br> Percent oxygen saturation // woapoxs // % O2S // Garcia et al. (2006b)<br> Phosphate // woaphos // μmol l-1 // Garcia et al. (2006b)<br> Regional current velocity // regfl // m s-1 // Carton et al. (2005)<br> Salinity // woasal // pss // Boyer et al. (2005)<br> Silicate // woasil // μmol l-1 // Garcia et al. (2006b)<br> Seasonal variation index // lutzsvi // // Lutz et al. (2007)<br> Temperature // woatemp // °C // Boyer et al. (2005)<br> Particulate organic carbon // poc // g Corg m-2 yr-1 // Lutz et al. (2007)<br> Vertical current velocity // vertfl // m s-1 // Carton et al. (2005)<br> Vertically generalised productivity model // vgpmmin, vgpmmean, vgpmmax // mg C m-2 d-1 // Behrenfeld and Falkowski (1997)</p>
Environmental gradients, covering coastal central California, which are relevant to modeling habitat suitability of Deinandra increscens subsp. villosa
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