Filtern
Volltext vorhanden
- nein (18) (entfernen)
Dokumenttyp
- Wissenschaftlicher Artikel (14)
- Rezension (3)
- Preprint (1)
Gehört zur Bibliographie
- ja (18)
Schlagworte
- ecological niche modelling (2)
- Artificial neuronal network (1)
- Biodiversity monitoring (1)
- Biotic interactions (1)
- Boosted regression trees (1)
- Boosted regression trees (BRT) (1)
- Borneo (1)
- Borrelia afzelii (1)
- Borrelia lusitaniae (1)
- Cerrado (1)
- Classification trees (1)
- Climate change (1)
- Clonal plants (1)
- Coastal regions (1)
- Database (1)
- Deforestation (1)
- Dispersal (1)
- Distribution patterns (1)
- Driving forces (1)
- Earthworms (1)
- Ecosystem engineer (1)
- Ecosystem function (1)
- Feedback biotic-abiotic (1)
- Forest regeneration (1)
- Functional diversity (1)
- Functional effect (1)
- Functional traits (1)
- Generalized linear models (1)
- Hypothesis generation (1)
- Land use (1)
- Land-cover change (1)
- Landscape (1)
- Landslides (1)
- Lyme disease (1)
- Management (1)
- Mato Grosso (1)
- Maximum entropy method (1)
- Model comparison (1)
- Modeling (1)
- Multivariate adaptive regression splines (1)
- Oligochaeta (1)
- Orthoptera (1)
- Plant growth (1)
- Population dynamics (1)
- Random forests (1)
- SDM (1)
- Shrubland regeneration (1)
- Soil hydrology (1)
- Southeast Asia (1)
- Species distribution models (1)
- Statistical modeling (1)
- Tropical montane forests (1)
- Weighted model ensembles (1)
- biomass (1)
- carnivora (1)
- cash crops (1)
- climate change (1)
- conservation planning (1)
- drylands (1)
- ecohydrological modelling (1)
- ecohydrology (1)
- ecological forecasts (1)
- ecological theory (1)
- ecosystem change (1)
- feedback (1)
- geographical range shifts (1)
- global environmental change (1)
- hyporheic zone (1)
- land degradation (1)
- land use change (1)
- lizard (1)
- maximum entropy (MaxEnt) (1)
- mechanistic model (1)
- mechanistic models (1)
- meso-scale ecosystems (1)
- migration (1)
- organic layer (1)
- parameterization (1)
- pesticides (1)
- plant-animal-soil-system (1)
- process-based model (1)
- process-based statistics (1)
- river networks (1)
- sampling bias (1)
- shallow translational landslides (1)
- soil parameters (1)
- species distribution model (1)
- species distribution modelling (1)
- stratification (1)
- tropical montane forest (1)
- uncertainty (1)
- validation (1)
- viverridae (1)
- water quality (1)
- zooprophylaxis (1)
Institut
- Institut für Geowissenschaften (18) (entfernen)
The Middle Spotted Woodpecker (Dendrocopos medius) is the bird species which Germany has the greatest global responsibility to protect. It is an umbrella species for the entire assemblage of animals associated with mature broadleaved trees, especially oak. Even though well studied in small to medium scale stands, the validity of habitat suitability analysis for this species in larger forests has not previously been proved. Aim of this study was to test suitability of permanent forest inventory plots for modelling its distribution in a 17,000 ha forest landscape and to derive habitat threshold values as a basis for formulating management guidelines. Based on 150 randomly selected 12.5 ha plots we identified mean age and basal area of oaks as the most important habitat factors using a backward selection logistic model. Internal validation showed an AUC of 0.89 and a R-2(N) of 0.58. Determination of thresholds using maximally selected rank statistics found higher probability of occurrence in stands with a mean age >95 years. Above that age the probability increased again in stands with more than 6.4 m(2) basal area oak/ha. Our results show that widely available forest inventory data can serve as a valuable basis for monitoring the Middle Spotted Woodpecker, either within the framework of the Natura 2000 Network, or more generally in integrated forest management with the aim of providing suitable habitats for the entire assemblage of species on old deciduous trees, especially oak.
