According to EC regulations the deliberate release of genetically modified (GM) crops into the agro-environment needs to be accompanied by environmental monitoring to detect potential adverse effects, e.g. unacceptable levels of gene flow from GM to non-GM crops, or adverse effects on single species or species groups thus reducing biodiversity. There is, however, considerable scientific and public debate on how GM crops should be monitored with sufficient accuracy, discussing questions of potential adverse effects, agro-environmental variables or indicators to be monitored and respective detection methods; Another basic component, the appropriate number and location of monitoring sites, is hardly considered. Currently, no consistent GM crop monitoring approach combines these components systematically. This study focuses on and integrates spatial agro-environmental aspects at a landscape level in order to design monitoring networks. Based on examples of environmental variables associated with the cropping of Bt-Maize (Zea maize L.), herbicide-tolerant (HT) winter oilseed rape (Brassica napus L.), HT sugar beet (Beta vulgaris L.), and starch-modified potato (Solanum tuberosum L.), we develop a transferable framework and assessment scheme that comprises anticipated adverse environmental effects, variables to be measured and monitoring methods. These we integrate with a rule-based GIS (geographic information system) analysis, applying widely available spatial area and point information from existing environmental networks. This is used to develop scenarios with optimised regional GM crop monitoring networks.
Photographic capture–recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost‐effectiveness. Recently, several computer‐aided photo‐matching algorithms have been developed to more efficiently match images of unique individuals in databases with thousands of images. However, the identification accuracy of these algorithms can severely bias estimates of vital rates and population size. Therefore, it is important to understand the performance and limitations of state‐of‐the‐art photo‐matching algorithms prior to implementation in capture–recapture studies involving possibly thousands of images. Here, we compared the performance of four photo‐matching algorithms; Wild‐ID, I3S Pattern+, APHIS, and AmphIdent using multiple amphibian databases of varying image quality. We measured the performance of each algorithm and evaluated the performance in relation to database size and the number of matching images in the database. We found that algorithm performance differed greatly by algorithm and image database, with recognition rates ranging from 100% to 22.6% when limiting the review to the 10 highest ranking images. We found that recognition rate degraded marginally with increased database size and could be improved considerably with a higher number of matching images in the database. In our study, the pixel‐based algorithm of AmphIdent exhibited superior recognition rates compared to the other approaches. We recommend carefully evaluating algorithm performance prior to using it to match a complete database. By choosing a suitable matching algorithm, databases of sizes that are unfeasible to match “by eye” can be easily translated to accurate individual capture histories necessary for robust demographic estimates.
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