Boris Schröder

COMPONENTS OF UNCERTAINTY IN SPECIES DISTRIBUTION ANALYSIS: A CASE STUDY OF THE GREAT GREY SHRIKE

Sophisticated statistical analyses are common in ecological research, particularly in species distribution modeling. The effects of sometimes arbitrary decisions during the modeling procedure on the final outcome are difficult to assess, and to date are largely unexplored. We conducted an analysis quantifying the contribution of uncertainty in each step during the model-building sequence to variation in model validity and climate change projection uncertainty. Our study system was the distribution of the Great Grey Shrike in the German federal state of Saxony. For each of four steps (data quality, collinearity method, model type, and variable selection), we ran three different options in a factorial experiment, leading to 81 different model approaches. Each was subjected to a fivefold cross-validation, measuring area under curve (AUC) to assess model quality. Next, we used three climate change scenarios times three precipitation realizations to project future distributions from each model, yielding 729 projections. Again, we analyzed which step introduced most variability (the four model-building steps plus the two scenario steps) into predicted species prevalences by the year 2050. Predicted prevalences ranged from a factor of 0.2 to a factor of 10 of present prevalence, with the majority of predictions between 1.1 and 4.2 (inter-quartile range). We found that model type and data quality dominated this analysis. In particular, artificial neural networks yielded low cross-validation robustness and gave very conservative climate change predictions. Generalized linear and additive models were very similar in quality and predictions, and superior to neural networks. Variations in scenarios and realizations had very little effect, due to the small spatial extent of the study region and its relatively small range of climatic conditions. We conclude that, for climate projections, model type and data quality were the most influential factors. Since comparison of model types has received good coverage in the ecological literature, effects of data quality should now come under more scrutiny.

Population dynamics and habitat connectivity affecting the spatial spread of populations: a simulation study

In this paper we show how the spatialconfiguration of habitat quality affects the spatial spread of apopulation in a heterogeneous environment. Our main result is thatfor species with limited dispersal ability and a landscape withisolated habitats, stepping stone patches of habitat greatlyincrease the ability of species to disperse. Our results showthat increasing reproductive rate first enables and thenaccelerates spatial spread, whereas increasing the connectivity has aremarkable effect only in case of low reproductive rates. Theimportance of landscape structure varied according to thedemographic characteristics of the population. To show this wepresent a spatially explicit habitat model taking into accountpopulation dynamics and habitat connectivity. The population dynamicsare based on a matrix projection model and are calculated on eachcell of a regular lattice. The parameters of the Leslie matrix dependon habitat suitability as well as density. Dispersal between adjacentcells takes place either unrestricted or with higher probability inthe direction of a higher habitat quality (restricted dispersal).Connectivity is maintained by corridors and stepping stones ofoptimal habitat quality in our fragmented model landscape containinga mosaic of different habitat suitabilities. The cellular automatonmodel serves as a basis for investigating different combinations ofparameter values and spatial arrangements of cells with high and lowquality.

Integrating movement ecology with biodiversity research - exploring new avenues to address spatiotemporal biodiversity dynamics

Movement of organisms is one of the key mechanisms shaping biodiversity, e.g. the distribution of genes, individuals and species in space and time. Recent technological and conceptual advances have improved our ability to assess the causes and consequences of individual movement, and led to the emergence of the new field of ‘movement ecology’. Here, we outline how movement ecology can contribute to the broad field of biodiversity research, i.e. the study of processes and patterns of life among and across different scales, from genes to ecosystems, and we propose a conceptual framework linking these hitherto largely separated fields of research. Our framework builds on the concept of movement ecology for individuals, and demonstrates its importance for linking individual organismal movement with biodiversity. First, organismal movements can provide ‘mobile links’ between habitats or ecosystems, thereby connecting resources, genes, and processes among otherwise separate locations. Understanding these mobile links and their impact on biodiversity will be facilitated by movement ecology, because mobile links can be created by different modes of movement (i.e., foraging, dispersal, migration) that relate to different spatiotemporal scales and have differential effects on biodiversity. Second, organismal movements can also mediate coexistence in communities, through ‘equalizing’ and ‘stabilizing’ mechanisms. This novel integrated framework provides a conceptual starting point for a better understanding of biodiversity dynamics in light of individual movement and space-use behavior across spatiotemporal scales. By illustrating this framework with examples, we argue that the integration of movement ecology and biodiversity research will also enhance our ability to conserve diversity at the genetic, species, and ecosystem levels.

