Comparing fire spread algorithms using equivalence testing and neutral landscape models

We demonstrate a method to evaluate the degree to which a meta-model approximates spatial disturbance processes represented by a more detailed model across a range of landscape conditions, using neutral landscapes and equivalence testing. We illustrate this approach by comparing burn patterns produced by a relatively simple fire spread algorithm with those generated by a more detailed fire behavior model from which the simpler algorithm was derived. Equivalence testing allows objective comparisons of the output of simple and complex models, to determine if the results are significantly similar. Neutral landscape models represent a range of landscape conditions that the model may encounter, allowing evaluation of the sensitivity and behavior of the model to different landscape compositions and configurations. We first tested the model for universal applicability, then narrowed the testing to assess the practical domain of applicability. As a whole, the calibrated simple model passed the test for significant equivalence using the 25% threshold. When applied to a range of landscape conditions different from the calibration scenarios, the model failed the tests for equivalence. Although our particular model failed the tests, the neutral landscape models were helpful in determining an appropriate domain of applicability and in assessing the model sensitivity to landscape changes. Equivalence testing provides an effective method for model comparison, and coupled with neutral landscapes, our approach provides an objective way to assess the domain of applicability of a spatial model.     

Relationship between change of hypoxic tissue and vascular proliferation after cerebral infarction in rats

AIM: To clarify the relationship between the change of hypoxic tissue and vascular proliferation after cerebral infarction. METHODS: Rats with middle cerebral artery occlusion were randomly divided into 1.5 h ischemia-reperfusion (1.5 h IR) group and permanent ischemia (PI) group. Double staining by immunofluorescence was used to identify the hypoxic tissue and microvessels by EF5 and vWF labelings, respectively. On 3 d, 7 d and 14 d post-operation, microvessel density (MVD) and hypoxic tissue in ischemic cortex were observed. RESULTS: Hypoxic tissues existed from 3 d to 14 d post-operation in 1.5 h IR group and disappeared after 3 d in PI group. The MVD within hypoxic tissue was smaller than that around it at all observed time points (all P<0.01). With the lapse of time, MVD in ischemic cortex gradually increased both in 1.5 IR and PI groups (all P<0.01) and was bigger in 1.5 h IR group than that in PI group on 7 d and 14 d (both P<0.01). CONCLUSION: Hypoxic tissues mainly locate in areas of sparse microvessel after cerebral infarction and their duration is partially dependent on the grade of microvessel proliferation in ischemic cortex.     

Upscaling as ecological information transfer: a simple framework with application to Arctic ecosystem carbon exchange

Transferring ecological information across scale often involves spatial aggregation, which alters information content and may bias estimates if the scaling process is nonlinear. Here, a potential solution, the preservation of the information content of fine-scale measurements, is highlighted using modeled net ecosystem exchange (NEE) of an Arctic tundra landscape as an example. The variance of aggregated normalized difference vegetation index (NDVI), measured from an airborne platform, decreased linearly with log(scale), resulting in a linear relationship between log(scale) and the scale-wise modeled NEE estimate. Preserving three units of information, the mean, variance and skewness of fine-scale NDVI observations, resulted in upscaled NEE estimates that deviated less than 4% from the fine-scale estimate. Preserving only the mean and variance resulted in nearly 23% NEE bias, and preserving only the mean resulted in larger error and a change in sign from CO₂ sink to source. Compressing NDVI maps by 70–75% using wavelet thresholding with the Haar and Coiflet basis functions resulted in 13% NEE bias across the study domain. Applying unique scale-dependent transfer functions between NDVI and leaf area index (LAI) decreased, but did not remove, bias in modeled flux in a smaller expanse using handheld NDVI observations. Quantifying the parameters of statistical distributions to preserve ecological information reduces bias when upscaling and makes possible spatial data assimilation to further reduce errors in estimates of ecological processes across scale.     

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.     

Changes in fox squirrel anti-predator behaviors across the urban–rural gradient

Predator stimuli created by humans in the urban environment may alter animals’ anti-predator behaviors. I hypothesized that habituation would cause anti-predator behaviors to decrease in urban settings in response to humans. Additionally, I hypothesized that populations habituated to humans would show reduced responses to other predator stimuli. I observed three populations of squirrels (urban, suburban and rural) responses to human approaches, red-tailed hawk vocalizations (Buteo jamaicensis) and coyote (Canis latrans) vocalizations. Mahalanobis distances of anti-predator behaviors in response to human approaches were consistent with the urban–rural gradient. Flight initiation distances (X ² = 26.33, df = 2, P < 0.001) and amount of time dedicated to anti-predator behavior (X ² = 10.94, df = 2, P = 0.004) in response to human approaches were also consistent with the urban–rural gradient. Supporting the habituation hypothesis, naive juvenile squirrels increased flight initiation distances (X ² = 35.89, df = 1, P < 0.001) and time dedicated to anti-predator behaviors (X ² = 9.46, df = 1, P = 0.002) relative to adult squirrels in the same urban environment. Time dedicated to anti-predator behaviors differed among all three sites in response to both coyote (X ² = 9.83, df = 2, P = 0.007) and hawk (X ² = 6.50, df = 2, P = 0.035) vocalizations. Responses to both vocalizations on rural sites (coyote = 45%, hawk = 55%) greater than twice that found on the urban sites (coyote = 11%, hawk = 20%). This is possibly the first case of a transfer of habituation demonstrated under field conditions.     

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.