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Empirical species distribution models (SDMs) constitute often the tool of choice for the assessment of rapid climate change effects on species vulnerability. Conclusions regarding extinction risks might be misleading, however, because SDMs do not explicitly incorporate dispersal or other demographic processes. Here, we supplement SDMs with a dynamic population model 1) to predict climate-induced range dynamics for black grouse in Switzerland, 2) to compare direct and indirect measures of extinction risks, and 3) to quantify uncertainty in predictions as well as the sources of that uncertainty. To this end, we linked models of habitat suitability to a spatially explicit, individual-based model. In an extensive sensitivity analysis, we quantified uncertainty in various model outputs introduced by different SDM algorithms, by different climate scenarios and by demographic model parameters. Potentially suitable habitats were predicted to shift uphill and eastwards. By the end of the 21st century, abrupt habitat losses were predicted in the western Prealps for some climate scenarios. In contrast, population size and occupied area were primarily controlled by currently negative population growth and gradually declined from the beginning of the century across all climate scenarios and SDM algorithms. However, predictions of population dynamic features were highly variable across simulations. Results indicate that inferring extinction probabilities simply from the quantity of suitable habitat may underestimate extinction risks because this may ignore important interactions between life history traits and available habitat. Also, in dynamic range predictions uncertainty in SDM algorithms and climate scenarios can become secondary to uncertainty in dynamic model components. Our study emphasises the need for principal evaluation tools like sensitivity analysis in order to assess uncertainty and robustness in dynamic range predictions. A more direct benefit of such robustness analysis is an improved mechanistic understanding of dynamic species responses to climate change.
AimAdvancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better-surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo.
LocationBorneo, Southeast Asia.
MethodsWe collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range-restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north-eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas.
ResultsSpatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased.
Main ConclusionsWe conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.
The Brazilian Cerrado is recognized as one of the most threatened biomes in the world, as the region has experienced a striking change from natural Cerrado vegetation to intense cash crop production. This paper reviews the history of land conversion in the Cerrado and the development of soil properties and water resources under past and ongoing land use. We compared soil and water quality parameters from different land uses considering 80 soil and 18 water studies conducted in different regions across the Cerrado to provide quantitative evidence of soil and water alterations from land use change.
Following the conversion of native Cerrado, significant effects on soil pH, bulk density and available P and K for croplands and less-pronounced effects on pastures were evident. Soil total N did not differ between land uses because most of the sites classified as croplands were nitrogen-fixing soybeans, which are not artificially fertilized with N. In contrast, water quality studies showed nitrogen enrichment in agricultural catchments, indicating fertilizer impacts and potential susceptibility to eutrophication. Regardless of the land use, P is widely absent because of the high-fixing capacities of deeply weathered soils and the filtering capacity of riparian vegetation. Pesticides, however, were consistently detected throughout the entire aquatic system. In several case studies, extremely high-peak concentrations exceeded Brazilian and European Union (EU) water quality limits, which were potentially accompanied by serious health implications. Land use intensification is likely to continue, particularly in regions where less annual rainfall and severe droughts are projected in the northeastern and western Cerrado. Thus, the leaching risk and displacement of agrochemicals are expected to increase, particularly because the current legislation has caused a reduction in riparian vegetation. We conclude that land use intensification is likely to seriously limit the Cerrado's future regarding both agricultural productivity and ecosystem stability. Because only limited data are available, we recommend further field studies to understand the interaction between terrestrial and aquatic systems. This study may serve as a valuable database for integrated modelling to investigate the impact of land use and climate change on soil and water resources and to test and develop mitigation measures for the Cerrado. Copyright (C) 2014 John Wiley & Sons, Ltd.
To determine whether the genospecies composition of Lyme disease spirochetes is spatially stratified, we collected questing Ixodes ricinus ticks in neighboring plots where rodents, birds, and lizards were present as reservoir host and compared the prevalence of various genospecies. The overall prevalence of spirochetes in questing ticks varied across the study site. Borrelia lusitaniae appeared to infect adult ticks in one plot at the same frequency as did Borrelia afzelii in the other plots. The relative density of questing nymphal and adult ticks varied profoundly. Where lizards were exceedingly abundant, these vertebrates seemed to constitute the dominant host for nymphal ticks, contributing the majority of infected adult ticks. Because lizards support solely B.lusitaniae and appear to exclude other genospecies, their narrow genospecies association results in predominance of B.lusitaniae in sites where lizards are abundant, while limiting its spread to the host's habitat range. To the extent that Central European B.lusitaniae strains are nonpathogenic, the presence of numerous lizards should locally decrease risk of infection for people. Evaluation of regional risk of infection by Lyme disease spirochetes should take the spatial effect of hosts into consideration, which stratify the distribution of specifically infected ticks on a small scale.
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.
Ecohydrology analyses the interactions of biotic and abiotic aspects of our ecosystems and landscapes. It is a highly diverse discipline in terms of its thematic and methodical research foci. This article gives an overview of current German ecohydrological research approaches within plant-animal-soil-systems, meso-scale catchments and their river networks, lake systems, coastal areas and tidal rivers. It discusses their relevant spatial and temporal process scales and different types of interactions and feedback dynamics between hydrological and biotic processes and patterns. The following topics are considered key challenges: innovative analysis of the interdisciplinary scale continuum, development of dynamically coupled model systems, integrated monitoring of coupled processes at the interface and transition from basic to applied ecohydrological science to develop sustainable water and land resource management strategies under regional and global change.
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.
Perspectives in modelling earthworm dynamics and their feedbacks with abiotic soil properties
(2012)
Effects of earthworms on soil abiotic properties are well documented from several decades of laboratory and mesocosm experiments, and they are supposed to affect large-scale soil ecosystem functioning. The prediction of the spatiotemporal occurrence of earthworms and the related functional effects in the field or at larger scales, however, is constrained by adequate modelling approaches. Correlative, phenomenological methods, such as species distribution models, facilitate the identification of factors that drive species' distributions. However, these methods ignore the ability of earthworms to select and modify their own habitat and therefore may lead to unreliable predictions. Understanding these feedbacks between earthworms and abiotic soil properties is a key requisite to better understand their spatiotemporal distribution as well as to quantify the various functional effects of earthworms in soil ecosystems. Process-based models that investigate either effects or responses of earthworms on soil environmental conditions are mostly applied in ecotoxicological and bioturbation studies. Process-based models that describe feedbacks between earthworms and soil abiotic properties explicitly are rare. In this review, we analysed 18 process-based earthworm dynamic modelling studies pointing out the current gaps and future challenges in feedback modelling. We identify three main challenges: (i) adequate and reliable process identification in model development at and across relevant spatiotemporal scales (individual behaviour and population dynamics of earthworms), (ii) use of information from different data sources in one model (laboratory or field experiments, earthworm species or functional type) and (iii) quantification of uncertainties in data (e.g. spatiotemporal variability of earthworm abundances and soil hydraulic properties) and derived parameters (e.g. population growth rate and hydraulic conductivity) that are used in the model.
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.