Decomposing environmental, spatial, and spatiotemporal components of species distributions

Species distribution models are an important tool to predict the impact of global change on species distributional ranges and community assemblages. Although considerable progress has been made in the statistical modeling during the last decade, many approaches still ignore important features of species distributions, such as nonlinearity and interactions between predictors, spatial autocorrelation, and nonstationarity, or at most incorporate only some of these features. Ecologists, however, require a modeling framework that simultaneously addresses all these features flexibly and consistently. Here we describe such an approach that allows the estimation of the global effects of environmental variables in addition to local components dealing with spatiotemporal autocorrelation as well as nonstationary effects. The local components can be used to infer unknown spatiotemporal processes; the global component describes how the species is influenced by the environment and can be used for predictions, allowing ...

Biodiversity and the mitigation of climate change through bioenergy: impacts of increased maize cultivation on farmland wildlife

The public promotion of renewable energies is expected to increase the number of biogas plants and stimulate energy crops cultivation (e.g. maize) in Germany. In order to assess the indirect effects of the resulting land‐use changes on biodiversity, we developed six land‐use scenarios and simulated the responses of six farmland wildlife species with the spatially explicit agent‐based model system ALMaSS. The scenarios differed in composition and spatial configuration of arable crops. We implemented scenarios where maize for energy production replaced 15% and 30% of the area covered by other cash crops. Biogas maize farms were either randomly distributed or located within small or large aggregation clusters. The animal species investigated were skylark (Alauda arvensis), grey partridge (Perdix perdix), European brown hare (Lepus europaeus), field vole (Microtus agrestis), a linyphiid spider (Erigone atra) and a carabid beetle (Bembidion lampros). The changes in crop composition had a negative effect on the population sizes of skylark, partridge and hare and a positive effect on the population sizes of spider and beetle and no effect on the population size of vole. An aggregated cultivation of maize amplified these effects for skylark. Species responses to changes in the crop composition were consistent across three differently structured landscapes. Our work suggests that with the compliance to some recommendations, negative effects of biogas‐related land‐use change on the populations of the six representative farmland species can largely be avoided.

Predicting the species composition of Nardus stricta communities by logistic regression modelling

Abstract Question: Predictive models in plant ecology usually deal with single species or community types. Little effort has so far been made to predict the species composition of a community explicitly. The modelling approach presented here provides a conceptual framework on how to achieve this by combining habitat models for a large number of species to an additive community model. Our approach is exemplified by Nardus stricta communities (acidophilous, low-productive grassland). Location: Large areas of Germany, 0-2040 m a.s.l. Methods: Logistic regression is applied for individual species models which are subsequently combined for an explicit prediction of species composition. Several parameters reflecting soil, management and climatic conditions serve as predictor variables. For validation, bootstrap and jackknife resampling procedures are used as well as ordination techniques (DCA, CCA). Results: We calculated significant models for 138 individual species. The predictions of species composition and species richness yield good agreements with the observed data. DCA and CCA results show that the community model preserves the main patterns in floristic space. Conclusions: Our approach of predicting species composition is an effective tool that can be applied in nature conservation, e.g. to assess the effects of different site conditions and alternative management scenarios on species composition and richness. Abbreviations: AUC = Area under curve; CCR = Correct classification rate; GAM = Generalized additive model; GLM = Generalized linear model, ROC = Receiver operating characteristic. Nomenclature: Ehrendorfer (1973); Frahm & Frey (1983)

Connectivity compensates for low habitat quality and small patch size in the butterfly Cupido minimus

Habitat size, habitat isolation and habitat quality are regarded as the main determinants of butterfly occurrence in fragmented landscapes. To analyze the relationship between the occurrence of the butterfly Cupido minimus and these factors, patch occupancy of the immature stages in patches of its host plant Anthyllisvulneraria was studied in the nature reserve Hohe Wann in Bavaria (Germany). In 2001 and 2002, 82 A.vulneraria patches were surveyed for the presence of C. minimus larvae. The occurrence was largely affected by the size of the food plant patches. In a habitat model that uses multiple logistic regression, the type of management and habitat connectivity are further determinants of species distribution. Internal and temporal validation demonstrate the stability and robustness of the developed habitat models. Additionally, it was proved that the colonization rate of C. minimus was significantly influenced by the distance to the next occupied Anthyllis patch. Concerning long-term survival of (meta-) populations in fragmented landscapes, the results show that lower habitat quality may be compensated by higher connectivity between host plant patches.

