Although considerable conservation resources have been committed to develop and use law enforcement monitoring and management tools such as SMART, measures of success are ill-defined and, to date, few reports detail results post-implementation. Here, we present 4 case studies from protected areas with Amur tigers (Panthera tigris altaica) in Russia, in which indicators of success were defined and evaluated at each. The ultimate goal was an increase in tiger numbers to 1 individual/100 km(2) at each site. We predicted that improvements in law enforcement effectiveness would be followed by increases in prey numbers and, subsequently, tiger numbers. We used short-term and long-term indicators of success, including: (i) patrol team effort and effectiveness; (ii) catch per unit effort indicators (to measure reductions in threats); and (iii) changes in target species numbers. In addition to implementing a monitoring system, we focused on improving law enforcement management using an adaptive management process. Over 4 years, we noted clear increases in patrol effort and a partial reduction in threats. Although we did not detect clear trends in ungulate numbers, tiger populations remained stable or increased, suggesting that poaching of tigers may be more limiting than prey depletion. Increased effectiveness is needed before a clear reduction in threats can be noted, and more time is needed before detecting responses in target populations. Nonetheless, delineation of concrete goals and indicators of success provide a means of evaluating progress and weaknesses. Such monitoring should be a central component of law enforcement strategies for protected areas.

Summary1. Typically in resource selection studies, the spatial extent of a home range is defined first and then the available resources within that perimeter are estimated. However, the home ranges (or habitats) of some animals are constrained by linear environmental features (e.g. rivers, shorelines). Traditional home range estimators often overestimate home range extent for such species, which can lead to spurious estimation of resource availability and selection. 2. We used a synoptic model of space use to explicitly account for resource selection of a species constrained by linear features in its environment to compare with traditional home range estimators. We used the endangered Blakiston's fish owl Bubo blakistoni in the Russian Far East as our example. 3. Mean annual home range size (AE standard error) was more than three times larger when using kernel methods (30Á3 AE 15Á1 km 2 ) than when using the synoptic model (9Á4 AE 2Á0 km 2 , n = 7). 4. Fish owls showed strong selection for areas within valleys, closer to waterways, closer to patches of permanently open water and with greater channel complexity than available sites. 5. Synthesis and applications. The synoptic model solves a long-standing problem in home range and resource selection studies because it provides an objective way to estimate the space use of a species whose habitat is constrained by linear features in its environment. Improvements in the accuracy of such estimations can lead to identification of important resources across landscapes, the development of more rigorous site-specific or landscape-scale management plans, and to scientifically defensible conservation or threat mitigation measures.

Conservation efforts for Blakiston's fish owl Bubo blakistoni in Russia are limited, partly because habitat use by these rare owls is poorly known. We therefore studied nesting and foraging habitat characteristics of Blakiston's fish owls in Primorye, Russia. We sampled habitat at 14 nest sites, 12 nest stand sites and 13 random sites; we also sampled rivers within 14 fish owl home ranges across our 20,213 km 2 study area. We found that large old trees and riparian oldgrowth forest were the primary characteristics of nest and foraging sites, respectively. Large trees were probably used as nest sites because they have cavities large enough to accommodate these birds. Big trees are also important because they are primary sources of large woody debris in rivers, which enhances suitable habitat for salmon, the owl's primary prey. Based on habitat characteristics, nest sites were correctly distinguished from random sites 74% (Kappa 5 0.48) of the time, nest stands from random sites 56% (Kappa 5 0.12) of the time, and used sites from available foraging sites 68% (Kappa 5 0.36) of the time. The management and conservation of old-growth forests is essential for sustaining this species because they are central to the owls' nesting and foraging behaviour. Moreover, conservation of these forests sustains habitat for many other species.

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