Spatial patterns of macroinvertebrate functional feeding groups in streams in relation to physical variables and land-cover in Southwestern France

Artificial neural networks were used to quantify the distribution of macroinvertebrate functional feeding groups (FFGs) in relation to physical variables and to land-cover in the Adour–Garonne stream system (SW France; 116,000 km²). The relative abundances of 5 FFGs were calculated from macroinvertebrate data recorded at 165 sampling sites. Each site was characterized using 5 physical variables (elevation, stream order, stream width, distance from the source, slope) and 3 land-cover variables (% forested, % urban areas, % agricultural areas). The sites were first classified using the Self-Organizing Map algorithm (SOM), according to the physical and land-cover variables. Two major clusters of sites corresponded to anthropogenically modified and natural areas, respectively. Anthropogenically modified areas were clearly divided into agricultural and urban landscapes. Each major cluster was divided into 3–4 subsets of sites according to a topographic gradient of physical variables. To examine the variability of the communities, FFG proportions at the 165 sites were examined on the SOM trained with physical and land-cover variables. When the riverine landscape was natural, FFG patterns responded to the upstream–downstream gradient in physical variables. When the landscape was altered by agriculture or urbanization, the effects of land-cover on FFGs overcame the influence of the physical variables. The categorization of the landscape into forested, agricultural, and urban areas was relevant to detect changes in FFG patterns. In light of increasing development along riparian zones, the use of SOMs to detect responses of FFGs to landscape alterations at regional scales exemplifies an effective technique for assessing river health based on ecological indicator groups.     

Do small-grain processes matter for landscape scale questions? Sensitivity of a forest landscape model to the formulation of tree growth rate

Process-based forest landscape models are valuable tools for testing basic ecological theory and for projecting how forest landscapes may respond to climate change and other environmental shifts. However, the ability of these models to accurately predict environmentally-induced shifts in species distributions as well as changes in forest composition and structure is often contingent on the phenomenological representation of individual-level processes accurately scaling-up to landscape-level community dynamics. We use a spatially explicit landscape forest model (LandClim) to examine how three alternative formulations of individual tree growth (logistic, Gompertz, and von Bertalanffy) influence model results. Interactions between growth models and landscape characteristics (landscape heterogeneity and disturbance intensity) were tested to determine in what type of landscape simulation results were most sensitive to growth model structure. We found that simulation results were robust to growth function formulation when the results were assessed at a large spatial extent (landscape) and when coarse response variables, such as total forest biomass, were examined. However, results diverged when more detailed response variables, such as species composition within elevation bands, were considered. These differences were particularly prevalent in regions that included environmental transition zones where forest composition is strongly driven by growth-dependent competition. We found that neither landscape heterogeneity nor the intensity of landscape disturbances accentuated simulation sensitivity to growth model formulation. Our results indicate that at the landscape extent, simulation results are robust, but the reliability of model results at a finer resolution depends critically on accurate tree growth functions.     

Landscape regularity modelling for environmental challenges in agriculture

In agricultural landscapes, methods to identify and describe meaningful landscape patterns play an important role to understand the interaction between landscape organization and ecological processes. We propose an innovative stochastic modelling method of agricultural landscape organization where the temporal regularities in land-use are first identified through recognized Land-Use Successions before locating these successions in landscapes. These time–space regularities within landscapes are extracted using a new data mining method based on Hidden Markov Models. We applied this methodological proposal to the Niort Plain (West of France). We built a temporo-spatial analysis for this case study through spatially explicit analysis of Land-Use Succession dynamics. Implications and perspectives of such an approach, which links together the temporal and the spatial dimensions of the agricultural organization, are discussed by assessing the relationship between the agricultural landscape patterns defined using this approach and ecological data through an illustrative example of bird nests.     

