ISSUES IN ECOLOGY NUMBER SIXTEEN FALL 2012 System(GIS)and remote sensing tools needs.Thus,recent developments in connec affordable,and ne a s isms can be logistically complicated. with a functional approach that highlights scan rac only rela t requirements controlled experiments addressing movements Modeling Approaches for dispersal at relevant Quantifying may more accurately and efficiently reflect across large emented in a GlS env s of tracking individual animals and inte grate only those mo vements that produce approach has specific data meaning popu tion impacts ispe require input from b s to help C. comine of this anpr isthat current geneti ns may not reflect the impact of current ally for species wit comes. Least-cost analysis identifics the least human perse by past epidemics by oal adaptation.which can drive etic where ost"may reflect the actual energ 0ecradland expended to move over the area,mortality risk cting or impact on A common product of connectiv ity analysis e of habitat.Habitats that the anima hil n D nThe le tion of cells that has the lowest cumulative unique strengths as the path as a pa w popu sistance (an indica of how well a land patch)to the other endpoint. cape can be traversed by a giv species),a can 1 of information about s hahitat nrefer can be lysts lon methods to rig sly n in red in the r anel o -specific resistar om at e which is a swath of cells expected to provide a ow,genetic at use, ow-co r movemen ance.hased on the extent to which land patchesa esults in higher costs.This latte cover,in hay be y ar mpacts measures may be useful for some gen cale of perc ption andh may not be able to cor sider total 1.C ing species-specific movements and so referred to as 6 esa The Ecological Society of America.esahg@esa ora ISSUES IN ECOLOGY NUMBER SIXTEEN FALL 2012 6 esa © The Ecological Society of America • esahq@esa.org System (GIS) and remote sensing tools become more widely available, affordable, and scalable. However, measuring functional con￾nectivity using the movements of individual organisms can be logistically complicated. Even the largest studies using the most appro￾priate technologies can track only relatively few individuals over modest time periods, and controlled experiments addressing movements and dispersal at relevant scales are extremely difficult to implement. One way to address this difficulty is to measure gene flow, which may more accurately and efficiently reflect functional connectivity across large landscapes. Genetic studies avoid the logistic and financial costs of tracking individual animals and inte￾grate only those movements that produce meaningful population impacts – dispersals that result in breeding or emigration. A short￾coming of this approach is that current genetic patterns may not reflect the impact of current landscape features, especially for species with large population sizes or long generation times, or species affected by unobserved events, such as genetic bottlenecks caused by past epidemics or human persecution. In addition, genetic connectivity may be masked in some instances by local adaptation, which can drive genetic distinctiveness even in a well-connected land￾scape, by selecting for particular characteristics of the local environment. A common product of connectivity analysis is a map of predicted core areas, linkage zones, or barriers. Such maps often become the basis for management actions. Several tools can be used to map these features, and each has unique strengths and weaknesses. All of the approaches described in the next section depend on accurately defining landscape resistance (an indication of how well a land￾scape can be traversed by a given species), a challenging task when only a limited amount of information about species habitat prefer￾ences is available. Furthermore, connectivity models can be difficult to validate. Several research teams are working to develop methods to rigorously estimate species-specific resistance from data on gene flow, genetic distances, habitat use, and movement paths. Simple estimates of resis￾tance, based on the extent to which land￾scapes are impacted by roads, loss of natural land cover, increased edge effects, spread of invasive species, and other direct human impacts measures may be useful for some gen￾eralist species, but are insufficient for address￾ing species-specific movements and habitat needs. Thus, recent developments in connec￾tivity modeling combine a structural land￾scape approach, identifying both the potential for and obstacles to long-term habitat shifts, with a functional approach that highlights the specific connectivity needs of species with restricted habitat requirements. Modeling Approaches for Identifying and Quantifying Landscape Connectivity We describe five widely-used analytical approaches, all implemented in a GIS envi￾ronment, to assist planners in mapping and prioritizing landscape connections. Each approach has specific data requirements that often require input from biologists to help define model parameters. In addition, each approach is designed to meet different objec￾tives and will, therefore, produce different out￾comes. Least-cost analysis identifies the least costly route that an animal can take from one area to another. The method assumes that the animal incurs a cost as it moves over an area, where “cost” may reflect the actual energy expended to move over the area, mortality risk, or impact on future reproductive potential. In practice, cost is usually estimated simply as the inverse of habitat suitability. Habitats that the animal favors are assigned low cost while unsuitable habitats are assigned high cost. The least-cost path is the contiguous collec￾tion of cells that has the lowest cumulative cost as the path crosses from one endpoint (such as a park, natural area, or known popu￾lation; sometimes referred to as a node or patch) to the other endpoint. Computers using GIS software can easily identify this path. Because the least-cost path is only one cell wide (for example, the center panel in Figure 2), it is often not a realistic area to pro￾pose for conservation. Therefore, analysts usu￾ally identify the least-cost corridor (shown in red in the panel on the right in Figure 2), which is a swath of cells expected to provide a low-cost route for movement. Increased distance between two nodes or patches also results in higher costs. This latter assumption is important, in that some species may be able to identify and take advantage of shorter linkages, while others operate at a finer scale of perception and therefore may not be able to consider total corridor length. Correctly assigning these cost values (also referred to as