Translating niche features: Modelling differential exposure of Argentine reptiles to global climate change

Global climate change affects the distributions of ectotherms and may be the cause of several conservation problems, such as great displacement of climatic suitable spaces for species and, consequently, important reductions of the extent of liveable places, threatening the existence of many of them. Species exposure (and hence vulnerability) to global climate change is linked to features of their climatic niches (such as the relative position of the inhabited localities of each species in the climatic space), and therefore to characteristics of their geographic ranges (such as the extent of the distributions or altitudinal range inhabited by the species). In order to analyze the pattern of response of Argentine reptiles to global climate change, we ran phylogenetic generalized least squares models using species exposure to global climate change as a response variable, and (i) niche properties (breadth and position of the species in the climate space) and (ii) general features of the distribution of species (maximum latitude, altitudinal range, maximum elevation, distributional range and proximity to the most important dispersal barrier) as predictors. Our results suggest that the best way to explain climate change exposure is by combining breadth and position of climatic niche of the species or combining geographic features that are indicators of both niche characteristics. Our best model shows that in our study area, species with the narrowest distributional ranges that also inhabit the highest elevations are the most exposed to the effects of global climate change. In this sense, reptile species from Yungas, Puna and Andes ecoregions could be especially vulnerable to the effects of climate change. We believe that these types of models may represent an interesting tool for determining species and places particularly threatened by the effects of global climate change, which should be strongly considered in conservation planning.     

A combined method for determining inhibition type, kinetic parameters, and inhibition coefficients for aerobic cometabolism of 1,1,1-trichloroethane by a butane-grown mixed culture.

A combined method for determining inhibition type, kinetic parameters, and inhibition coefficients is developed and presented. The method was validated by applying it to data obtained from batch kinetics of the aerobic cometabolism of 1,1,1-trichloroethane (1,1,1-TCA) by a butane-grown mixed culture. The maximum degradation rates (k(max)) and half-saturation coefficients (K(s)) were independently determined in single compound tests, and compared with those obtained from inhibition tests. The inhibition type was determined using direct linear plots at various substrate and inhibitor concentrations. Kinetic parameters (k(max) and K(s)) and inhibition coefficients (K(ic) and K(iu)) were determined by nonlinear least squares regression (NLSR) fits of the inhibition model determined from the direct linear plots. Initial guesses of the kinetic parameters for NLSR were determined from linearized inhibition equations that were derived from the correlations between apparent maximum degradation rates (k(app)(max)) and/or the apparent half-saturation coefficient (K(app)(s)) and the k(max), K(s), and inhibitor concentration (I(L)) for each inhibition equation. Two different inhibition types were indicated from the direct linear plots: competitive inhibition of 1,1,1-TCA on butane degradation, and mixed inhibition of 1,1,1-TCA transformation by butane. Good agreement was achieved between independently measured k(max) and K(s) values and those obtained from both NLSR and the linearized inhibition equations. The initial guesses of all the kinetic parameters determined from linear plots were in the range of the values estimated from NLSR analysis. Overall the results show that use of the direct linear plot method to identify the inhibition type, coupled with initial guesses from linearized plots for NLSR analysis, results in an accurate method for determining inhibition types and coefficients. Detailed studies with pure cultures and purified enzymes are needed to further demonstrate the utility of this method.     

Spatial autocorrelation and red herrings in geographical ecology

Aim Spatial autocorrelation in ecological data can inflate Type I errors in statistical analyses. There has also been a recent claim that spatial autocorrelation generates ‘red herrings’, such that virtually all past analyses are flawed. We consider the origins of this phenomenon, the implications of spatial autocorrelation for macro‐scale patterns of species diversity and set out a clarification of the statistical problems generated by its presence. Location To illustrate the issues involved, we analyse the species richness of the birds of western/central Europe, north Africa and the Middle East. Methods Spatial correlograms for richness and five environmental variables were generated using Moran's I coefficients. Multiple regression, using both ordinary least‐squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, were used to identify the strongest predictors of richness. Autocorrelation analyses of the residuals obtained after stepwise OLS regression were undertaken, and the ranks of variables in the full OLS and GLS models were compared. Results Bird richness is characterized by a quadratic north–south gradient. Spatial correlograms usually had positive autocorrelation up to c. 1600 km. Including the environmental variables successively in the OLS model reduced spatial autocorrelation in the residuals to non‐detectable levels, indicating that the variables explained all spatial structure in the data. In principle, if residuals are not autocorrelated then OLS is a special case of GLS. However, our comparison between OLS and GLS models including all environmental variables revealed that GLS de‐emphasized predictors with strong autocorrelation and long‐distance clinal structures, giving more importance to variables acting at smaller geographical scales. Conclusion Although spatial autocorrelation should always be investigated, it does not necessarily generate bias. Rather, it can be a useful tool to investigate mechanisms operating on richness at different spatial scales. Claims that analyses that do not take into account spatial autocorrelation are flawed are without foundation.     