Multi-scaled habitat considerations for conserving urban biodiversity: native reptiles and small mammals in Brisbane,Australia

The rapid expansion of the world’s urban population is a major driver of contemporary landscape change and ecosystem modification. Urbanisation destroys, degrades and fragments native ecosystems, replacing them with a heterogeneous matrix of urban development, parks, roads, and isolated remnant fragments of varying size and quality. This presents a major challenge for biodiversity conservation within urban areas. To make spatially explicit decisions about urban biodiversity conservation actions, urban planners and managers need to be able to separate the relative influence of landscape composition and configuration from patch and local (site)-scale variables for a range of fauna species. We address this problem using a hierarchical landscape approach for native, terrestrial reptiles and small mammals living in a fragmented semi-urban landscape of Brisbane, Australia. Generalised linear modelling and hierarchical partitioning analysis were applied to quantify the relative influence of landscape composition and configuration, patch size and shape, and local habitat composition and structure on the species’ richness of mammal and reptile assemblages. Landscape structure (composition and configuration) and local-scale habitat structure variables were found to be most important for influencing reptile and mammal assemblages, although the relative importance of specific variables differed between reptile and mammal assemblages. These findings highlight the importance of considering landscape composition and configuration in addition to local habitat elements when planning and/or managing for the conservation of native, terrestrial fauna diversity in urban landscapes.     

Potential impacts of changing agricultural activities on scenic beauty – a prototypical technique for automated rapid assessment

As a result of the liberalisation of the agricultural market, mountain regions in Central Europe are at great risk of experiencing increasing land abandonment and spontaneous reforestation. Prior to taking measures for landscape maintenance, the ecological and landscape-aesthetic consequences of land abandonment should be analysed. This paper addresses the aesthetic component of such analyses: we investigated whether lay people perceive land abandonment and spontaneous reforestation as a loss or a gain and developed a prototypical technique for rapid aesthetic assessment of reforestation scenarios for vast regions.First, we conducted image experiments to assess the respondents' reactions to increasing levels of reforestation. Based on these experiments we concluded that a medium degree of reforestation is most desirable. Second, we analysed the relationship between scenic beauty and landscape patterns and found that landscape preference values correlate significantly with various quantitative measures of the landscape pattern (e.g., diversity and contagion indices of grey- tone and colour images). Third, we applied a GIS-assisted moving- window technique to transform spatially explicit remote-sensing data (in particular orthophotos) of a test region to spatially explicit data of landscape-pattern indices. Thanks to the significant positive correlation between pattern indices and landscape preference values, the resulting maps can preliminarily be interpreted as beauty-maps of the test-region.     

A frame-based spatially explicit model of subarctic vegetation response to climatic change: comparison with a point model

An important challenge in global-change research is to simulate short-term transient changes in climate, disturbance regime, and recruitment that drive long-term vegetation distributions. Spatial features (e.g., topographic barriers) and processes, including disturbance propagation and seed dispersal, largely control these short-term transient changes. Here we present a frame-based spatially explicit model (ALFRESCO) that simulates landscape-level response of vegetation to transient changes in climate and explicitly represents the spatial processes of disturbance propagation and seed dispersal. The spatial model and the point model from which it was developed showed similar results in some cases, but diverged in situations where interactions among neighboring cells (fire spread and seed dispersal) were crucial. Topographic barriers had little influence on fire size in low-flammability vegetation types, but reduced the average fire size and increased the number of fires in highly flammable vegetation (dry grassland). Large fires were more common in landscapes with large contiguous patches of two vegetation types while a more heterogeneous vegetation distribution increased fires in the less flammable vegetation type. When climate was held constant for thousands of years on a hypothetical landscape with the same initial vegetation, the spatial and point models produced identical results for some climates (cold, warm, and hot mesic), but produced markedly different results at current climate and when much drier conditions were imposed under a hot climate. Spruce migration into upland tundra was slowed or prevented by topographic barriers, depending on the size of the corridor. We suggest that frame-based, spatially explicit models of vegetation response to climate change are a useful tool to investigate both short- and long-term transients in vegetation at the regional scale. We also suggest that it is difficult to anticipate when non-spatial models will be reliable and when spatially explicit models are essential. ALFRESCO provides an important link between models of landscape-level vegetation dynamics and larger spatio-temporal models of global climate change.     

