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147 results for “environmental correlations”
Data from: Temporal scale of environmental correlations affects ecological synchrony
Population densities of a species, measured in different locations are often correlated over time, a phenomenon referred to as synchrony. Synchrony results from dispersal of individuals among locations and spatially correlated environmental variation, among other causes. Synchrony is often measured by a correlation coefficient. However, synchrony can vary with timescale. We demonstrate theoretically and experimentally that the timescale-specificity of environmental correlation affects the overall magnitude and timescale-specificity of synchrony, and that these effects are modified by population dispersal. Our laboratory experiments linked populations of flour beetles by changes in habitat size and dispersal. Linear filter theory, applied to a metapopulation model for the experimental system, predicted the observed timescale-specific effects. The timescales at which environmental covariation occurs can affect the population dynamics of species in fragmented habitats.
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: Oceanographic connectivity and environmental correlates of genetic structuring in Atlantic herring in the Baltic Sea
Marine fish often show little genetic structuring in neutral marker genes, and Atlantic herring (Clupea harengus) in the Baltic Sea are no exception; historically, very low levels of population differentiation (FST ≈ 0.002) have been found, despite a high degree of interpopulation environmental heterogeneity in salinity and temperature. Recent exome sequencing and SNP studies have however shown that many loci are under selection in this system. Here, we combined population genetic analyses of a large number of transcriptome-derived microsatellite markers with oceanographic modelling to investigate genetic differentiation and connectivity in Atlantic herring at a relatively fine scale within the Baltic Sea. We found evidence for weak but robust and significant genetic structuring (FST = 0.008) explainable by oceanographic connectivity. Genetic differentiation was also associated with site differences in temperature and salinity, with the result driven by the locus Her14 which appears to be under directional selection (FST = 0.08). The results show that Baltic herring are genetically structured within the Baltic Sea, and highlight the role of oceanography and environmental factors in explaining this structuring. The results also have implications for the management of herring fisheries, the most economically important fishery in the Baltic Sea, suggesting that the current fisheries management units may be in need of revision.
Data from: A meta-analysis reveals a positive correlation between genetic diversity metrics and environmental status in the long-lived seagrass Posidonia oceanica
The seagrass Posidonia oceanica is a key engineering species structuring coastal marine systems throughout much of the Mediterranean basin. Its decline is of concern, leading to the search for short- and long-term indicators of seagrass health. Using ArcGIS maps from a recent, high-resolution (1–4 km) modelling study of 18 disturbance factors affecting coastal marine systems across the Mediterranean (Micheli et al. 2013, http://globalmarine.nceas.ucsb.edu/mediterranean/), we tested for correlations with genetic diversity metrics (allelic diversity, genotypic/clonal diversity and heterozygosity) in a meta-analysis of 56 meadows. Contrary to initial predictions, weak but significantly positive correlations were found for commercial shipping, organic pollution (pesticides) and cumulative impact. This counterintuitive finding suggests greater resistance and resilience of individuals with higher genetic and genotypic diversity under disturbance (at least for a time) and/or increased sexual reproduction under an intermediate disturbance model. We interpret the absence of low and medium levels of genetic variation at impacted locations as probable local extinctions of individuals that already exceeded their resistance capacity. Alternatively, high diversity at high-impact sites is likely a temporal artefact, reflecting the mismatch with pre-environmental impact conditions, especially because flowering and sexual recruitment are seldom observed. While genetic diversity metrics are a valuable tool for restoration and mitigation, caution must be exercised in the interpretation of correlative patterns as found in this study, because the exceptional longevity of individuals creates a temporal mismatch that may falsely suggest good meadow health status, while gradual deterioration of allelic diversity might go unnoticed.
Data from: Evolutionary processes and its environmental correlates in the cranial morphology of western chipmunks (Tamias)
The importance of the environment in shaping phenotypic evolution lies at the core of evolutionary biology. Chipmunks of the genus Tamias (subgenus Neotamias) are part of a very recent radiation, occupying a wide range of environments with marked niche partitioning among species. One open question is if and how those differences in environments affected phenotypic evolution in this lineage. Herein we examine the relative importance of genetic drift versus natural selection in the origin of cranial diversity exhibited by clade members. We also explore the degree to which variation in potential selective agents (environmental variables) are correlated with the patterns of morphological variation presented. We found that genetic drift cannot explain morphological diversification in the group, thus supporting the potential role of natural selection as the predominant evolutionary force during Neotamias cranial diversification, although the strength of selection varied greatly among species. This morphological diversification, in turn, was correlated with environmental conditions, suggesting a possible causal relationship. These results underscore that extant Neotamias represent a radiation in which aspects of the environment might have acted as the selective force driving species' divergence.
Data from: Fish functional traits correlated with environmental variables in a temperate biodiversity hotspot
The global biodiversity crisis has invigorated the search for generalized patterns in most disciplines within the natural sciences. Studies based on organismal functional traits attempt to broaden implications of results by identifying the response of functional traits, instead of taxonomic units, to environmental variables. Determining the functional trait responses enables more direct comparisons with, or predictions for, communities of different taxonomic composition. The North American freshwater fish fauna is both diverse and increasingly imperiled through human mediated disturbances, including climate change. The Tennessee River, USA, contains one of the most diverse assemblages of freshwater fish in North America and has more imperiled species than other rivers, but there has been no trait-based study of community structure in the system. We identified 211 localities in the upper Tennessee River that were sampled by the Tennessee Valley Authority between 2009 and 2011 and compiled fish functional traits for the observed species and environmental variables for each locality. Using fourth corner analysis, we identified significant correlations between many fish functional traits and environmental variables. Functional traits associated with an opportunistic life history strategy were correlated with localities subject to greater land use disturbance and less flow regulation, while functional traits associated with a periodic life history strategy were correlated with localities subject to regular disturbance and regulated flow. These are patterns observed at the continental scale highlighting the generalizability of trait-based methods. Contrary to studies that found no community structure differences when considering riparian buffer zones, we found that fish functional traits were correlated with different environmental variables between analyses with buffer zones vs. entire catchment area land cover proportions. Using existing databases and fourth corner analysis, our results support the broad application potential for trait-based methods and indicate trait-based methods can detect environmental filtering by riparian zone land cover.
