Niklaus E. Zimmermann

Predictive habitat distribution models in ecology

With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities to their present environment. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management. In this paper, we present a review of predictive habitat distribution modeling. The variety of statistical techniques used is growing. Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions. Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models. The selection of an appropriate method should not depend solely on statistical considerations. Some models are better suited to reflect theoretical findings on the shape and nature of the species’ response (or realized niche). Conceptual considerations include e.g. the trade-off between optimizing accuracy versus optimizing generality. In the field of static distribution modeling, the latter is mostly related to selecting appropriate predictor variables and to designing an appropriate procedure for model selection. New methods, including threshold-independent measures (e.g. receiver operating characteristic (ROC)-plots) and resampling techniques (e.g. bootstrap, cross-validation) have been introduced in ecology for testing the accuracy of predictive models. The choice of an evaluation measure should be driven primarily by the goals of the study. This may possibly lead to the attribution of different weights to the various types of prediction errors (e.g. omission, commission or confusion). Testing the model in a wider range of situations (in space and time) will permit one to define the range of applications for which the model predictions are suitable. In turn, the qualification of the model depends primarily on the goals of the study that define the qualification criteria and on the usability of the model, rather than on statistics alone.

Predictive mapping of alpine grasslands in Switzerland: Species versus community approach

Separate logistic regression models were developed to predict the distribution and large-scale spatial patterns of dominant graminoid species and communities in alpine grasslands. The models are driven by four bioclimatic param- eters: degree-days of growing season (basis 0 °C), a moisture index for July, potential direct solar radiation for March, and a continentality index. Geology and slope angle were used as a surrogate for nutrient availability and soil water capacity. The bioclimatic parameters were derived from monthly mean tem- perature, precipitation, cloudiness and potential direct solar radiation. The environmental parameters were interpolated us- ing a digital elevation model with a resolution of 50 m. The vegetation data for model calibration originate from field sur- veys and literature. An independent test data set with samples from three different climatic zones was used to test the model. The degree of coincidence between simulated and observed patterns was similar for species and communities, but the κ- values for communities were generally higher (κ = 0.539) than for species (mean individual κ = 0.201). Information on land use was detected as a major factor that could significantly improve both the species and the community model. Never- theless, the climatic factors used to drive the model explained a major part of the observed patterns.

Tree line shifts in the Swiss Alps: Climate change or land abandonment?

Abstract Questions: Did the forest area in the Swiss Alps increase between 1985 and 1997? Does the forest expansion near the tree line represent an invasion into abandoned grasslands (ingrowth) or a true upward shift of the local tree line? What land cover / land use classes did primarily regenerate to forest, and what forest structural types did primarily regenerate? And, what are possible drivers of forest regeneration in the tree line ecotone, climate and/or land use change? Location: Swiss Alps. Methods: Forest expansion was quantified using data from the repeated Swiss land use statistics GEOSTAT. A moving window algorithm was developed to distinguish between forest ingrowth and upward shift. To test a possible climate change influence, the resulting upward shifts were compared to a potential regional tree line. Results: A significant increase of forest cover was found between 1650 m and 2450 m. Above 1650 m, 10% of the new forest areas were identified as true upward shifts whereas 90% represented ingrowth, and we identified both land use and climate change as likely drivers. Most upward shift activities were found to occur within a band of 300 m below the potential regional tree line, indicating land use as the most likely driver. Only 4% of the upward shifts were identified to rise above the potential regional tree line, thus indicating climate change. Conclusions: Land abandonment was the most dominant driver for the establishment of new forest areas, even at the tree line ecotone. However, a small fraction of upwards shift can be attributed to the recent climate warming, a fraction that is likely to increase further if climate continues to warm, and with a longer time-span between warming and measurement of forest cover. Nomenclature: Aeschimann & Heitz (1996).

Climatic extremes improve predictions of spatial patterns of tree species

Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D2, +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

Impacts of climate change on the world's most exceptional ecoregions

The current rate of warming due to increases in greenhouse gas (GHG) emissions is very likely unprecedented over the last 10,000 y. Although the majority of countries have adopted the view that global warming must be limited to <2 °C, current GHG emission rates and nonagreement at Copenhagen in December 2009 increase the likelihood of this limit being exceeded by 2100. Extensive evidence has linked major changes in biological systems to 20th century warming. The “Global 200” comprises 238 ecoregions of exceptional biodiversity [Olson DM, Dinerstein E (2002) Ann Mo Bot Gard 89:199–224]. We assess the likelihood that, by 2070, these iconic ecoregions will regularly experience monthly climatic conditions that were extreme in 1961–1990. Using >600 realizations from climate model ensembles, we show that up to 86% of terrestrial and 83% of freshwater ecoregions will be exposed to average monthly temperature patterns >2 SDs (2σ) of the 1961–1990 baseline, including 82% of critically endangered ecoregions. The entire range of 89 ecoregions will experience extreme monthly temperatures with a local warming of <2 °C. Tropical and subtropical ecoregions, and mangroves, face extreme conditions earliest, some with <1 °C warming. In contrast, few ecoregions within Boreal Forests and Tundra biomes will experience such extremes this century. On average, precipitation regimes do not exceed 2σ of the baseline period, although considerable variability exists across the climate realizations. Further, the strength of the correlation between seasonal temperature and precipitation changes over numerous ecoregions. These results suggest many Global 200 ecoregions may be under substantial climatic stress by 2100.