The Hazel Grouse Bonasa bonasia is strongly affected by forest dynamics, and populations in many areas within Europe are declining. As a result of the 'wilding' concept implemented in the National Park Bavarian Forest, this area is one of the refuges for the species in Germany. Even though the effects of prevailing processes make the situation there particularly interesting, no recent investigation about habitat selection in the rapidly changing environment of the national park has been undertaken. We modelled the species-habitat relationship to derive the important habitat features in the national park as well as factors and critical threshold for monitoring, and to evaluate the predictive power of models based on field surveys compared to an analysis of infrared aerial photographs. We conducted our surveys on 49 plots of 25 ha each where Hazel Grouse was recorded and on an equally sized set of plots with no grouse occurrence, and used this dataset to build a predictive habitat-suitability model using logistic regression with backward stepwise variable selection. Habitat heterogeneity, stand structure, presence of mountain ash and willow, root plates, forest aisles, and young broadleaf stands proved to be predictive habitat variables. After internal validation via bootstrapping, our model shows an AUC value of 0.91 and a correct classification rate of 87%. Considering the methodological difficulties attached to backward selection, we applied Bayesian model averaging as an alternative. This multi-model approach also yielded similar results. To derive simple thresholds for important predictors as a basis for management decisions, we alternatively ran tree-based modelling, which also leads to a very similar selection of predictors. Performance of our different survey approaches was assessed by comparing two independent models with a model including both data resources: one constructed only from field survey data, the other based on data derived from aerial photographs. Models based on field data seem to perform slightly better than those based on aerial photography, but models using both predictor datasets provided the highest predictive accuracy.
The globally threatened Aquatic Warbler Acrocephalus paludicola is an umbrella species for fen mires and is at risk of extinction in its westernmost breeding population due to severe habitat loss. We used boosted regression trees to model Aquatic Warbler habitat selection in order to make recommendations for effective management of the last remnant habitats. Habitat data were collected in the years 2004-2006 in all remaining breeding sites in Pomerania (eastern Germany and western Poland) as well as in recently abandoned sites. Models were validated using data from similar Aquatic Warbler habitats in Lithuania. The probability of occurrence of Aquatic Warblers in late May/early June was positively associated with low isolation from other occupied sites, less eutrophic conditions, a high proportion of area mown early in the preceding year, high availability of vegetation 60-70 cm high, high prey abundance and high habitat heterogeneity. Early summer land management is needed in the more productive sites to prevent habitat deterioration by succession to higher and denser vegetation. As this also poses a serious threat to broods, management that creates a mosaic of early and late used patches is recommended to preserve and restore productive Aquatic Warbler sites. In less productive sites, winter mowing can maintain suitable habitat conditions. Aquatic Warbler-friendly land use supports a variety of other threatened plant and animal species typical of fens and sedge meadows and can meet the economic interests of local land users.
Substantial investment in climate change research has led to dire predictions of the impacts and risks to biodiversity. The Intergovernmental Panel on Climate Change fourth assessment report(1) cites 28,586 studies demonstrating significant biological changes in terrestrial systems(2). Already high extinction rates, driven primarily by habitat loss, are predicted to increase under climate change(3-6). Yet there is little specific advice or precedent in the literature to guide climate adaptation investment for conserving biodiversity within realistic economic constraints(7). Here we present a systematic ecological and economic analysis of a climate adaptation problem in one of the world's most species-rich and threatened ecosystems: the South African fynbos. We discover a counterintuitive optimal investment strategy that switches twice between options as the available adaptation budget increases. We demonstrate that optimal investment is nonlinearly dependent on available resources, making the choice of how much to invest as important as determining where to invest and what actions to take. Our study emphasizes the importance of a sound analytical framework for prioritizing adaptation investments(4). Integrating ecological predictions in an economic decision framework will help support complex choices between adaptation options under severe uncertainty. Our prioritization method can be applied at any scale to minimize species loss and to evaluate the robustness of decisions to uncertainty about key assumptions.
Questions: Which are the factors that influence forest and shrubland loss and regeneration and their underlying drivers?
Location: Central Chile, a world biodiversity hotspot.
Methods: Using land-cover data from the years 1975, 1985, 1999 and 2008, we fitted classification trees and multiple logistic regression models to account for the relationship between different trajectories of vegetation change and a range of biophysical and socio-economic factors.
Results: The variables that most consistently showed significant effects on vegetation change across all time-intervals were slope and distance to primary roads. We found that forest and shrubland loss on one side and regeneration on the other often displayed opposite patterns in relation to the different explanatory variables. Deforestation was positively related to distance to primary roads and to distance within forest edges and was favoured by a low insolation and a low slope. In turn, forest regeneration was negatively related to the distance to primary roads and positively to the distance to the nearest forest patch, insolation and slope. Shrubland loss was positively influenced by slope and distance to cities and primary roads and negatively influenced by distance to rivers. Conversely, shrubland regeneration was negatively related to slope, distance to cities and distance to primary roads and positively related to distance from existing forest patches and distance to rivers.
Conclusions: This article reveals how biophysical and socioeconomic factors influence vegetation cover change and the underlying social, political and economical drivers. This assessment provides a basis for management decisions, considering the crucial role of perennial vegetation cover for sustaining biodiversity and ecosystem services.
Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlativeprocess spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.
