@article{GohrvonWehrdenMayetal.2022, author = {Gohr, Charlotte and von Wehrden, Henrik and May, Felix and Ibisch, Pierre L.}, title = {Remotely sensed effectiveness assessments of protected areas lack a common framework: A review}, series = {Ecosphere}, volume = {13}, journal = {Ecosphere}, number = {4}, publisher = {Wiley}, issn = {2150-8925}, doi = {10.1002/ecs2.4053}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-4799}, pages = {14}, year = {2022}, abstract = {Effective protected areas reflect socio-ecological values, such as biodiversity and habitat maintenance, as well as human well-being. These values, which safeguard ecosystem services in protected areas, are treated as models for the sustainable preservation and use of resources. While there is much research on the effectiveness of protected areas in a variety of disciplines, the question is whether there is a common framework that uses remote sensing methods. We conducted a qualitative and a quantitative analysis of 44 peer-reviewed scientific papers utilizing remote sensing data in order to examine the effectiveness of protected areas. Very few studies to date have a wide or even a global geographical focus; instead, most quantify the effectiveness of protected areas by focusing on local-scale case studies and single indicators such as forest cover change. Methods that help integrate spatial selection approaches, to compare a protected area's characteristics with its surroundings, are increasingly being used. Based on this review, we argue for a multi-indicator-based framework on protected area effectiveness, including the development of a consistent set of socio-ecological indicators for a global analysis. In turn, this will allow for globally applicable use, including a concrete evaluation that considers the diversity of regional parameters, biome-specific variables, and political frameworks. Ideally, such a framework will enhance the monitoring and evaluation of global strategies and conventions.}, language = {en} } @article{WeissvonWehrdenLinde2023, author = {Weiss, Fabio and von Wehrden, Henrik and Linde, Andreas}, title = {Random year intercepts in mixed models help to assess uncertainties in insect population trends}, series = {Insect Conservation and Diversity}, volume = {16}, journal = {Insect Conservation and Diversity}, number = {4}, publisher = {Wiley}, issn = {1752-4598}, doi = {10.1111/icad.12644}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-6496}, pages = {531 -- 537}, year = {2023}, abstract = {1. An increasing number of studies is investigating insect population trends based on time series data. However, the available data is often subject to temporal pseudoreplication. Inter-annual variability of environmental conditions and strong fluctuations in insect abundances can impede reliable trend estimation. Temporal random effect structures in regression models have been proposed as solution for this issue, but remain controversial. 2. We investigated trends in ground beetle abundance across 24 years using generalised linear mixed models. We fitted four models: A base model, a model featuring a random year intercept, a model featuring basic weather parameters, and a model featuring both random year intercept and weather parameters. We then performed a simple sensitivity analysis to assess the robustness of the four models with respect to influential years, also testing for possible spurious baseline and snapshot effects. 3. The model structure had a significant impact on the overall magnitude of the estimated trends. However, we found almost no difference among the models in how the removal of single years (sensitivity analysis) relatively affected trend coefficients. The two models with a random year intercept yielded significantly larger confidence intervals and their p-values were more sensitive during sensitivity analysis. Significant differences of the model with random year intercept and weather parameters to all other models suggest that the random year effects and specific weather effects are rather additive than interchangeable. 4. We conclude that random year intercepts help to produce more reliable and cautious uncertainty measures for insect population trends. Moreover, they might help to identify influential years in sensitivity analyses more easily. We recommend random year intercepts in addition to any variables representing temporally variable environmental conditions, such as weather variables.}, language = {en} }