Cowbird parasitism of Pale-headed Brush-finch Atlapetes pallidiceps : implications for conservation and management

Pale-headed Brush-finch Atlapetes pallidiceps is a restricted-range species that is threatened with extinction due to habitat loss. The total population of 60–80 individuals achieved a reproductive output of only 0.74 young per breeding pair in 2002. Brood parasitism by Shiny Cowbird Molothrus bonariensis was a major factor reducing breeding success, affecting 38.5% of broods. Parasitism rates reached 50% in an ungrazed reserve, but only 14% on an adjacent grazed plot. The resulting difference in breeding success was not, however, attributable to vegetation parameters used to describe microhabitat use. Cowbird parasitism rates therefore seem to be influenced largely by factors operating at the landscape level. These may include grazing scheme, topography, humidity and host availability. It is suggested that lower species diversity and bird abundance rendered the grazed site less attractive to cowbirds. Current parasitism rates are of great conservation concern due to the low population size of Pale-headed Brush-finch, and the initiation of controlling measures is pressing. Management options described from intensive cowbird control programmes in North America are reviewed and evaluated for their applicability here. To combine the possibility of further data collection with commencement of immediate conservation action, we consider two alternative approaches. Nest monitoring and cowbird egg removal would enable the study of the distribution of parasitism in relation to landscape and vegetation variables, whereas cowbird shooting and nest monitoring might provide a larger short-term benefit to reproductive output. Habitat management, resumption of some grazing in the reserve and cowbird removal should be considered for the intermediate future.

Ecosystem Engineering by Plants on Wave-Exposed Intertidal Flats Is Governed by Relationships between Effect and Response Traits.

In hydrodynamically stressful environments, some species—known as ecosystem engineers—are able to modify the environment for their own benefit. Little is known however, about the interaction between functional plant traits and ecosystem engineering. We studied the responses of Scirpus tabernaemontani and Scirpus maritimus to wave impact in full-scale flume experiments. Stem density and biomass were used to predict the ecosystem engineering effect of wave attenuation. Also the drag force on plants, their bending angle after wave impact and the stem biomechanical properties were quantified as both responses of stress experienced and effects on ecosystem engineering. We analyzed lignin, cellulose, and silica contents as traits likely effecting stress resistance (avoidance, tolerance). Stem density and biomass were strong predictors for wave attenuation, S. maritimus showing a higher effect than S. tabernaemontani. The drag force and drag force per wet frontal area both differed significantly between the species at shallow water depths (20 cm). At greater depths (35 cm), drag forces and bending angles were significantly higher for S. maritimus than for S. tabernaemontani. However, they do not differ in drag force per wet frontal area due to the larger plant surface of S. maritimus. Stem resistance to breaking and stem flexibility were significantly higher in S. tabernaemontani, having a higher cellulose concentration and a larger cross-section in its basal stem parts. S. maritimus had clearly more lignin and silica contents in the basal stem parts than S. tabernaemontani. We concluded that the effect of biomass seems more relevant for the engineering effect of emergent macrophytes with leaves than species morphology: S. tabernaemontani has avoiding traits with minor effects on wave attenuation; S. maritimus has tolerating traits with larger effects. This implies that ecosystem engineering effects are directly linked with traits affecting species stress resistance and responding to stress experienced.

Regionalizing Indicator Values for Soil Reaction in the Bavarian Alps – from Averages to Multivariate Spectra

We present an approach to produce maps of Ellenberg values for soil reaction (R-value) in the Bavarian Alps. Eleven meaningful environmental predictors covering GIS-derived information on climatic, topographic and soil conditions were used to predict R-values. As dependent variables, Ellenberg indicator values for soil reaction were queried from plot records in the vegetation database WINALPecobase. We used an additive georegression model, which combines complex prediction models and the increased prediction accuracy of a boosting algorithm. In addition to environmental predictors we included spatial effects into the model to account for spatial autocorrelation. As we were particularly interested in the usefulness of averaged R-values for spatial prediction, we applied two different models: (1) a geo-additive regression model that estimates mean R-values and (2) a proportional odds model predicting the probability distribution over R-values 1 to 9. We found meaningful dependencies between the R-value and our predictors. Both models produced the same spatial pattern of predictions. Spatial effects had an impact only in the first model. The main drawback of mean R-values is the oversimplification of complex conditions of soil reaction, which is entailed by averaging and regression to mean values. Therefore, regionalized average indicator values provide only limited information on site-ecological characteristics. Model 1 failed to predict the range and shapes of original indicator spectra precisely. In contrast, the second model provided a more sophisticated picture of soil reaction. To make the multivariate output of model 2 comparable to that of model 1, we propose to plot the distribution in a three-dimensional color-space. In addition, comparison of both models based on a multiple linear regression model resulted in a R2 of 0.93. The proportional odds model is a promising approach also for other indicator values and different regions as well as for other ordinal-scaled ecological parameters.