Intermediate landscape disturbance maximizes metapopulation density

The viability of metapopulations in fragmented landscapes has become a central theme in conservation biology. Landscape fragmentation is increasingly recognized as a dynamical process: in many situations, the quality of local habitats must be expected to undergo continual changes. Here we assess the implications of such recurrent local disturbances for the equilibrium density of metapopulations. Using a spatially explicit lattice model in which the considered metapopulation as well as the underlying landscape pattern change dynamically, we show that equilibrium metapopulation density is maximized at intermediate frequencies of local landscape disturbance. On both sides around this maximum, the metapopulation may go extinct. We show how the position and shape of the intermediate viability maximum is responding to changes in the landscape’s overall habitat quality and the population’s propensity for local extinction. We interpret our findings in terms of a dual effect of intensified landscape disturbances, which on the one hand exterminate local populations and on the other hand enhance a metapopulation’s capacity for spreading between habitat clusters.     

Historical influences dominate the composition of regenerating plant communities in abandoned citrus groves in north-central Florida

The question of what determines plant community composition is fundamental to the study of plant community ecology. We examined the relative roles of historical land use, landscape context, and the biophysical environment as determinants of plant community composition in regenerating citrus groves in north-central Florida. Results were interpreted in light of plant functional traits. Herbaceous and woody plants responded differently to broad-scale variables; herbs correlated most strongly with surrounding land cover at a scale of 8 km, while the only significant determinant of woody species distributions was local land use history. There were significant correlations between herbaceous species and spatial context, habitat isolation, environmental variables, and historical variables. Partial Mantel tests indicated that each variable provided a unique contribution in explaining some of the variation in the herbaceous dataset. The correlation between woody plants and local historical variables remained significant even with other effects corrected for. In the herbaceous community, species composition was linked to functional traits much as expected from classical theory. While spatial influences in our study system are important for both woody and herbaceous plants, the primary determinant of plant community composition in regenerating citrus groves is historical land use. Our results suggest that the fine-scale mechanisms of local competition, tolerance and facilitation invoked by many classical studies may ultimately be less important than land use history in understanding current plant community composition in regenerating agricultural areas.     

Relating tradable credits for biodiversity to sustainability criteria in a dynamic landscape

Tradable biodiversity credit systems provide flexible means to resolve conflicts between development and conservation land-use options for habitats occupied by threatened or endangered species. We describe an approach to incorporate the influence of habitat fragmentation into the conservation value of tradable credits. Habitat fragmentation decreases gene flow, increases rates of genetic drift and inbreeding, and increases probabilities of patch extinction. Importantly, tradable credit systems will change the level of fragmentation over time for small and/or declining populations. We apply landscape equivalency analysis (LEA), a generalizable, landscape-scale accounting system that assigns conservation value to habitat patches based on patch contributions to abundance and genetic variance at landscape scales. By evaluating habitat trades using two models that vary the relationship between dispersal behaviors and landscape patterns, we show that LEA provides a novel method for limiting access to habitat at the landscape-scale, recognizing that the appropriate amount of migration needed to supplement patch recruitment and to offset drift and inbreeding will vary as landscape pattern changes over time. We also found that decisions based on probabilities of persistence alone would ignore changes in migration, genetic drift, and patch extinction that result from habitat trades. The general principle of LEA is that habitat patches traded should make at least equivalent contributions to rates of recruitment and migration estimated at a landscape scale. Traditional approaches for assessing the “take” and “jeopardy” standards under the Endangered Species Act based on changes in abundance and probability of persistence may be inadequate to prevent trades that increase fragmentation.     

