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In a selected literature survey we reviewed studies on the habitat heterogeneity-animal species diversity relationship and evaluated whether there are uncertainties and biases in its empirical support. We reviewed 85 publications for the period 1960-2003. We screened each publication for terms that were used to define habitat heterogeneity, the animal species group and ecosystem studied, the definition of the structural variable, the measurement of vegetation structure and the temporal and spatial scale of the study. The majority of studies found a positive correlation between habitat heterogeneity/diversity and animal species diversity. However, empirical support for this relationship is drastically biased towards studies of vertebrates and habitats under anthropogenic influence. In this paper we show that ecological effects of habitat heterogeneity may vary considerably between species groups depending on whether structural attributes are perceived as heterogeneity or fragmentation. Possible effects may also vary relative to the structural variable measured. Based upon this, we introduce a classification framework that may be used for across-studies comparisons. Moreover, the effect of habitat heterogeneity for one species group may differ in relation to the spatial scale. In several studies, however, different species groups are closely linked to 'keystone structures' that determine animal species diversity by their presence. Detecting crucial keystone structures of the vegetation has profound implications for nature conservation and biodiversity management.
Shrub encroachment linked to heavy grazing has dramatically changed savanna landscapes, and is a major form of rangeland degradation. Our understanding of how shrub encroachment affects arthropod communities is poor, however. Here, we investigate the effects of shrub encroachment on abundance and diversity of ground-dwelling (wingless) arthropods at varying levels of shrub cover in the southern Kalahari. We also ascertain if invertebrate assemblage composition changes with habitat structure and identify which aspects of habitat structure (e.g., grass cover, herbaceous plant cover, shrub density) correlate most strongly with these changes. Ant, scorpion and dung beetle abundance increased with shrub cover, whereas grasshoppers and solifuges declined. Spider and beetle abundance exhibited hump-shaped relationships with shrub cover. RTU richness within orders either mirrored abundances, or exhibited no trend. Shrub density was the habitat component most correlated with similarities between invertebrate assemblages. Ground-dwelling arthropods showed clear shifts in species assemblage composition at a similarity level of 65% according to shrub density. Changes in indicator species showed that within the Tenebrionidae (darkling beetles), certain species respond positively to shrub thickening, replacing other species within the Family. Small-bodied, wingless Scarabaeidae (dung beetles) tended to increase with increased shrub density and three species emerged as significant indicators of more thickened habitats, although this might be a response to greater dung availability, rather than habitat structure itself. We conclude that because ground- dwelling invertebrates showed such clear responses in species assemblage composition, they present excellent candidates for use as indicator species in further studies into bush encroachment.
While several empirical and theoretical studies have clearly shown the negative effects of climate or landscape changes on population and species survival only few of them addressed combined and correlated consequences of these key environmental drivers. This also includes positive landscape changes such as active habitat management and restoration to buffer the negative effects of deteriorating climatic conditions. In this study, we apply a conceptual spatial modelling approach based on functional types to explore the effects of both positive and negative correlations between changes in habitat and climate conditions on the survival of spatially structured populations. We test the effect of different climate and landscape change scenarios on four different functional types that represent a broad spectrum of species characterised by their landscape level carrying capacity, the local population turnover rates at the patch level (K-strategies vs. r-strategies) and dispersal characterstics. As expected, simulation results show that correlated landscape and climatic changes can accelerate (in case of habitat loss or degradation) or slow down (in case of habitat gain or improvement) regional species extinction. However, the strength of the combined changes depends on local turnover at the patch level, the overall landscape capacity of the species, and its specific dispersal characteristics. Under all scenarios of correlated changes in habitat and climate conditions we found the highest sensitivity for functional types representing species with a low landscape capacity but a high population growth rate and a strong density regulation causing a high turnover at the local patch level.
The relative importance of habitat loss or habitat degradation, in combination with climate deterioration, differed among the functional types. However, an increase in regional capacity revealed a similar response pattern: For all types, habitat improvement led to higher survival times than habitat gain, i.e. the establishment of new habitat patches. This suggests that improving local habitat quality at a regional scale is a more promising conservation strategy under climate change than implementing new habitat patches. This conceptual modelling study provides a general framework to better understand and support the management of populations prone to complex environmental changes.
