a b s t r a c tMapping wetlands across both natural and human-altered landscapes is important for the management of these ecosystems. Though they are considered important landscape elements providing both ecological and socioeconomic benefits, accurate wetland inventories do not exist in many areas. In this study, a multi-scale geographic object-based image analysis (GEOBIA) approach was employed to segment three high spatial resolution images acquired over landscapes of varying heterogeneity due to humandisturbance to determine the robustness of this method to changing scene variability. Multispectral layers, a digital elevation layer, normalized-difference vegetation index (NDVI) layer, and a first-order texture layer were used to segment images across three segmentation scales with a focus on accurate delineation of wetland boundaries and wetland components. Each ancillary input layer contributed to improving segmentation at different scales. Wetlands were classified using a nearest neighbor approach across a relatively undisturbed park site and an agricultural site using GeoEye1 imagery, and an urban site using WorldView2 data. Successful wetland classification was achieved across all study sites with an accuracy above 80%, though results suggest that overall a higher degree of landscape heterogeneity may negatively affect both segmentation and classification. The agricultural site suffered from the greatest amount of over and under segmentation, and lowest map accuracy (kappa: 0.78) which was partially attributed to confusion among a greater proportion of mixed vegetated classes from both wetlands and uplands. Accuracy of individual wetland classes based on the Canadian Wetland Classification system varied between each site, with kappa values ranging from 0.64 for the swamp class and 0.89 for the marsh class. This research developed a unique approach to mapping wetlands of various degrees of disturbance using GEOBIA, which can be applied to study other wetlands of similar settings. Ó

Wildlife diversity and abundance are declining globally and population reinforcement with captive‐reared animals is a common intervention used to prevent extinctions. Released captive‐reared individuals may undergo an acclimation period before their behavior and success is comparable to wild‐reared individuals because they lack experience with predators, complex habitats and variable environmental conditions. Quantifying post‐release acclimation effects on fitness and behavior is important for maximizing the success of reintroduction programs and for predicting the number of captive‐reared animals required for release. Endangered Blanding's turtles Emydoidea blandingii exhibit low recruitment and may benefit from population reinforcement with captive‐reared, ‘headstarted’ individuals (headstarts). We used 6 years of data to compare survival, growth, habitat use and movement ecology between wild‐hatched juvenile turtles and headstarts reared from eggs rescued from injured females. We found strong evidence of an acclimation effect in headstarts, with lower movement, growth, and survival during the first one to two years post‐release. Following this acclimation period, headstarts had movement, growth and survival similar to wild‐hatched juveniles. Habitat use did not differ between headstarts and wild‐hatched juveniles. We hypothesize that the acclimation period occurred because headstarts were introduced directly into the wild (i.e. ‘hard release’) and that providing additional support before or after release may improve the success of headstarts. Headstarts had a monthly survival probability of 0.89 in the first year post‐release, and 0.98 after the first year post‐release. We estimated that headstarts at our sites have approximately three times higher probability of surviving to 10 years of age, compared to wild‐hatched individuals at other sites. Our results highlight that headstarts should be released into habitat individually rather than in clusters, and highlight the need to investigate whether post‐release mortality of captive‐reared animals could be mitigated by increased acclimation to wild conditions, for example through prerelease periods in outdoor pens.

Remote sensing imagery is being used intensively to estimate the biochemical content of vegetation (e.g., chlorophyll, nitrogen, and lignin) at the leaf level. As a result of our need for vegetation biochemical information and our increasing ability to obtain canopy spectral data, a few techniques have been explored to scale leaf-level biochemical content to the canopy level for forests and crops. However, due to the contribution of non-green materials (i.e., standing dead litter, rock, and bare soil) from canopy spectra in semi-arid grasslands, it is difficult to obtain information about grassland biochemical content from remote sensing data at the canopy level. This paper summarizes available methods used to scale biochemical information from the leaf level to the canopy level and groups these methods into three categories: direct extrapolation, canopy-integrated approach, and inversion of physical models. As for semi-arid heterogeneous grasslands, we conclude that all methods are useful, but none are ideal. It is recommended that future research should explore a systematic upscaling framework which combines spatial pattern analysis, canopy-integrated approach, and modeling methods to retrieve vegetation biochemical content at the canopy level.

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