The ecology and culture of landscape sustainability: emerging knowledge and innovation in landscape research and practice

Landscape researchers and practitioners, using the lens of sustainability science, are breaking new ground about how people’s behaviors and actions influence the structure, function, and change of designed landscapes in an urbanizing world. The phrase—the scientific basis of the design for landscape sustainability—is used to describe how sustainability science can contribute to translational landscape research and practice about the systemic relationships among landscape sustainability, people’s contact with nature, and complex place-based problems. In the first section of this article, important definitions about the scientific basis of the design for landscape sustainability are reviewed including the six Es of landscape sustainability—environment, economic, equity, aesthetics, experience, and ethics. A conceptual framework about the six Es of landscape sustainability for designed landscapes is introduced. The interrelatedness, opportunities, contradictions, and limitations of the conceptual framework are discussed in relation to human health/security, ecosystem services, biodiversity, and resource management. The conceptual framework about the six Es of landscape sustainability for designed landscapes follows the tradition in which landscape researchers and practitioners synthesize emerging trends into conceptual frameworks for advancing basic and applied activities.     

Multi-scale predictive habitat suitability modeling based on hierarchically delineated patches: an example for yellow-billed cuckoos nesting in riparian forests,California, USA

The discipline of landscape ecology recognizes the importance of measuring habitat suitability variables at spatial scales relevant to specific organisms. This paper uses a novel multi-scale hierarchical patch delineation method, PatchMorph, to measure landscape patch characteristics at two distinct spatial scales and statistically relate them to the presence of state-listed endangered yellow-billed cuckoos (Coccyzus americanus occidentalis) nesting in forest patches along the Sacramento River, California, USA. The landscape patch characteristics calculated were: patch thickness, area of cottonwood forest, area of riparian scrub, area of other mixed riparian forest, and total patch area. A third, regional spatial variable, delineating the north and south portions of study area was also analyzed for the effect of regional processes. Using field surveys, the landscape characteristics were related to patch occupancy by yellow-billed cuckoos. The area of cottonwood forest measured at the finest spatial scale of patches was found to be the most important factor determining yellow-billed cuckoo presence in the forest patches, while no patch characteristics at the larger scale of habitat patches were important. The regional spatial variable was important in two of the three analysis techniques. Model validation using an independent data set of surveys (conducted 1987–1990) found 76–82% model accuracy for all the statistical techniques used. Our results show that the spatial scale at which habitat characteristics are measured influences the suitability of forest patches. This multi-scale patch and model selection approach to habitat suitability analysis can readily be generalized for use with other organisms and systems.     

Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model

Land use change is the result of interactions between processes operating at different scales. Simulation models at regional to global scales are often incapable of including locally determined processes of land use change. This paper introduces a modeling approach that integrates demand-driven changes in land area with locally determined conversion processes. The model is illustrated with an application for European land use. Interactions between changing demands for agricultural land and vegetation processes leading to the re-growth of (semi-) natural vegetation on abandoned farmland are explicitly addressed. Succession of natural vegetation is simulated based on the spatial variation in biophysical and management related conditions, while the dynamics of the agricultural area are determined by a global multi-sector model. The results allow an exploration of the future dynamics of European land use and landscapes. The model approach is similarly suitable for other regions and processes where large scale processes interact with local dynamics.     

Confronting collinearity: comparing methods for disentangling the effects of habitat loss and fragmentation

Estimating the relative importance of habitat loss and fragmentation is necessary to estimate the potential benefits of specific management actions and to ensure that limited conservation resources are used efficiently. However, estimating relative effects is complicated because the two processes are highly correlated. Previous studies have used a wide variety of statistical methods to separate their effects and we speculated that the published results may have been influenced by the methods used. We used simulations to determine whether, under identical conditions, the following 7 methods generate different estimates of relative importance for realistically correlated landscape predictors: residual regression, model or variable selection, averaged coefficients from all supported models, summed Akaike weights, classical variance partitioning, hierarchical variance partitioning, and a multiple regression model with no adjustments for collinearity. We found that different methods generated different rankings of the predictors and that some metrics were strongly biased. Residual regression and variance partitioning were highly biased by correlations among predictors and the bias depended on the direction of a predictor’s effect (positive vs. negative). Our results suggest that many efforts to deal with the correlation between amount and fragmentation may have done more harm than good. If confounding effects are controlled and adequate thought is given to the ecological mechanisms behind modeled predictors, then standardized partial regression coefficients are unbiased estimates of the relative importance of amount and fragmentation, even when predictors are highly correlated.