Automated data analysis to rapidly derive and communicate ecological insights from satellite-tag data: A case study of reintroduced red kites

Analysis of satellite-telemetry data mostly occurs long after it has been collected, due to the time and effort needed to collate and interpret such material. Such delayed reporting does reduce the usefulness of such data for nature conservation when timely information about animal movements is required. To counter this problem we present a novel approach which combines automated analysis of satellite-telemetry data with rapid communication of insights derived from such data. A relatively simple algorithm (comprising speed of movement and turning angle calculated from fixes), allowed instantaneous detection of excursions away from settlement areas and automated calculation of home ranges on the remaining data Automating the detection of both excursions and home range calculations enabled us to disseminate ecological insights from satellite-tag data instantaneously through a dedicated web portal to inform conservationists and wider audiences. We recommend automated analysis, interpretation and communication of satellite tag and other ecological data to advance nature conservation research and practice

Linking like with like: optimising connectivity between environmentally-similar habitats

Habitat fragmentation is one of the greatest threats to biodiversity. To minimise the effect of fragmentation on biodiversity, connectivity between otherwise isolated habitats should be promoted. However, the identification of linkages favouring connectivity is not trivial. Firstly, they compete with other land uses, so they need to be cost-efficient. Secondly, linkages for one species might be barriers for others, so they should effectively account for distinct mobility requirements. Thirdly, detailed information on the auto-ecology of most of the species is lacking, so linkages need being defined based on surrogates. In order to address these challenges we develop a framework that (a) identifies environmentally-similar habitats; (b) identifies environmental barriers (i.e., regions with a very distinct environment from the areas to be linked), and; (c) determines cost-efficient linkages between environmentally-similar habitats, free from environmental barriers. The assumption is that species with similar ecological requirements occupy the same environments, so environmental similarity provides a rationale for the identification of the areas that need to be linked. A variant of the classical minimum Steiner tree problem in graphs is used to address c). We present a heuristic for this problem that is capable of handling large datasets. To illustrate the framework we identify linkages between environmentally-similar protected areas in the Iberian Peninsula. The Natura 2000 network is used as a positive ‘attractor’ of links while the human footprint is used as ‘repellent’ of links.Wecompare the outcomes of our approach with cost-efficient networks linking protected areas that disregard the effect of environmental barriers. As expected, the latter achieved a smaller area covered with linkages, but with barriers that can significantly reduce the permeability of the landscape for the dispersal of some species

Animal behavior, cost-based corridor models, and real corridors

Corridors are popular conservation tools because they are thought to allow animals to safely move between habitat fragments, thereby maintaining landscape connectivity. Nonetheless, few studies show that mammals actually use corridors as predicted. Further, the assumptions underlying corridor models are rarely validated with field data. We categorized corridor use as a behavior, to identify animal-defined corridors, using movement data from fishers (Martes pennanti) tracked near Albany, New York, USA. We then used least-cost path analysis and circuit theory to predict fisher corridors and validated the performance of all three corridor models with data from camera traps. Six of eight fishers tracked used corridors to connect the forest patches that constitute their home ranges, however the locations of these corridors were not well predicted by the two cost-based models, which together identified only 5 of the 23 used corridors. Further, camera trap data suggest the cost-based corridor models performed poorly, often detecting fewer fishers and mammals than nearby habitat cores, whereas camera traps within animal-defined corridors recorded more passes made by fishers, carnivores, and all other non-target mammal groups. Our results suggest that (1) fishers use corridors to connect disjunct habitat fragments, (2) animal movement data can be used to identify corridors at local scales, (3) camera traps are useful tools for testing corridor model predictions, and (4) that corridor models can be improved by incorporating animal behavior data. Given the conservation importance and monetary costs of corridors, improving and validating corridor model predictions is vital.publishe

Identifying Corridors among Large Protected Areas in the United States

Conservation scientists emphasize the importance of maintaining a connected network of protected areas to prevent ecosystems and populations from becoming isolated, reduce the risk of extinction, and ultimately sustain biodiversity. Keeping protected areas connected in a network is increasingly recognized as a conservation priority in the current era of rapid climate change. Models that identify suitable linkages between core areas have been used to prioritize potentially important corridors for maintaining functional connectivity. Here, we identify the most "natural" (i.e., least human-modified) corridors between large protected areas in the contiguous Unites States. We aggregated results from multiple connectivity models to develop a composite map of corridors reflecting agreement of models run under different assumptions about how human modification of land may influence connectivity. To identify which land units are most important for sustaining structural connectivity, we used the composite map of corridors to evaluate connectivity priorities in two ways: (1) among land units outside of our pool of large core protected areas and (2) among units administratively protected as Inventoried Roadless (IRAs) or Wilderness Study Areas (WSAs). Corridor values varied substantially among classes of "unprotected" non-core land units, and land units of high connectivity value and priority represent diverse ownerships and existing levels of protections. We provide a ranking of IRAs and WSAs that should be prioritized for additional protection to maintain minimal human modification. Our results provide a coarse-scale assessment of connectivity priorities for maintaining a connected network of protected areas