Selection of scale for Everglades landscape models

This article addresses the problem of determining the optimal “Model Grain” or spatial resolution (scale) for landscape modeling in the Everglades. Selecting an appropriate scale for landscape modeling is a critical task that is necessary before using spatial data for model development. How the landscape is viewed in a simulation model is dependent on the scale (cell size) in which it is created. Given that different processes usually have different rates of fluctuations (frequencies), the question of selection of an appropriate modeling scale is a difficult one and most relevant to developing spatial ecosystem models. The question of choosing the appropriate scale for modeling is addressed using the landscape indices (e.g., cover fraction, diversity index, fractal dimension, and transition probabilities) recently developed for quantifying overall characteristics of spatial patterns. A vegetation map of an Everglades impoundment area developed from SPOT satellite data was used in the analyses. The data from this original 20 × 20 m data set was spatially aggregated to a 40 × 40 m resolution and incremented by 40 meters on up to 1000 × 1000 m (i.e., 40, 80, 120, 160 … 1000) scale. The primary focus was on the loss of information and the variation of spatial indices as a function of broadening “Model Grain” or scale. Cover fraction and diversity indices with broadening scale indicate important features, such as tree islands and brush mixture communities in the landscape, nearly disappear at or beyond the 700 m scale. The fractal analyses indicate that the area perimeter relationship changes quite rapidly after about 100 m scale. These results and others reported in the paper should be useful for setting appropriate objectives and expectations for Everglades landscape models built to varying spatial scales.     

Movement rules for herbivores in spatially heterogeneous environments: responses to small scale pattern

Foraging herbivores respond to the spatial pattern of resources at a variety of scales. At small scales of space and time, existing models capture the essence of the feeding process and successfully predict intake rates. Models that operate over larger scales have not exhibited a similar success, in part because we have a limited understanding of the rules used by animals to make decisions in spatially complex environments, or of the consequences of departing from these rules. To evaluate the rules that large herbivores use when navigating between forages, we examined movements of bighorn sheep foraging on apparent prey (alfalfa plants) in hand-constructed patches of plants. Observations of movements and path lengths were compared to simulations that used a variety of different rules-of-thumb to determine a search path. Rules used in simulations ranged from a random walk with various detection distances, to more complicated rules that solved a variant of the travelling salesman problem. Simulations of a random walk yielded movement lengths that exceeded observations by a factor of 3 for long detection distances, and by 30-fold for short detection distances. Observed move distances were most closely approximated by simulations based on a nearest-neighbor ruleover 75 % of all moves by bighorn sheep were to the closest available plant. Movement rules based on random walks are clearly inappropriate for many herbivores that typically consume visually apparent plants, and we suggest the use of a nearest-neighbor rule for modelling foraging by large herbivores.     

The scale of landscape effect on seed dispersal depends on both response variables and landscape predictor

Landscape Ecology - Landscape structure can affect seed dispersal, but the spatial scale at which such effect is maximized (scale of effect, SoE) is unknown. We assessed patterns and predictors of...     

The effect of exposure on landscape scale soil surface temperatures and species distribution models

Species distribution models (SDMs) often use elevation as a surrogate for temperature or utilise elevation sensitive interpolations from weather stations. These methods may be unsuitable at the landscape scale, especially where there are sparse weather stations, dramatic variations in exposure or low elevational ranges. The goal of this study was to determine whether radiation, moisture or a novel estimate of exposure could improve temperature estimates and SDMs for vegetation on the Illawarra Escarpment, near Sydney, Australia. Forty temperature sensors were placed on the soil surface of an approximately 12,000 ha study site between November 2004 and August 2006. Linear regression was used to determine the relationship with environmental factors. Elevation was correlated more with moderate temperatures (winter maximums, summer minimums, spring and autumn averages) than extreme temperatures (summer maximums, winter minimums). The correlation (r ²) between temperature and environmental factors was improved by up to 0.38 by incorporating exposure, moisture and radiation in the regressions. Summer maximums and winter minimums were predominately determined by exposure to the NW and coastal influences respectively, while exposure to the NE and SW was important during other seasons. These directions correspond with the winds that are most influential in the study area. The improved temperature estimates were used in Generalised Additive Models for 37 plant species. The deviance explained by most models was increased relative to elevation, especially for moist rainforest species. It was concluded that improving the accuracy of seasonal temperature estimates could improve our ability to explain the patchy distribution of many species. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Statistical models of landscape pattern metrics,with applications to regional scale dynamic forest simulations

