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Context Connectivity assessments typically rely on resistance surfaces derived from habitat models, assuming that higher-quality habitat facilitates movement. This assumption remains largely untested though, and it is unlikely that the same environmental factors determine both animal movements and habitat selection, potentially biasing connectivity assessments. Objectives We evaluated how much connectivity assessments differ when based on resistance surfaces from habitat versus movement models. In addition, we tested how sensitive connectivity assessments are with respect to the parameterization of the movement models.Methods We parameterized maximum entropy models to predict habitat suitability, and step selection functions to derive movement models for brown bear (Ursus arctos) in the northeastern Carpathians. We compared spatial patterns and distributions of resistance values derived from those models, and locations and characteristics of potential movement corridors. Results Brown bears preferred areas with high forest cover, close to forest edges, high topographic complexity, and with low human pressure in both habitat and movement models. However, resistance surfaces derived from the habitat models based on predictors measured at broad and medium scales tended to underestimate connectivity, as they predicted substantially higher resistance values for most of the study area, including corridors. 123Landscape Ecol (2016) 31:1863-1882 DOI 10.1007 Conclusions Our findings highlighted that connectivity assessments should be based on movement information if available, rather than generic habitat models. However, the parameterization of movement models is important, because the type of movement events considered, and the sampling method of environmental covariates can greatly affect connectivity assessments, and hence the predicted corridors.

Assessing landscape connectivity is important to understand the ecology of landscapes and to evaluate alternative conservation strategies. The question is though, how to quantify connectivity appropriately, especially when the information available about the suitability of the matrix surrounding habitat is limited. Our goal here was to investigate the effects of matrix representation on assessments of the connectivity among habitat patches and of the relative importance of individual patches for the connectivity within a habitat network. We evaluated a set of 50 9 50 km 2 test areas in the Carpathian Mountains and considered three different matrix representations (binary, categorical and continuous) using two types of connections among habitat patches (shortest lines and least-cost paths). We compared connections, and the importance of patches, based on (1) isolation, (2) incidencefunctional, and (3) graph measures. Our results showed that matrix representation can greatly affect assessments of connections (i.e., connection length, effective distance, and spatial location), but not patch prioritization. Although patch importance was not much affected by matrix representation, it was influenced by the connectivity measure and its parameterization. We found the biggest differences in the case of the integral index of connectivity and equally weighted patches, but no consistent pattern in response to changing dispersal distance. Connectivity assessments in more fragmented landscapes were more sensitive to the selection of matrix representation. Although we recommend using continuous matrix representation whenever possible, our results indicated that simpler matrix representations can be also used as a proxy to delineate those patches that are important for overall connectivity, but not to identify connections among habitat patches.

Understanding the causes and consequences of forest-fragmentation changes is critical for preserving various ecosystem services and to maintain biodiversity levels. We used long-term (1860s-2010s) and large-scale data on historical forest cover in the Polish Carpathians to identify the trajectories of forest fragmentation. Past forest cover was reconstructed for the 1860s, 1930s, 1970s and 2010s using historical maps and the contemporary national database of topographic objects. We analyzed forest-cover changes in 127 randomly selected circular test areas. Forest fragmentation was quantified with GuidosToolbox software using measures based on a landscape hypsometric curve (LHC). Despite a general increase in forest cover, forest fragmentation showed divergent trajectories: a decrease between the 1860s and 1930s (in 57% of test areas), and an increase between the 1930s and 1970s and between the 1970s and 2010s (in 58% and 72% of test areas, respectively). Although deforestation typically involves the increasing fragmentation of forest habitats, we found that forest expansion may not necessarily lead to more homogenous forested landscape, due to complex land-ownership and land-use legacy patterns. This is both a challenge and an opportunity for policy makers to tune policies in such a way as to maintain the desired fragmentation of forest habitats.

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