The interplay of spatial scale and landscape transformation modulates the abundance and intraspecific variation in the ecomorphological traits of a phyllostomid bat

Land use intensification imposes selective pressures that systematically change the frequency of wild population phenotypes. Growing evidence is biased towards the comparison of populations from discrete categories of land uses, ignoring the role of landscape emerging properties on the phenotype selection of wild fauna. Across the largest urban–rural gradient of the Colombian Orinoquia, we measured ecomorphological traits of 216 individuals of the flat-faced fruit-eating bat Artibeus planirostris. We did this to evaluate the scale of effect at which landscape transformation better predicts changes in phenotype and abundance of an urban-tolerant species. Forest percentage at 1.25 km was the main predictor affecting negatively bat abundance and positively its wing aspect ratio and body mass. Landscape variables affected forearm length at all spatial scales, this effect appeared to be sex-dependent, and the most important predictor, forest percentage at 0.5 km, had a negative effect on this trait. Our results indicate that landscape elements and spatial scale interact to shape ecomorphological traits and the abundance of A. planirostris. Interestingly, the scale of effect coincided at 1.25 km among all biological responses, suggesting that species’ abundance can be linked to the variation on phenotype under different environmental filters across landscape scenarios.

Structuring of plant communities across agricultural landscape mosaics: the importance of connectivity and the scale of effect

Background Plant communities of fragmented agricultural landscapes, are subject to patch isolation and scale-dependent effects. Variation in configuration, composition, and distance from one another affect biological processes of disturbance, productivity, and the movement ecology of species. However, connectivity and spatial structuring among these diverse communities are rarely considered together in the investigation of biological processes. Spatially optimised predictor variables that are based on informed measures of connectivity among communities, offer a solution to untangling multiple processes that drive biodiversity. Results To address the gap between theory and practice, a novel spatial optimisation method that incorporates hypotheses of community connectivity, was used to estimate the scale of effect of biotic and abiotic factors that distinguish plant communities. We tested: (1) whether different hypotheses of connectivity among sites was important to measuring diversity and environmental variation among plant communities; and (2) whether spatially optimised variables of species relative abundance and the abiotic environment among communities were consistent with diversity parameters in distinguishing four habitat types; namely Crop, Edge, Oak, and Wasteland. The global estimates of spatial autocorrelation, which did not consider environmental variation among sites, indicated significant positive autocorrelation under four hypotheses of landscape connectivity. The spatially optimised approach indicated significant positive and negative autocorrelation of species relative abundance at fine and broad scales, which depended on the measure of connectivity and environmental variation among sites. Conclusions These findings showed that variation in community diversity parameters does not necessarily correspond to underlying spatial structuring of species relative abundance. The technique used to generate spatially-optimised predictors is extendible to incorporate multiple variables of interest along with a priori hypotheses of landscape connectivity. Spatially-optimised variables with appropriate definitions of connectivity might be better than diversity parameters in explaining functional differences among communities.

Bee responses to landscape composition on coupled and decoupled multiscale approaches

ContextMultiscale approaches are essential for understanding ecological processes and detecting the scale of effect. However, nested multiscale approaches retain the effect of the landscape attributes from the smaller spatial scales into the larger ones. Thus, decoupling local vs. regional scales can reveal detailed ecological responses to landscape context, but this multiscale approach is poorly explored. ObjectivesWe evaluated the scale of effect of the forest cover (%) and landscape heterogeneity on Euglossini bees combining coupled and decoupled multiscale approaches. MethodsThe Euglossini males were sampled in forest patches from 15 landscapes within the Atlantic Forest, southeast Brazil. For simplicity, we defined that the coupled approaches represented the local scales and decoupled approaches the regional scales. We decoupled the scales by cutting out the smaller scales inserted into larger ones. We estimated the relationship of the bee community attributes with forest cover (%) and landscape heterogeneity in local and regional scales using Generalized Linear Models. ResultsWe found positive effects of landscape heterogeneity on species richness for regional scales. Forest cover and landscape heterogeneity in local scales showed positive effects on the euglossine abundances. The scale of effect for euglossine richness was higher than species abundances. ConclusionsCombining coupled and decoupled multiscale approaches showed adequate capture of the scale of effect of the landscape composition on bee communities. Therefore, it is of paramount importance to measure the influence of the landscape context on biodiversity. Maintaining landscapes with larger forest cover and spatial heterogeneity is essential to keep euglossine species requirements.

The interplay of spatial scale and landscape transformation moderates the abundance and intraspecific variation in the ecomorphological traits of a phyllostomid bat

Land-use intensification imposes selective pressures that systematically change the frequency of wild population phenotypes. Growing evidence is biased towards the comparison of populations from discrete categories of land uses, ignoring the role of landscape emerging properties on the phenotype selection of wild fauna. Across the largest urban-rural gradient of the Colombian Orinoquia, we measured ecomorphological traits of 216 individuals of the Flat-faced Fruit-eating Bat Artibeus planirostris, to evaluate the scale of effect at which landscape transformation better predicts changes in phenotype and abundance of an urban-tolerant species. Forest percentage at 1.25 km was the main predictor affecting abundance, wing aspect ratio, and body mass of this phyllostomid; but the direction of the effect differed between abundance and ecomorphological traits. Although landscape factors explained changes in the forearm length at all spatial scales, the effect was sex-dependent and the most important predictor was forest percentage at 0.5 km. Our results indicate that landscape elements and spatial scale interact to shape ecomorphological traits and the abundance of A. planirostris. Interestingly, the scale of effect was congruent among all biological responses. A pattern that likely arises since species' abundance can reflect the variation on phenotype under different environmental filters across landscape scenarios.

How to use and report Bayesian hypothesis tests

This article provides guidance on interpreting and reporting Bayesian hypothesis tests, in order to aid their understanding. To use and report a Bayesian hypothesis test, predicted effect sizes must be specified. The paper will provide guidance in specifying effect sizes of interest (which also will be of relevance to those using frequentist statistics). First, if a minimally interesting effect size can be specified, a null interval is defined as the effects smaller in magnitude than the minimally interesting effect. Then the proportion of the posterior distribution that falls in the null interval indicates the plausibility of the null interval hypothesis. Second, if a rough scale of effect can be determined, a Bayes factor can indicate evidence for a model representing that scale of effect versus a model of H0. Both methods allow data to count against a theory that predicts a difference. By contrast, non-significance does not count against such a theory. Various examples are provided including the suitability of Bayesian analyses for demonstrating the absence of conscious perception under putative subliminal conditions, and its presence in supraliminal conditions.

siland: an R package for estimating the spatial influence of landscape

ContextThe spatial distributions of species and populations are both influenced by local variables and by characteristics of surrounding landscapes. Understanding how landscape features spatially structure the frequency of a trait in a population, the abundance of a species or the species’ richness remains difficult specially because the spatial scale effects of the landscape variables are often unknown.ObjectivesHere, we present “siland”, an R package for analyzing the effect of landscape features on georeferenced point observations (described in a Geographic Information System shapefile format).Methods & Results“siland” simultaneously estimates the spatial scales and intensities of landscape variable effects. It does not require any information about the scale of effect. Two methods are available: one is based on focal sample site (Bsiland method, b for buffer) and one is distance weighted using Spatial Influence Function (Fsiland method, f for function). ‘siland’ allows for effects tests, effects maps and models comparison.ConclusionsAdaptable and user-friendly, the “siland” package is a very practical tool to perform landscape analysis.