Highlighting order and disorder in social–ecological landscapes to foster adaptive capacity and sustainability

Landscape sustainability can be considered in terms of order and disorder, where order implies causality, well-defined boundaries and predictable outcomes, while disorder implies uncertain causality, shifting boundaries and often-unpredictable outcomes. We address the interplay of order and disorder in social–ecological landscapes (SELs) using spatiotemporal analysis of entropy-related indices of Normalized Difference Vegetation Index time-series. These indices can provide insights for complex systems analysis for the evaluation of adaptive capacity in SELs. In particular, our overarching aim is to help interpret what an increase of order/disorder means with regards to SELs and the underlying drivers and causes of conditions in SELs. The approach can be used to increase spatially explicit anticipatory capability in environmental science and natural resource management based on how the system has responded to stress in the past. Such capability is crucial to address SEL adaptive capacity and for sustainable planning given that surprises may increase as a consequence of both climate change and multiple interacting anthropogenic stressors. These advancements should greatly contribute to the application of spatial resilience strategies in general, and to sustainable landscape planning in particular, and for the spatially explicit adaptive comanagement of ecosystem services.     

Socioeconomics drive woody invasive plant richness in New England, USA through forest fragmentation

Woody invasive plants are an increasing component of the New England flora. Their success and geographic spread are mediated in part by landscape characteristics. We tested whether woody invasive plant richness was higher in landscapes with many forest edges relative to other forest types and explained land use/land cover and forest fragmentation patterns using socioeconomic and physical variables. Our models demonstrated that woody invasive plant richness was higher in landscapes with more edge forest relative to patch, perforated, and especially core forest types. Using spatially-explicit, hierarchical Bayesian, compositional data models we showed that infrastructure and physical factors, including road length and elevation range, and time-lagged socioeconomic factors, primarily population, help to explain development and forest fragmentation patterns. Our social–ecological approach identified landscape patterns driven by human development and linked them to increased woody plant invasions. Identifying these landscape patterns will aid ongoing efforts to use current distribution patterns to better predict where invasive species may occur in unsampled regions under current and future conditions.     

Vegetation Composition and Succession of Abandoned Farmland: Effects of Ecological,Historical and Spatial Factors

In North America, as well as in Europe, the mechanization and the modernization of the agricultural activities had strongly modified the agricultural landscapes. Originating from these transformations of the agricultural environment, abandoned farmlands remain poorly known environments. A holistic approach, including the analysis of ecological, historical and spatial factors, is used in order to understand the dynamics of these environments created by agricultural abandonment in southwestern Québec (Canada). The analysis of 36 abandoned farmlands in the study area reveals the existence of two ecological groups. The first ecological group is composed by abandoned farmlands originating from pasture and at the moment dominated by a spiny shrub vegetation. The second ecological group includes past cultivated field at the moment dominated by either hydric herbaceous and shrub vegetation. Abiotic ecological variables such as slope, surface stoniness, canopy opening and soil pH, as well as land-use history, and age of the abandoned farmlands, are major factors explaining the current state of abandoned farmlands. Succession vector analysis reveals a strong differentiation among abandoned farmlands as to their tree species regeneration. Possible management alternatives, such as reforestation, are proposed in order to integrate those new environments to the modern agricultural landscape.     

The impacts of historical land-use and landscape variables on hollow-bearing trees along an urbanisation gradient

Hollow-bearing trees provide habitat for diverse taxonomic groups and as such they are recognised for their importance globally. There is, however scant reference to this resource relative within urban forest patches. The functional ecology of habitat remnants along an urbanisation gradient plays an important ecological, social and economic role within urban landscapes. Here we quantify the impacts of urbanisation, landscape, environmental, disturbance (past and present) and stand variables on hollow-bearing tree density within urban forest patches. This was undertaken by surveying 45 forest patches on the Gold Coast, south-east Queensland, Australia. Sites were categorised as; urban, peri-urban or rural along an urbanisation gradient, with an additional five control sites. Historical logging practices were found to be the driving factor influencing hollow-bearing tree density along the urbanisation gradient; while the impacts of urbanisation itself are not as yet discernible. These findings highlight the significance of incorporating historical land use practise into current and future urban planning, as these will have continuing impacts on remaining urban biodiversity values. These findings, will benefit natural resource managers and urban planners when making decisions about where and how best to manage for hollow-bearing trees along urbanisation gradients.     

