Spatial autocorrelation and the selection of simultaneous autoregressive models

ABSTRACT Aim Spatial autocorrelation is a frequent phenomenon in ecological data and can affect estimates of model coefficients and inference from statistical models. Here, we test the performance of three different simultaneous autoregressive (SAR) model types (spatial error = SAR err , lagged = SAR lag and mixed = SAR mix ) and common ordinary least squares (OLS) regression when accounting for spatial autocorrelation in species distribution data using four artificial data sets with known (but different) spatial autocorrelation structures. Methods We evaluate the performance of SAR models by examining spatial patterns in model residuals (with correlograms and residual maps), by comparing model parameter estimates with true values, and by assessing their type I error control with calibration curves. We calculate a total of 3240 SAR models and illustrate how the best models [in terms of minimum residual spatial autocorrelation (minRSA), maximum model fit ( R 2 ), or Akaike information criterion (AIC)] can be identified using model selection procedures. Results Our study shows that the performance of SAR models depends on model specification (i.e. model type, neighbourhood distance, coding styles of spatial weights matrices) and on the kind of spatial autocorrelation present. SAR model parameter estimates might not be more precise than those from OLS regressions in all cases. SAR err models were the most reliable SAR models and performed well in all cases (independent of the kind of spatial autocorrelation induced and whether models were selected by minRSA, R 2 or AIC), whereas OLS, SAR lag and SAR mix models showed weak type I error control and/or unpredictable biases in parameter estimates. Main conclusions SAR err models are recommended for use when dealing with spatially autocorrelated species distribution data. SAR lag and SAR mix might not always give better estimates of model coefficients than OLS, and can thus generate bias. Other spatial modelling techniques should be assessed comprehensively to test their predictive performance and accuracy for biogeographical and macroecological research

Mammal predator and prey species richness are strongly linked at macroscales

Predator-prey interactions play an important role for species composition and community dynamics at local scales, but their importance in shaping large-scale gradients of species richness remains unexplored. Here, we use global range maps, structural equation models (SEM), and comprehensive databases of dietary preferences and body masses of all terrestrial, non-volant mammals worldwide, to test whether (1) prey bottom-up or predator top-down relationships are important drivers of broad-scale species richness gradients once the environment and human influence have been accounted for, (2) predator-prey richness associations vary among biogeographic regions, and (3) body size influences large-scale covariation between predators and prey. SEMs including only productivity, climate, and human factors explained a high proportion of variance in prey richness (R2 = 0.56) but considerably less in predator richness (R2 = 0.13). Adding predator-to-prey or prey-topredator paths strongly increased the explained variance in both cases (prey R2 = 0.79, predator R2 = 0.57), suggesting that predator-prey interactions play an important role in driving global diversity gradients. Prey bottom-up effects prevailed over productivity, climate, and human influence to explain predator richness, whereas productivity and climate were more important than predator top-down effects for explaining prey richness, although predator top-down effects were still significant. Global predator-prey associations were not reproduced in all regions, indicating that distinct paleoclimate and evolutionary histories (Africa and Australia) may alter species interactions across trophic levels. Stronger crosstrophic- level associations were recorded within categories of similar body size (e.g., large prey to large predators) than between them (e.g., large prey to small predators), suggesting that mass-related energetic and physiological constraints influence broad-scale richness links, especially for large-bodied mammals. Overall, our results support the idea that trophic interactions can be important drivers of large-scale species richness gradients in combination with environmental effects. © 2013 by the Ecological Society of America

A new absolute arrival time data set for Europe

The main aim of this study is to create a data set of accurate absolute arrival times for stations in Europe which do not report to the International Seismological Centre (ISC). Waveforms were obtained from data centres and temporary experiments and a semi-automatic picking method was applied to determine absolute arrival times for P and S phases. 85 000 arrival times were picked whose distribution of residuals shows generally low standard deviations on the order of 0.5-0.7 s. Furthermore, mean teleseismic station residuals reflect the properties of the underlying crust and uppermost mantle. Comparison to ISC data for matching event-station-phase combinations also confirms the good quality of the new absolute arrival time picks. Most importantly, this data set complements the ISC data as it fills regional data coverage gaps in Europ

The speaking vocabulary of kindergarten, grade I, grade II, and grade III

Thesis (Ed.M.)--Boston Universit

Strong paleoclimatic legacies in current plant functional diversity patterns across Europe