Recent theoretical studies have shown contrasting effects of temporal correlation of environmental fluctuations ( red noise) on the risk of population extinction. It is still debated whether and under which conditions red noise increases or decreases extinction risk compared with uncorrelated ( white) noise. Here, we explain the opposing effects by introducing two features of red noise time series. On the one hand, positive autocorrelation increases the probability of series of poor environmental conditions, implying increasing extinction risk. On the other hand, for a given time period, the probability of at least one extremely bad year ("catastrophe") is reduced compared with white noise, implying decreasing extinction risk. Which of these two features determines extinction risk depends on the strength of environmental fluctuations and the sensitivity of population dynamics to these fluctuations. If extreme ( catastrophic) events can occur ( strong noise) or sensitivity is high ( overcompensatory density dependence), then temporal correlation decreases extinction risk; otherwise, it increases it. Thus, our results provide a simple explanation for the contrasting previous findings and are a crucial step toward a general understanding of the effect of noise color on extinction risk
The impact of temporally correlated fluctuating environments (coloured noise) on the extinction risk of populations has become a main focus in theoretical population ecology. In this study we particularly focus on the extinction risk in strongly autocorrelated environments. Here, in contrast to moderate autocorrelation, we found the extinction risk to be highly dependent on the process of noise generation, in particular on the method of variance scaling. Such variance scaling is commonly applied to avoid variance-driven biases when comparing the extinction risk for white and coloured noise. In this study we found an often-used scaling technique to lead to high variability in the resulting variances of different time series for strong auto-correlation eventually leading to deviations in the projected extinction risk. Therefore, we present an alternative method that always delivers the target variance, even in the case of strong temporal correlation. Furthermore, in contrast to the earlier method, our very intuitive method is not bound to auto-regressive processes but can be applied to all types of coloured noises. We recommend the method introduced here to be used when the target of interest is the effect of noise colour on extinction risk not obscured by any variance effects.
Understanding mechanisms to predict changes in plant and animal communities is a key challenge in ecology. The need to transfer knowledge gained from single species to a more generalized approach has led to the development of categorization systems where species' similarities in life strategies and traits are classified into ecological groups (EGs) like functional groups/types or guilds. While approaches in plant ecology undergo a steady improvement and refinement of methodologies, progression in animal ecology is lagging behind. With this review, we aim to initiate a further development of functional classification systems in animal ecology, comparable to recent developments in plant ecology. We here (i) give an overview of terms and definitions of EGs in animal ecology, (ii) discuss existing classification systems, methods and application areas of EGs (focusing on terrestrial vertebrates), and (iii) provide a "roadmap towards an animal functional type approach" for improving the application of EGs and classifications in animal ecology. We found that an animal functional type approach requires: (i) the identification of core traits describing species' dependency on their habitat and life history traits, (ii) an optimization of trait selection by clustering traits into hierarchies, (iii) the assessment of "soft traits" as substitute for hardly measurable traits, e.g. body size for dispersal ability, and (iv) testing of delineated groups for validation including experiments.
In semi-arid savannas, unsustainable land use can lead to degradation of entire landscapes, e.g. in the form of shrub encroachment. This leads to habitat loss and is assumed to reduce species diversity. In BIOTA phase 1, we investigated the effects of land use on population dynamics on farm scale. In phase 2 we scale up to consider the whole regional landscape consisting of a diverse mosaic of farms with different historic and present land use intensities. This mosaic creates a heterogeneous, dynamic pattern of structural diversity at a large spatial scale. Understanding how the region-wide dynamic land use pattern affects the abundance of animal and plant species requires the integration of processes on large as well as on small spatial scales. In our multidisciplinary approach, we integrate information from remote sensing, genetic and ecological field studies as well as small scale process models in a dynamic region-wide simulation tool. <hr> Interdisziplinäres Zentrum für Musterdynamik und Angewandte Fernerkundung Workshop vom 9. - 10. Februar 2006.
Plant population modelling has been around since the 1970s, providing a valuable approach to understanding plant ecology from a mechanistic standpoint. It is surprising then that this area of research has not grown in prominence with respect to other approaches employed in modelling plant systems. In this review, we provide an analysis of the development and role of modelling in the field of plant population biology through an exploration of where it has been, where it is now and, in our opinion, where it should be headed. We focus, in particular, on the role plant population modelling could play in ecological forecasting, an urgent need given current rates of regional and global environmental change. We suggest that a critical element limiting the current application of plant population modelling in environmental research is the trade-off between the necessary resolution and detail required to accurately characterize ecological dynamics pitted against the goal of generality, particularly at broad spatial scales. In addition to suggestions how to overcome the current shortcoming of data on the process-level we discuss two emerging strategies that may offer a way to overcome the described limitation: (1) application of a modern approach to spatial scaling from local processes to broader levels of interaction and (2) plant functional-type modelling. Finally we outline what we believe to be needed in developing these approaches towards a 'science of forecasting'.