Linking camera‐trap data to taxonomy: Identifying photographs of morphologically similar chipmunks

Remote cameras are a common method for surveying wildlife and recently have been promoted for implementing large‐scale regional biodiversity monitoring programs. The use of camera‐trap data depends on the correct identification of animals captured in the photographs, yet misidentification rates can be high, especially when morphologically similar species co‐occur, and this can lead to faulty inferences and hinder conservation efforts. Correct identification is dependent on diagnosable taxonomic characters, photograph quality, and the experience and training of the observer. However, keys rooted in taxonomy are rarely used for the identification of camera‐trap images and error rates are rarely assessed, even when morphologically similar species are present in the study area. We tested a method for ensuring high identification accuracy using two sympatric and morphologically similar chipmunk (Neotamias) species as a case study. We hypothesized that the identification accuracy would improve with use of the identification key and with observer training, resulting in higher levels of observer confidence and higher levels of agreement among observers. We developed an identification key and tested identification accuracy based on photographs of verified museum specimens. Our results supported predictions for each of these hypotheses. In addition, we validated the method in the field by comparing remote‐camera data with live‐trapping data. We recommend use of these methods to evaluate error rates and to exclude ambiguous records in camera‐trap datasets. We urge that ensuring correct and scientifically defensible species identifications is incumbent on researchers and should be incorporated into the camera‐trap workflow.     

A novel bacterial community index to assess stream ecological health

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Effects of the landscape on boreal toad gene flow: does the pattern–process relationship hold true across distinct landscapes at the northern range margin?

Understanding the impact of natural and anthropogenic landscape features on population connectivity is a major goal in evolutionary ecology and conservation. Discovery of dispersal barriers is important for predicting population responses to landscape and environmental changes, particularly for populations at geographic range margins. We used a landscape genetics approach to quantify the effects of landscape features on gene flow and connectivity of boreal toad (Bufo boreas) populations from two distinct landscapes in south-east Alaska (Admiralty Island, ANM, and the Chilkat River Valley, CRV). We used two common methodologies for calculating resistance distances in landscape genetics studies (resistance based on least-cost paths and circuit theory). We found a strong effect of saltwater on genetic distance of CRV populations, but no landscape effects were found for the ANM populations. Our discordant results show the importance of examining multiple landscapes that differ in the variability of their features, to maximize detectability of underlying processes and allow results to be broadly applicable across regions. Saltwater serves as a physiological barrier to boreal toad gene flow and affects populations on a small geographic scale, yet there appear to be few other barriers to toad dispersal in this intact northern region.     

Using multiple landscape genetic approaches to test the validity of genetic clusters in a species characterized by an isolation‐by‐distance pattern

Bayesian clustering methods are typically used to identify barriers to gene flow, but they are prone to deduce artificial subdivisions in a study population characterized by an isolation‐by‐distance pattern (IbD). Here we analysed the landscape genetic structure of a population of wild boars (Sus scrofa) from south‐western Germany. Two clustering methods inferred the presence of the same genetic discontinuity. However, the population in question was characterized by a strong IbD pattern. While landscape‐resistance modelling failed to identify landscape features that influenced wild boar movement, partial Mantel tests and multiple regression of distance matrices (MRDMs) suggested that the empirically inferred clusters were separated by a genuine barrier. When simulating random lines bisecting the study area, 60% of the unique barriers represented, according to partial Mantel tests and MRDMs, significant obstacles to gene flow. By contrast, the random‐lines simulation showed that the boundaries of the inferred empirical clusters corresponded to the most important genetic discontinuity in the study area. Given the degree of habitat fragmentation separating the two empirical partitions, it is likely that the clustering programs correctly identified a barrier to gene flow. The differing results between the work published here and other studies suggest that it will be very difficult to draw general conclusions about habitat permeability in wild boar from individual studies.     

Sexual size dimorphism and Rensch's rule in Canidae

The size variation between males and females of a species is a phenomenon known as sexual size dimorphism (SSD). The observed patterns of variation in SSD among species has led to the formulation of Rensch's rule, which establishes that, in species showing a male size bias, SSD increases with an increase in the body size of the species. However, for species in which there is a female size bias, the SSD would decrease when the body size of the species increases. In the present study, we examined the variation in body size and SSD of 33 species of canids from estimates of body mass and body length. We studied its relationship with life‐history characteristics and tested Rensch's rule using phylogenetic generalized least squares and phylogenetic reduced major axis regressions, respectively. We observed the existence of correlation between body mass and body length, although the SSDs from these estimators are uncorrelated. SSD did not show the pattern predicted by Rensch's rule. SSD also did not show any correlation with life‐history traits. It is likely that the low SSD observed in canids is related to the monogamy observed in the family, which is a rare situation in mammals.