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Although temporal heterogeneity is a well-accepted driver of biodiversity, effects of interannual variation in land-use intensity (LUI) have not been addressed yet. Additionally, responses to land use can differ greatly among different organisms; therefore, overall effects of land-use on total local biodiversity are hardly known. To test for effects of LUI (quantified as the combined intensity of fertilization, grazing, and mowing) and interannual variation in LUI (SD in LUI across time), we introduce a unique measure of whole-ecosystem biodiversity, multidiversity. This synthesizes individual diversity measures across up to 49 taxonomic groups of plants, animals, fungi, and bacteria from 150 grasslands. Multidiversity declined with increasing LUI among grasslands, particularly for rarer species and aboveground organisms, whereas common species and belowground groups were less sensitive. However, a high level of interannual variation in LUI increased overall multidiversity at low LUI and was even more beneficial for rarer species because it slowed the rate at which the multidiversity of rare species declined with increasing LUI. In more intensively managed grasslands, the diversity of rarer species was, on average, 18% of the maximum diversity across all grasslands when LUI was static over time but increased to 31% of the maximum when LUI changed maximally over time. In addition to decreasing overall LUI, we suggest varying LUI across years as a complementary strategy to promote biodiversity conservation.
Collinearity a review of methods to deal with it and a simulation study evaluating their performance
(2013)
Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the folk lore'-thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.
Aim Biotic interactions within guilds or across trophic levels have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of species interaction distribution models (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities.
Over the last two decades, macroecology the analysis of large-scale, multi-species ecological patterns and processes has established itself as a major line of biological research. Analyses of statistical links between environmental variables and biotic responses have long and successfully been employed as a main approach, but new developments are due to be utilized. Scanning the horizon of macroecology, we identified four challenges that will probably play a major role in the future. We support our claims by examples and bibliographic analyses. 1) Integrating the past into macroecological analyses, e.g. by using paleontological or phylogenetic information or by applying methods from historical biogeography, will sharpen our understanding of the underlying reasons for contemporary patterns. 2) Explicit consideration of the local processes that lead to the observed larger-scale patterns is necessary to understand the fine-grain variability found in nature, and will enable better prediction of future patterns (e.g. under environmental change conditions). 3) Macroecology is dependent on large-scale, high quality data from a broad spectrum of taxa and regions. More available data sources need to be tapped and new, small-grain large-extent data need to be collected. 4) Although macroecology already lead to mainstreaming cutting-edge statistical analysis techniques, we find that more sophisticated methods are needed to account for the biases inherent to sampling at large scale. Bayesian methods may be particularly suitable to address these challenges. To continue the vigorous development of the macroecological research agenda, it is time to address these challenges and to avoid becoming too complacent with current achievements.
Land use is increasingly recognized as a major driver of biodiversity and ecosystem functioning in many current research projects. In grasslands, land use is often classified by categorical descriptors such as pastures versus meadows or fertilized versus unfertilized sites. However, to account for the quantitative variation of multiple land-use types in heterogeneous landscapes, a quantitative, continuous index of land-use intensity (LUI) is desirable. Here we define such a compound, additive LUI index for managed grasslands including meadows and pastures. The LUI index summarizes the standardized intensity of three components of land use, namely fertilization, mowing, and livestock grazing at each site. We examined the performance of the LUI index to predict selected response variables on up to 150 grassland sites in the Biodiversity Exploratories in three regions in Germany(Alb, Hainich, Schorlheide). We tested the average Ellenberg nitrogen indicator values of the plant community, nitrogen and phosphorus concentration in the aboveground plant biomass, plant-available phosphorus concentration in the top soil, and soil C/N ratio, and the first principle component of these five response variables.
The LUI index significantly predicted the principal component of all five response variables, as well as some of the individual responses. Moreover, vascular plant diversity decreased significantly with LUI in two regions (Alb and Hainich).
Inter-annual changes in management practice were pronounced from 2006 to 2008, particularly due to variation in grazing intensity. This rendered the selection of the appropriate reference year(s) an important decision for analyses of land-use effects, whereas details in the standardization of the index were of minor importance. We also tested several alternative calculations of a LUI index, but all are strongly linearly correlated to the proposed index.
The proposed LUI index reduces the complexity of agricultural practices to a single dimension and may serve as a baseline to test how different groups of organisms and processes respond to land use. In combination with more detailed analyses, this index may help to unravel whether and how land-use intensities, associated disturbance levels or other local or regional influences drive ecological processes.
SDM performance varied for different range dynamics. Prediction accuracies decreased when abrupt range shifts occurred as species were outpaced by the rate of climate change, and increased again when a new equilibrium situation was realised. When ranges contracted, prediction accuracies increased as the absences were predicted well. Far- dispersing species were faster in tracking climate change, and were predicted more accurately by SDMs than short- dispersing species. BRTs mostly outperformed GLMs. The presence of a predator, and the inclusion of its incidence as an environmental predictor, made BRTs and GLMs perform similarly. Results are discussed in light of other studies dealing with effects of ecological traits and processes on SDM performance. Perspectives are given on further advancements of SDMs and for possible interfaces with more mechanistic approaches in order to improve predictions under environmental change.