Climate change and European forests: What do we know, what are the uncertainties, and what are the implications for forest management?

The knowledge about potential climate change impacts on forests is continuously expanding and some changes in growth, drought induced mortality and species distribution have been observed. However despite a significant body of research, a knowledge and communication gap exists between scientists and non-scientists as to how climate change impact scenarios can be interpreted and what they imply for European forests. It is still challenging to advise forest decision makers on how best to plan for climate change as many uncertainties and unknowns remain and it is difficult to communicate these to practitioners and other decision makers while retaining emphasis on the importance of planning for adaptation. In this paper, recent developments in climate change observations and projections, observed and projected impacts on European forests and the associated uncertainties are reviewed and synthesised with a view to understanding the implications for forest management. Current impact assessments with simulation models contain several simplifications, which explain the discrepancy between results of many simulation studies and the rapidly increasing body of evidence about already observed changes in forest productivity and species distribution. In simulation models uncertainties tend to cascade onto one another; from estimating what future societies will be like and general circulation models (GCMs) at the global level, down to forest models and forest management at the local level. Individual climate change impact studies should not be uncritically used for decision-making without reflection on possible shortcomings in system understanding, model accuracy and other assumptions made. It is important for decision makers in forest management to realise that they have to take long-lasting management decisions while uncertainty about climate change impacts are still large. We discuss how to communicate about uncertainty - which is imperative for decision making - without diluting the overall message. Considering the range of possible trends and uncertainties in adaptive forest management requires expert knowledge and enhanced efforts for providing science-based decision support.

Competitive interactions between forest trees are driven by species' trait hierarchy, not phylogenetic or functional similarity: implications for forest community assembly.

The relative importance of competition vs. environmental filtering in the assembly of communities is commonly inferred from their functional and phylogenetic structure, on the grounds that similar species compete most strongly for resources and are therefore less likely to coexist locally. This approach ignores the possibility that competitive effects can be determined by relative positions of species on a hierarchy of competitive ability. Using growth data, we estimated 275 interaction coefficients between tree species in the French mountains. We show that interaction strengths are mainly driven by trait hierarchy and not by functional or phylogenetic similarity. On the basis of this result, we thus propose that functional and phylogenetic convergence in local tree community might be due to competition-sorting species with different competitive abilities and not only environmental filtering as commonly assumed. We then show a functional and phylogenetic convergence of forest structure with increasing plot age, which supports this view.

Plant functional traits have globally consistent effects on competition

Phenotypic traits and their associated trade-offs have been shown to have globally consistent effects on individual plant physiological functions, but how these effects scale up to influence competition, a key driver of community assembly in terrestrial vegetation, has remained unclear. Here we use growth data from more than 3 million trees in over 140,000 plots across the world to show how three key functional traits—wood density, specific leaf area and maximum height—consistently influence competitive interactions. Fast maximum growth of a species was correlated negatively with its wood density in all biomes, and positively with its specific leaf area in most biomes. Low wood density was also correlated with a low ability to tolerate competition and a low competitive effect on neighbours, while high specific leaf area was correlated with a low competitive effect. Thus, traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies. Competition within species was stronger than between species, but an increase in trait dissimilarity between species had little influence in weakening competition. No benefit of dissimilarity was detected for specific leaf area or wood density, and only a weak benefit for maximum height. Our trait-based approach to modelling competition makes generalization possible across the forest ecosystems of the world and their highly diverse species composition.

Prediction of plant species distributions across six millennia

The usefulness of species distribution models (SDMs) in predicting impacts of climate change on biodiversity is difficult to assess because changes in species ranges may take decades or centuries to occur. One alternative way to evaluate the predictive ability of SDMs across time is to compare their predictions with data on past species distributions. We use data on plant distributions, fossil pollen and current and mid-Holocene climate to test the ability of SDMs to predict past climate-change impacts. We find that species showing little change in the estimated position of their realized niche, with resulting good model performance, tend to be dominant competitors for light. Different mechanisms appear to be responsible for among-species differences in model performance. Confidence in predictions of the impacts of climate change could be improved by selecting species with characteristics that suggest little change is expected in the relationships between species occurrence and climate patterns.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

Summary 1 Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits.2 We developed two spatial predictor sets of remotely sensed and topo‐climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non‐parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics.3 More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo‐climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross‐validated accuracies for rare species.4 Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core‐satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species.5 Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change.