Optimizing landscape selection for estimating relative effects of landscape variables on ecological responses

Empirical studies of the relative effects of landscape variables may compromise inferential strength with common approaches to landscape selection. We propose a methodology for landscape sample selection that is designed to overcome some common statistical pitfalls that may hamper estimates of relative effects of landscape variables on ecological responses. We illustrate our proposed methodology through an application aimed at quantifying the relationships between farmland heterogeneity and biodiversity. For this project, we required 100 study landscapes that represented the widest possible ranges of compositional and configurational farmland heterogeneity, where these two aspects of heterogeneity were quantified as crop cover diversity (Shannon diversity index) and mean crop field size, respectively. These were calculated at multiple spatial extents from a detailed map of the region derived through satellite image segmentation and classification. Potential study landscapes were then selected in a structured approach such that: (1) they represented the widest possible range of both heterogeneity variables, (2) they were not spatially autocorrelated, and (3) there was independence (no correlation) between the two heterogeneity variables, allowing for more precise estimates of the regression coefficients that reflect their independent effects. All selection criteria were satisfied at multiple extents surrounding the study landscapes, to allow for multi-scale analysis. Our approach to landscape selection should improve the inferential strength of studies estimating the relative effects of landscape variables, particularly those with a view to developing land management guidelines.     

Separating the effects of habitat area,fragmentation and matrix resistance on genetic differentiation in complex landscapes

Little is known about how variation in landscape mosaics affects genetic differentiation. The goal of this paper is to quantify the relative importance of habitat area and configuration, as well as the contrast in resistance between habitat and non-habitat, on genetic differentiation. We hypothesized that habitat configuration would be more influential than habitat area in influencing genetic differentiation. Population size is positively related to habitat area, and therefore habitat area should affect genetic drift, but not gene flow. In contrast, differential rates and patterns of gene flow across a landscape should be related to habitat configuration. Using spatially explicit, individual-based simulation modeling, we found that habitat configuration had stronger relationships with genetic differentiation than did habitat area, but there was a high degree of confounding between the effects of habitat area and configuration. We evaluated the predictive ability of six widely used landscape metrics and found that patch cohesion and correlation length of habitat are among the strongest individual predictors of genetic differentiation. Correlation length, patch density and clumpy are the most parsimonious set of variables to predict the magnitude of genetic differentiation in complex landscapes.     

Scale-Dependent Influence of Topography-Based Hydrologic Features on Patterns of Woody Plant Encroachment in Savanna Landscapes

Rainfall in drylands is erratic. Topographic features of landscapes can dampen or amplify temporal variability by spatially influencing patterns of water loss and accumulation. The extent to which portions of a landscape may differentially capture or retain scarce water and nutrient resources is an important determinant of vegetation patterns, particularly with respect to the distribution of woody plants. We therefore hypothesized that historic changes in woody cover on landscapes experiencing similar climate and disturbance regimes would vary with catena-to-catena (hillslope-to-hillslope) variation in topography-based hydrologic features. We tested this hypothesis by comparing topographic wetness index (TWI) values on replicate landscapes where woody plant abundance has increased over the past 100 yr. These landscapes are characterized by savanna parklands on coarse-textured upland portions of catenas that grade (1–3% slopes) into closed-canopy woodlands on fine-textured (lowland) portions of catenas. TWI values for woody and herbaceous communities were comparable within uplands, suggesting factors unrelated to surface/subsurface hydrology determine patterns of woody cover in these catena locations. TWI values for upland savanna parklands were significantly lower than those of closed-canopy woodlands occupying catena footslopes. Furthermore, uplands adjoining historically static woodland boundaries had lower TWI values than those where woodland boundaries had moved upslope 2.1 m yr⁻¹ from 1976 to 1995. Results suggest runoff–runon relationships influence patterns of woody plant cover and change at the catena scale and may override constraints imposed by soil texture. As a result, changes in woody cover potentially accompanying changes in disturbance regimes, climate or atmospheric chemistry are likely to be constrained by topoedaphic settings. Models of vegetation dynamics may therefore need to explicitly account for rainfall–topography–soil texture relationships and associated scale-dependent mechanisms to accurately predict rates and patterns of change in woody and herbaceous plant abundance.     