Forest managers in Canada need to model landscape pattern or spatial configurationoverlarge (100,000 km²) regions. This presents a scalingproblem, as landscape configuration is measured at a high spatial resolution,but a low spatial resolution is indicated for regional simulation. We present astatistical solution to this scaling problem by showing how a wide range oflandscape pattern metrics can be modelled from low resolution data. Our studyarea comprises about 75,000 km² of boreal mixedwoodforest in northeast Alberta, Canada. Within this area we gridded a sample of 84digital forest cover maps, each about 9500 ha in size, to aresolution of 1 ha and used FRAGSTATS to compute a suite oflandscape pattern metrics for each map. We then used multivariate dimensionreduction techniques and canonical correlation analysis to model therelationship between landscape pattern metrics and simpler stand table metricsthat are easily obtained from non-spatial forest inventories. These analyseswere performed on four habitat types common in boreal mixedwood forests: youngdeciduous, old deciduous, white spruce, and mixedwood types. Using only threelandscape variables obtained directly from stand attribute tables (totalhabitatarea, and the mean and standard deviation of habitat patch size), ourstatistical models explained more than 73% of the joint variation in fivelandscape pattern metrics (representing patch shape, forest interior habitat,and patch isolation). By PCA, these five indices captured much of the totalvariability in the rich set of landscape pattern metrics that FRAGSTATS cangenerate. The predictor variables and strengths of association were highlyconsistent across habitat classes. We illustrate the potential use of suchstatistical relationships by simulating the regional, cumulative effects ofwildfire and forest management on the spatial arrangement of forest patches,using non-spatial stand attribute tables.This revised version was published online in May 2005 with corrections to the Cover Date.     

Underestimating the effects of spatial heterogeneity due to individual movement and spatial scale: infectious disease as an example

Many ecological and epidemiological studies occur in systems with mobile individuals and heterogeneous landscapes. Using a simulation model, we show that the accuracy of inferring an underlying biological process from observational data depends on movement and spatial scale of the analysis. As an example, we focused on estimating the relationship between host density and pathogen transmission. Observational data can result in highly biased inference about the underlying process when individuals move among sampling areas. Even without sampling error, the effect of host density on disease transmission is underestimated by approximately 50 % when one in ten hosts move among sampling areas per lifetime. Aggregating data across larger regions causes minimal bias when host movement is low, and results in less biased inference when movement rates are high. However, increasing data aggregation reduces the observed spatial variation, which would lead to the misperception that a spatially targeted control effort may not be very effective. In addition, averaging over the local heterogeneity will result in underestimating the importance of spatial covariates. Minimizing the bias due to movement is not just about choosing the best spatial scale for analysis, but also about reducing the error associated with using the sampling location as a proxy for an individual’s spatial history. This error associated with the exposure covariate can be reduced by choosing sampling regions with less movement, including longitudinal information of individuals’ movements, or reducing the window of exposure by using repeated sampling or younger individuals.

     

Issues and challenges in landscape models for agriculture: from the representation of agroecosystems to the design of management strategies

Context

Agroecosystems produce food and many other services that are crucial for human well-being. Given the scales at which the processes underlying these services take place, agricultural landscapes appear as appropriate spatial units for their evaluation and management. The design of sustainable agricultural landscapes that value these services has thus become a pressing issue but faces major challenges stemming from the diversity of processes, their interactions and the number of scales at stake. Agricultural landscape modelling can provide a key contribution to this design but must still overcome several difficulties to offer reliable tools for decision makers.

Objectives

Our study aimed at shedding light on the main scientific and technical difficulties that make the building of landscape models that may efficiently inform decision-makers a complex task, as well as translating them in terms of challenges that can be further investigated and discussed.

Methods

We examine current issues and challenges and indicate future research needs to overcome the scientific and technical obstacles in the development of useful agricultural landscape models.

Results

We highlight research perspectives to better couple landscape patterns and process models and account for feedbacks, integrate the decisions of multiple stakeholders, consider the spatial and temporal heterogeneity of data and processes, explore alternative landscape organisations and assess multiobjective performance.

Conclusion

Coping with the issues and challenges discussed in this paper should improve our understanding of agroecosystems and give rise to new hypotheses, thereby informing future research.