Cost Distance Modelling of Landscape Connectivity and Gap-crossing Ability using Radio-tracking Data

Journal of Applied Ecology, Volume 47, Issue 3, Date: June 2010, Pages: 603-610

Yvan Richard and Doug P. Armstrong

“Landscape connectivity, the ability of species to move between different elements of a landscape, has been evaluated mainly by expert opinion, proxy data or homing experiments, all of which have major limitations. Cost distance modelling can overcome these limitations, but the resistance values of different landscape elements are difficult to estimate.

“Here, we present a novel method combining step selection functions with cost distance modelling to assess functional landscape connectivity. Instead of relying on movement metrics, the method uses a case-control design to assess whether the chosen steps differ from a random sample of alternatives of similar lengths. Alternative models of landscape connectivity and dispersal behaviour are represented as maps of resistance values, and compared using an information-theoretic approach to select those hypotheses that maximize the discrepancy between chosen steps and random alternatives.

“We applied this method to daily locations recorded along the dispersal paths of 38 juvenile North Island robins Petroica longipes in a fragmented pastoral landscape in New Zealand. We compared models with different resistance values for four recognized vegetation types in the landscape and assessed gap-crossing behaviour by changing the resistance value of pasture as a function of distance to the closest woody vegetation.

“Model comparison showed that juvenile robins move in decreasing order of preference through native forest, plantations and shrubland, and showed a marked reluctance for flying over pasture. Under the best model, the largest gap crossed was 110 m.

“Synthesis and applications. In combination with data on the total cost distances travelled by dispersers, cost distance models of landscape connectivity can be used to predict distributions of dispersal distances in any landscape with similar vegetation types. They can therefore predict responses of species to landscape management or predict spatial dynamics of populations following reintroduction. Our method is potentially applicable to any dispersal data, even with a relatively small number of locations recorded in complex landscapes, meaning models can be fitted to data that cannot be analysed using previous method. Tools are freely available for download to allow researchers and wildlife managers to apply our methods to their own data.”

Assessing the Impact of Extreme Climatic Events on Aspen Defoliation using MODIS Imagery

Geocarto International, Volume 25, Issue 2 April 2010, pages 133 – 147

Nate Currit and Samuel B. St Clair

“Recent studies document the decline of quaking aspen across large geographic areas of North America. Extreme climatic events are possible contributors to the decline, and drought is often cited as an important driver of aspen phenology. Little is known, however, about the effects of spring freeze events on aspen phenology, even though such events are projected to occur more frequently in future. This study uses moderate resolution imaging spectrometer (MODIS) imagery to assess the spatial pattern and magnitude of damage to aspen forests during spring freeze and summer drought events that occurred in Utah in 2007. The analysis finds above normal Normalized Difference Vegetation Index (NDVI) in early spring, and below normal NDVI following the freeze event and during the summer drought. Aspen damage is concentrated in certain terrain classes, depending on the type of extreme climatic event. These findings suggest there are predictable patterns of aspen defoliation that identify aspen stands vulnerable to extreme climatic events.”

Methodology for Spatial and Temporal Analysis of Drought using Large-scale Gridded Data

Geophysical Research Abstracts, Vol. 12, EGU2010-12703-1, 2010

Gerald A Corzo P, Marjolein H.J. van Huijgevoort, and Henny A.J. van Lanen

“In recent years, there is an increased understanding of the importance of drought, in particular due to global change. For a good understanding of historic droughts, and to evaluate the impact of future global change scenarios, more advanced techniques to account for spatio-temporal variability are required. So far, methodologies to characterize spatio-temporal patterns of large-scale drought (e.g. global scale) are still limited. This explorative work presents methodological processes developed to analyze a gridded dataset with forcing data that has been compiled through the EU-FP6 WATCH project (0.5o, daily, 1958-2001). Two new methodologies are proposed: the Standardized Clustered Precipitation Index (SCPI), which quantifies monthly precipitation changes, and the Cluster Precipitation Distributions (CPDs) which consider the spatial reduction of continuous period without daily rain. Both methods are used to characterize meteorological drought. The SCPI methodology is an extension of the Standardized Precipitation Index (SPI) that incorporates a multivariate clustering analysis to determine the spatial changes of the index and the rate of change. The SPIs are calculated based on a monthly moving average of a specified length (e.g. 30 days), and their variability is calculated in k years to identify a change in the SPI levels. To determine this k-years change, a monthly spatial pattern of severity is calculated in the time frame defined in the calculation of the SPI. A second method is presented (i.e. CPD) to prepare for analysis of hydrological drought in a next phase of this research. CPD identifies spatial regions where probabilities of longer periods of non-precipitation events are present. These probability distributions, however, do not consider geographical positioning which may affect drought analysis of a particular region. Therefore, the probabilities of non-precipitation events are grouped using a clustering technique that allows for geo-referenced information. The two methodologies provide important information for principles that can be used to develop methods to evaluate meteorological and subsequently hydrological drought from different types of large-scale grid-based models (e.g. RCMs, LSHMs, GHMs).”