Numerous studies indicate that environmental changes during the late Quaternary have elicited long‐term disequilibria between species diversity and environment. Despite its importance for ecosystem functioning, the importance of historical environmental conditions as determinants of FD (functional diversity) remains largely unstudied. We quantified the geographic distributions of plant FD (richness and dispersion) across Europe using distribution and functional trait information for 2702 plant species. We then compared the importance of historical and contemporary factors to determine the relevance of past conditions as predictors of current plant FD in Europe. For this, we compared the strength of the relationships between FD with temperature and precipitation stability since the LGM (Last Glacial Maximum), accessibility to LGM refugia, and contemporary environmental conditions (climate, productivity, soil, topography, and land use). Functional richness and dispersion exhibited geographic patterns with strong associations to the environmental history of the region. The effect size of accessibility to LGM refugia and climate stability since the LGM was comparable to that of the contemporary predictors. Both functional richness and dispersion increased with temperature stability since the LGM and accessibility to LGM refugia. Functional richness' geographic pattern was primarily associated with accessibility to LGM refugia growing degree‐days, land use heterogeneity, diversity of soil types, and absolute minimum winter temperature. Functional dispersion's geographic pattern was primarily associated with accessibility to LGM refugia growing degree‐days and absolute minimum winter temperature. The high explained variance and model support of historical predictors are consistent with the idea that long‐term variability in environmental conditions supplements contemporary factors in shaping FD patterns at continental scales. Given the importance of FD for ecosystem functioning, future climate change may elicit not just short‐term shifts in ecosystem functioning, but also long‐term functional disequilibria

Towards global data products of Essential Biodiversity Variables on species traits

Essential Biodiversity Variables (EBVs) allow observation and reporting of global biodiversity change, but a detailed framework for the empirical derivation of specific EBVs has yet to be developed. Here, we re-examine and refine the previous candidate set of species traits EBVs and show how traits related to phenology, morphology, reproduction, physiology and movement can contribute to EBV operationalization. The selected EBVs express intra-specific trait variation and allow monitoring of how organisms respond to global change. We evaluate the societal relevance of species traits EBVs for policy targets and demonstrate how open, interoperable and machine-readable trait data enable the building of EBV data products. We outline collection methods, meta(data) standardization, reproducible workflows, semantic tools and licence requirements for producing species traits EBVs. An operationalization is critical for assessing progress towards biodiversity conservation and sustainable development goals and has wide implications for data-intensive science in ecology, biogeography, conservation and Earth observation

Minimum Information Standards for Essential Biodiversity Variables

Minimum Information Standards (MIS) are sets of specifications for describing datasets that aim to standardize data reporting and to maximize its discoverability and interoperability. While MIS have greatly contributed to enhance the distribution and reuse of datasets in the biological and biomedical sciences, their adoption in ecology and biodiversity sciences is still incipient. Here we present a community effort to generate minimum standards for Essential Biodiversity Variables (EBVs). The operationalization of EBVs require integrating heterogeneous datasets of disparate origin and, often, to combine information from different geographic areas and time periods. Furthermore, developing policy-relevant indicators from EBVs requires an additional level of integration between datasets that inform on different facets of biodiversity, e.g. at levels from species to ecosystems. MIS for Essential Biodiversity Variables is founded in the description of the EBV-data cube as the unifying framework to deliver interoperable biodiversity observations. They summarize aspects of the spatial and temporal domains of the datasets, as well as uncertainty and bias reporting. MIS also incorporate the GEOSS proposed principles for data management. Finally, a metadata publishing toolkit will be developed in order to ensure that EBVs are discoverable and used under the auspices of GEO BON

An intervention study to prevent relapse in patients with schizophrenia

Purpose: To determine whether the use of relapse prevention plans (RPPs) in nursing practice is an effective intervention in reducing relapse rates among patients with schizophrenia. Design and Methods: Experimental design. Patients with schizophrenia (or a related psychotic disorder) and nurses from three mental health organizations were randomly assigned to either an experimental (RPP) or control condition (care as usual). The primary outcome measure was the psychotic relapses in the research groups. Results: The relapse rates in the experimental and control groups after 1-year follow-up were 12.5% and 26.2%, respectively (p=.12, ns). The relative risk of a relapse in the experimental versus the control group was 0.48(ns). Conclusions: In this study no statistically significant effects of the intervention were found. Effectiveness research in this area should be continued with larger sample sizes and longer follow-up periods