The Importance of Distance to Resources in the Spatial Modelling of Bat Foraging Habitat

PLoS ONE, published 25 Apr 2011

Ana Rainho, Jorge M. Palmeirim

“Many bats are threatened by habitat loss, but opportunities to manage their habitats are now increasing. Success of management depends greatly on the capacity to determine where and how interventions should take place, so models predicting how animals use landscapes are important to plan them. Bats are quite distinctive in the way they use space for foraging because (i) most are colonial central-place foragers and (ii) exploit scattered and distant resources, although this increases flying costs. To evaluate how important distances to resources are in modelling foraging bat habitat suitability, we radio-tracked two cave-dwelling species of conservation concern (Rhinolophus mehelyi and Miniopterus schreibersii) in a Mediterranean landscape. Habitat and distance variables were evaluated using logistic regression modelling. Distance variables greatly increased the performance of models, and distance to roost and to drinking water could alone explain 86 and 73% of the use of space by M. schreibersii and R. mehelyi, respectively. Land-cover and soil productivity also provided a significant contribution to the final models. Habitat suitability maps generated by models with and without distance variables differed substantially, confirming the shortcomings of maps generated without distance variables. Indeed, areas shown as highly suitable in maps generated without distance variables proved poorly suitable when distance variables were also considered. We concluded that distances to resources are determinant in the way bats forage across the landscape, and that using distance variables substantially improves the accuracy of suitability maps generated with spatially explicit models. Consequently, modelling with these variables is important to guide habitat management in bats and similarly mobile animals, particularly if they are central-place foragers or depend on spatially scarce resources.”

Dubai’s GIS Enhances Operations to Support Continued Growth

Esri License Agreement Enables Enterprise-wide GIS in World’s Fastest-Growing City

Dubai Municipality signed an enterprise license agreement (ELA) with Esri’s United Arab Emirates distributor GISTEC, making ArcGIS software available throughout the organization to help meet the planning and operational demands of Dubai’s rapid growth.

“This is a landmark decision because it not only helps the municipality become more effective but also benefits all the citizens in the emirate of Dubai,” says AbdulHakim Abdul Kareem Malik, director of the Dubai Municipality GIS department.

With broader access to the latest ArcGIS software, the municipality intends to leverage GIS technology and share geospatial data between departments.  The enterprise-wide GIS is expected to improve disaster response capabilities; citizen services; and the planning, design, construction, and management of Dubai’s expanding infrastructure and facilities.

“The ELA provides us with the products and flexibility we need as we plan to migrate our existing cadastre and certification management systems to the ArcGIS 10 platform, making it easier to access and use data,” says Malik. “The resulting streamlined business processes will help serve our citizens in their day-to-day requests and provide accurate spatial and nonspatial information.”

Further details on Dubai Municipality are available at To learn about Esri ELAs, visit For more information on GISTEC, visit

[Source: Esri press release]

Least Cost Distance Analysis for Spatial Interpolation

Computers & Geosciences, Volume 37 Issue 2, February 2011

Jonathan A. Greenberg, Carlos Rueda, Erin L. Hestir, Maria J. Santos, and Susan L. Ustin

“Spatial interpolation allows creation of continuous raster surfaces from a subsample of point-based measurements. Most interpolation approaches use Euclidean distance measurements between data points to generate predictions of values at unknown locations. However, there are many spatially distributed data sets that are not properly represented by Euclidean distances and require distance measures which represent their complex geographic connectivity. The problem of defining non-Euclidean distances between data points has been solved using the network-based solutions, but such techniques have historically relied on a network of connected line segments to determine point-to-point distances. While these vector-based solutions are computationally efficient, they cannot model more complex 2- and 3-dimensional systems of connectivity. Here, we use least-cost-path analyses to define distances between sampled points; a solution that allows for arbitrarily complex systems of connectivity to be interpolated. We used least-cost path distances in conjunction with the inverse distance weighting interpolation for a proof-of-concept interpolation of water temperature data in a complex deltaic river system. We compare our technique to Euclidean distance interpolation, and demonstrate that our technique, which follows connectivity rules, yields are more realistic interpolation of water temperature.”