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.
Data from: Effects of dispersal mode on the environmental and spatial correlates of nestedness and species turnover in pond communities
Advances in metacommunity theory have made a significant contribution to understanding the drivers of variation in biological communities. However, there has been limited empirical research exploring the expression of metacommunity theory for two fundamental components of beta diversity: nestedness and species turnover. In this paper, we examine the influence of local environmental and a range of spatial variables (hydrological connectivity, proximity and overall spatial structure) on total beta diversity and the nestedness and turnover components of beta diversity for the entire macroinvertebrate community and active and passively dispersing taxa within pond habitats. High beta diversity almost entirely reflects patterns of species turnover (replacement) rather than nestedness (differences in species richness) in our dataset. Local environmental variables were the main drivers of total beta diversity, nestedness and turnover when the entire community was considered and for both active and passively dispersing taxa. The influence of spatial processes on passively dispersing composition, total beta diversity and nestedness was significantly greater than for actively dispersing taxa. Our results suggest that species sorting (local environmental variables) operating through niche processes was the primary mechanism driving total beta diversity, nestedness and turnover for the entire community and active and passively dispersing taxa. In contrast, spatial factors (hydrological connectivity, proximity and spatial eigenvectors) only exerted a secondary influence on the nestedness and turnover components of beta diversity.
Data from: Do correlated responses to multiple environmental changes exacerbate or mitigate species loss?
Biological communities face multiple global changes simultaneously, and predicting how they will respond remains a key challenge. Co-tolerance theory offers a framework for understanding how species-level responses to multiple stressors affect community properties. Co-tolerance theory predicts that positive correlations in species responses (i.e., species that are susceptible to one stressor are more likely to be highly susceptible to a second) lessen total species loss, essentially because species cannot be eliminated from a community twice. However, it is unclear whether several of the tenets of co-tolerance theory describe real-world communities, and what consequences result from such deviations. Here, we use an empirical dataset of bird community response to land-use change over a climate gradient to examine co-tolerance theory's tenet that environmental changes only harm species (not benefit them). We show that this tenet is not met, and then use simulations to examine how predictions of total species richness and community intactness vary when multiple environmental changes both harm and benefit particular species in the community. Finally, we conduct a sensitivity analysis, examining how the average species response to environmental change, as well as the variance among species, can further alter predictions. Overall, we find that predictions of co-tolerance theory can break down when communities contain species that benefit from some environmental changes. As a result, the presence of multiple environmental changes can either compound or mitigate species loss when species' responses are positively correlated, preventing a one-size-fits-all statement regarding the effects of correlated responses. This finding highlights the need to carefully consider the underlying mechanisms of community change when making policy assessments regarding the consequences of correlations of species responses to environmental impacts.
FIGURE 9 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 9. Non-stationary associations between ecological diversity (ED) and standard deviation in altitude (ALTstd). The maps show the spatial variation in local beta coefficients (b) for ALTstd as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equalarea projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 5 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 5. Non-stationary associations between ecological diversity (ED) and net primary productivity (NPP). The maps show the spatial variation in local beta coefficients (b) for NPP as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 4 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 4. Non-stationary associations between ecological diversity (ED) and mean annual temperature (TEMP). The maps show the spatial variation in local beta coefficients (b) for TEMP as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of tropics in the Northern and Southern Hemispheres.
FIGURE 8 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 8. Non-stationary associations between ecological diversity (ED) and coefficient of variation in annual precipitation (PRECcv). The maps show the spatial variation in local beta coefficients (b) for PRECcv as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 7 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 7. Non-stationary associations between ecological diversity (ED) and annual range in temperature (TEMPr). The maps show the spatial variation in local beta coefficients (b) for TEMPr as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equalarea projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 1 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 1. Spatial patterns of variation in the ecological diversity (ED) of different mammal groups over the Americas. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 14 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 14. Non-stationary associations between phylogenetic diversity (AvPD) and coefficient of variation in annual precipitation (PRECcv). The maps show the spatial variation in local beta coefficients (b) for PRECcv as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 6 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 6. Non-stationary associations between ecological diversity (ED) and annual precipitation (PREC). The maps show the spatial variation in local beta coefficients (b) for PREC as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of tropics in the Northern and Southern Hemispheres.
FIGURE 13 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 13. Non-stationary associations between phylogenetic diversity (AvPD) and annual range in temperature (TEMPr). The maps show the spatial variation in local beta coefficients (b) for TEMPr as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 12 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 12. Non-stationary associations between phylogenetic diversity (AvPD) and annual precipitation (PREC). The maps show the spatial variation in local beta coefficients (b) for PREC as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
FIGURE 10 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas
FIGURE 10. Non-stationary associations between phylogenetic diversity (AvPD) and mean annual temperature (TEMP). The maps show the spatial variation in local beta coefficients (b) for TEMP as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.
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Allen Brain Atlas
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