Spatial components of biotic diversity in landscapes of Georgia,USA

Traditional measures of species diversity and spatially-explicit measures of landscape diversity (derived from Romme 1982) are used to compare biotic diversity in six landscapes across Georgia, USA; two each from the Appalachian Highlands, Piedmont, and Coastal Plain. Species richness and evenness of plots generally increased from the Coastal Plain to the Appalachian Highlands. Community richness, landscape contrast, and landscape heterogeneity increased from the Appalachian Highlands to the Coastal Plain, opposite the gradient of topographic complexity. Coastal Plain landscapes possessed greater contrast and heterogeneity than landscapes in the other two physiographic provinces. This high level of landscape diversity is interpreted as a response to two factors: the increased role of human activity in shaping landscape structure, and the increased range of soil moisture regimes encountered in the sand-rich substrates of the Coastal Plain (from permanently flooded hydric communities to well drained xeric uplands only a few meters higher in elevation).     

Predicting landslide vegetation in patches on landscape gradients in Puerto Rico

We explored the predictive value of common landscape characteristics for landslide vegetative stages in the Luquillo Experimental Forest of Puerto Rico using four different analyses. Maximum likelihood logistic regression showed that aspect, age, and substrate type could be used to predict vegetative structural stage. In addition it showed that the structural complexity of the vegetation was greater in landslides (1) facing the southeast (away from the dominant wind direction of recent hurricanes), (2) that were older, and (3) that had volcaniclastic rather than dioritic substrate. Multiple regression indicated that both elevation and age could be used to predict the current vegetation, and that vegetation complexity was greater both at lower elevation and in older landslides. Pearson product-moment correlation coefficients showed that (1) the presence of volcaniclastic substrate in landslides was negatively correlated with aspect, age, and elevation, (2) that road association and age were positively correlated, and (3) that slope was negatively correlated with area. Finally, principal components analysis showed that landslides were differentiated on axes defined primarily by age, aspect class, and elevation in the positive direction, and by volcaniclastic substrate in the negative direction. Because several statistical techniques indicated that age, aspect, elevation, and substrate were important in determining vegetation complexity on landslides, we conclude that landslide succession is influenced by variation in these landscape traits. In particular, we would expect to find more successional development on landslides which are older, face away from hurricane winds, are at lower elevation, and are on volcaniclastic substrate. Finally, our results lead into a hierarchical conceptual model of succession on landscapes where the biota respond first to either gradients or disturbance depending on their relative severity, and then to more local biotic mechanisms such as dispersal, predation and competition.     

Landscape structure influences continental distribution of hantavirus in deer mice

We hypothesized that landscape structure affects movement of individuals through the landscape, which affects the rate and pattern of disease transmission. Based on this hypothesis, we predicted a relationship between landscape structure and disease incidence in spatially structured populations. We tested this prediction for hantavirus incidence in deer mice (Penomysens moniculatus), using a novel index of habitat fragmentation for transect data. A series of four stepwise logistic regression analyses were conducted on serological and ecological data from 2837 mice from 101 sites across Canada. The significant variables, ranked in decreasing order of size of their effect on virus incidence were: human buildings, landscape composition (amount of deer mouse habitat in the 1-km radius landscape surrounding each site), landscape configuration (fragmentation of deer mouse habitat in the 1-km radius landscape surrounding each site), mean annual temperature, and seasonal variation. Our results suggest that epidemiological models should consider not only the demographic structure of the host population, but its spatial structure as well, as inferred from landscape structure. Landscape structure can have a greater effect on the pattern of distribution of a virus in its host population than other ecological variables such as climate and seasonal change. The usefulness of landscape data in epidemiological models depends on the use of the appropriate spatial scale, which can be determined empirically. Epidemiological models with a spatially structured host population can benefit from the explicit consideration of landscape structure.     