How to understand species' niches and range dynamics: a demographic research agenda for biogeography
(2012)
Range dynamics causes mismatches between a species geographical distribution and the set of suitable environments in which population growth is positive (the Hutchinsonian niche). This is because sourcesink population dynamics cause species to occupy unsuitable environments, and because environmental change creates non-equilibrium situations in which species may be absent from suitable environments (due to migration limitation) or present in unsuitable environments that were previously suitable (due to time-delayed extinction). Because correlative species distribution models do not account for these processes, they are likely to produce biased niche estimates and biased forecasts of future range dynamics. Recently developed dynamic range models (DRMs) overcome this problem: they statistically estimate both range dynamics and the underlying environmental response of demographic rates from species distribution data. This process-based statistical approach qualitatively advances biogeographical analyses. Yet, the application of DRMs to a broad range of species and study systems requires substantial research efforts in statistical modelling, empirical data collection and ecological theory. Here we review current and potential contributions of these fields to a demographic understanding of niches and range dynamics. Our review serves to formulate a demographic research agenda that entails: (1) advances in incorporating process-based models of demographic responses and range dynamics into a statistical framework, (2) systematic collection of data on temporal changes in distribution and abundance and on the response of demographic rates to environmental variation, and (3) improved theoretical understanding of the scaling of demographic rates and the dynamics of spatially coupled populations. This demographic research agenda is challenging but necessary for improved comprehension and quantification of niches and range dynamics. It also forms the basis for understanding how niches and range dynamics are shaped by evolutionary dynamics and biotic interactions. Ultimately, the demographic research agenda should lead to deeper integration of biogeography with empirical and theoretical ecology.
Landslides are a hazard for humans and artificial structures. From an ecological point of view, they represent an important ecosystem disturbance, especially in tropical montane forests. Here, shallow translational landslides are a frequent natural phenomenon and one local determinant of high levels of biodiversity. In this paper, we apply weighted ensembles of advanced phenomenological models from statistics and machine learning to analyze the driving factors of natural landslides in a tropical montane forest in South Ecuador. We exclusively interpret terrain attributes, derived from a digital elevation model, as proxies to several driving factors of landslides and use them as predictors in our models which are trained on a set of five historical landslide inventories. We check the model generality by transferring them in time and use three common performance criteria (i.e. AUC, explained deviance and slope of model calibration curve) to, on the one hand, compare several state-of-the-art model approaches and on the other hand, to create weighted model ensembles. Our results suggest that it is important to consider more than one single performance criterion.
Approaching our main question, we compare responses of weighted model ensembles that were trained on distinct functional units of landslides (i.e. initiation, transport and deposition zones). This way, we are able to show that it is quite possible to deduce driving factors of landslides, if the consistency between the training data and the processes is maintained. Opening the 'black box' of statistical models by interpreting univariate model response curves and relative importance of single predictors regarding their plausibility, we provide a means to verify this consistency.
With the exception of classification tree analysis, all techniques performed comparably well in our case study while being outperformed by weighted model ensembles. Univariate response curves of models trained on distinct functional units of landslides exposed different shapes following our expectations. Our results indicate the occurrence of landslides to be mainly controlled by factors related to the general position along a slope (i.e. ridge, open slope or valley) while landslide initiation seems to be favored by small scale convexities on otherwise plain open slopes.
Detailed knowledge on the spatial distribution of soils is crucial for environmental monitoring, management, and modeling. However soil maps with a finite number of discrete soil map units are often the only available information about soils. Depending on the map scale or the detailing of the map legend this information could be too imprecise. We present a method for the spatial disaggregation of map units, namely the refinement of complex soil map units in which two or more soil types are aggregated. Our aim is to draw new boundaries inside the map polygons to represent a single soil type and no longer a mixture of several soil types. The basic idea for our method is the functional relationship between soil types and topographic position as formulated in the concept of the catena. We use a comprehensive soil profile database and topographic attributes derived from a 10 m digital elevation model as input data for the classification of soil types with random forest models. We grouped all complex map units which have the same combination of soil types. Each group of map units is modeled separately. For prediction of the soil types we stratified the soil map into these groups and apply a specific random forest model only to the associated map units. In order to get reliable results we define a threshold for the predicted probabilities at 0.7 to assign a specific soil type. In areas where the probability is below 0.7 for every possible soil type we assign a new class "indifferent" because the model only makes unspecific classification there. Our results show a significant spatial refinement of the original soil polygons. Validation of our predictions was estimated on 1812 independent soil profiles which were collected subsequent to prediction in the field. Field validation gave an overall accuracy of 70%. Map units, in which shallow soils were grouped together with deep soils could be separated best. Also histosols could be predicted successful. Highest error rate were found in map units, in which Gleysoils were grouped together with deep soils or Anthrosols. To check for validity of our results we open the black box random forest model by calculating the variable importance for each predictor variable and plotting response surfaces. We found good confirmations of our hypotheses, that topography has a significant influence on the spatial arrangement of soil types and that these relationships can be used for disaggregation.