The influence of topography and fire in controlling landscape composition and structure in Sierra de Gredos (Central Spain)

Mediterranean landscapes are dynamic systems that undergo temporal changes in composition and structure in response to disturbances, such as fire. Neither landscape patterns nor driving factors that affect them are evenly distributed in space. Accordingly, disturbances and biophysical factors interact in space through time. The aim of this paper is to assess the relative influence of topography and fire on the landscape patterns of a large forested area located in Sierra de Gredos (Central Spain) through time. A series of Landsat MSS images from 1975 to 1990, and a digital elevation model (DEM) were used to map fires, assess topographical complexity and evaluate changes in landscape composition and structure. Functional regions across the entire landscape were identified using different classification criteria (i.e., percentage burned area and topographic properties) to model topographic and fire impacts at regional scales. A canonical variance partition method, with a time series split-plot design, quantified the relative influence and co-variation of topography and fire on land cover patterns through time. Main results indicated that analyzing portions of the landscape under similar environmental conditions and fire histories, the effects of different fire regimes on the spatio-temporal dynamics of main land covers can be highlighted. However, the impact of fire on landscape patterns was high variable among regions due to the different regeneration abilities of main land covers, the topographic constraints and the fire histories of each region. Hence, broad patterns of fire related variance and co-variation with topography emerged across the entire area due to the different conditions of each landscape portion in which this large Mediterranean landscape was divided. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

The Relationship between Environmental Amenities and Changing Human Settlement Patterns between 1980 and 2000 in the Midwestern USA

Natural resource amenities may be an attractor as people decide where they will live and invest in property. In the American Midwest these amenities range from lakes to forests to pastoral landscapes, depending on the ecological province. We used simple linear regression models to test the hypotheses that physiographic, land cover (composition and spatial pattern), forest characteristics, land use on undeveloped land, public ownership, soil productivity and proximity to urban centers predict changes in population, housing, and seasonal housing densities over a 10-year interval (1980–1990). We then generated multiple-regression models to predict population, total and seasonal housing density change in the most recent decade (1990–2000) based on ownership and ecological conditions in 1990 and tested them by comparing the predictions to actual change measured by the US Census Bureau. Our results indicate that the independent variables explained between 25 and 40% of the variability in population density change, 42–67% of the variability of total housing density change, and 13–32% of the variability in seasonal housing density change in the 1980s, depending on the province. The strength of the relationships between independent and dependent variables varied by province, and in some cases the sign varied as well. Topographic relief was significantly related to population growth in all provinces, and land cover composition and the presence of water was significantly related to total housing growth in all provinces. There was a surprisingly limited association of any of the independent variables to seasonal housing growth in the northern province, which is commonly perceived to attract seasonal use because of ecological amenities. Proximity to urban centers is related to population and housing density change, but not seasonal housing density change. Our tests indicated that models for population density change showed some utility, but the models for total and seasonal housing density generally performed poorly. Ecologic variables were consistently poor at predicting seasonal housing density change. Our results show that environmental characteristics appear to have some influence on the spatial distribution of population and housing change in the Midwest, although other factors that were not modeled are clearly dominant.     

The challenges of forest modeling given climate change

Forest landscape modeling encompasses many core principles of landscape ecology: spatial resolution and extent, spatially explicit local and regional context, disturbance dynamics, integration of human activity, and explicit links to management and policy. Models of forest change inform land managers about strategies to adapt to the effects of an altered or changing environment across large, forested landscapes. Despite past successes, major challenges remain for landscape ecologists representing the dynamics of complex systems with a computer model, particularly given climate change. Here, I review major modeling challenges unique to climate change and suggest paths forward as climate change increasingly becomes a focus of landscape modeling efforts.