Evaluierung von sechs Fotofallenmodellen hinsichtlich der Eignung für Fang-Wiederfang Methoden beim Eurasischen Luchs (Lynx lynx)

Digital outdoor cameras are increasingly used in wildlife research because they allow species inventories, population estimates, and behavior or activity observations. Which camera model is suitable and practical depends on environmental conditions, focus species and specific scientific questions posed. Here we focused on testing cameras appropriate for elusive species that can be identified visually owing to individual coat patterns. Specifically the camera should be adequate for calculating the minimum population of Eurasian Lynx (Lynx lynx) during a systematic monitoring with camera traps. Therefore we tested six digital camera models with regard to trigger speed and the image quality necessary for visual identification of pacing lynx on trails. The decision if a camera model is adequate for the scientific goal was regulated due to priority levels under laboratory conditions. Only one camera model proved to be suitable for camera-trap monitoring. Our practical camera test can be used to evaluate newer models of digital cameras as they become available. This application opens an avenue for a non-invasive population monitoring of rare and elusive species in a low mountain range area.Digitale Fotofallen werden weltweit in der Wildtierforschung eingesetzt. Die Einsatzgebiete sind vielfältig, sie reichen von Artenbestandsaufnahmen und Populationsschätzungen über die Verhaltensforschung bis hin zu Aktivitätsanalysen. Das jeweilig eingesetzte Kameramodell muss an die Aufnahmesituation und die Zielsetzung der Analyse angepasst sein. Das Ziel unseres Fotofallentests war es, ein Modell zu finden, welches für die visuelle Identifizierung von Fellmustern des Eurasischen Luchses geeignet ist. Die Fotofalle soll in einem systematischen Monitoring für die minimale Anzahl der im Gebiet vorkommenden Luchse und deren Populationsschätzung mit Fang-Wiederfang Methoden eingesetzt werden können. Bei dem Test von sechs Fotofallenmodellen, fiel das Hauptaugenmerk auf die Auslösegeschwindigkeit und die Bildqualität welche die nötigen Faktoren für die Sicherstellung der visuellen Identifikation von schreitenden Luchsen am Wildwechsel darstellen. Zur Entscheidungsfindung der Eignung eines Fotofallenmodells für die Fragestellung definierten wir Prioritätslevel unter Laborbedingungen. Es stellte sich heraus, dass nur ein Fotofallenmodell die Ansprüche erfüllte. Der praktische Fotofallentest kann für neuerscheinende Fotofallenmodelle adaptiert werden. Diese Anwendung eröffnet die Möglichkeit für ein nicht invasives Monitoring in Mittelgebirgslandschaften

Large-Scale Sheep Losses to Wolves (Canis lupus) in Germany Are Related to the Expansion of the Wolf Population but Not to Increasing Wolf Numbers

Recovery of predator populations triggers conflicts due to livestock depredation losses, particularly in Germany where the wolf (Canis lupus) population grows exponentially and livestock (especially sheep) losses raise public concerns and motivate the authorities to control wolf numbers. Yet, the effects of wolf numbers and alternative factors, such as abundance of prey and livestock, on livestock losses in this country are not investigated. In this study, we collected and analyzed data on the numbers of reproductive units of wolves (packs and pairs together) as a surrogate of adult wolf numbers, sheep killed by wolves, living sheep, red deer (Cervus elaphus), roe deer (Capreolus capreolus), and wild boar (Sus scrofa) in every German state and year from 2002 to 2019. We applied a negative binomial Generalized Linear Mixed Model (GLMM) to estimate the effects of these predictors on the numbers of sheep killed by wolves. We also examined the relationships between the percentages of killed/living sheep and the numbers of living sheep. Ranking of 63 models based on the Akaike information criterion revealed that sheep losses were determined by state, year, and number of living sheep, not by wolf numbers, at high precision and accuracy. The number of sheep killed by wolves increased consistently by 41% per year and by 30% for every additional 10,000 sheep, mainly in the north where most wolf territories are concentrated. This means that sheep are protected insufficiently and/or ineffectively. The percentages of killed/living sheep consistently increased by 0.02–0.05% per state and year, with the maximum percentage of 0.7%, on a backdrop of decreasing numbers of living sheep. In conclusion, we demonstrate that sheep losses in Germany have been driven by the expansion of the wolf population, not by wolf numbers, and by the number of sheep available. We suggest that Germany’s wolf conservation policy should focus on alternative nonlethal interventions, enforcement and standardization of intervention monitoring, and promotion of wolf tolerance rather than on lethal control of wolf population size.publishedVersio

Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning

"© 2022 The Author(s). Published by Elsevier B.V. on behalf of International Society of Photogrammetry and Remote Sensing (isprs). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)"Mapping standing dead trees, especially, in natural forests is very important for evaluation of the forest's health status, and its capability for storing Carbon, and the conservation of biodiversity. Apparently, natural forests have larger areas which renders the classical field surveying method very challenging, time-consuming, labor-intensive, and unsustainable. Thus, for effective forest management, there is the need for an automated approach that would be cost-effective. With the advent of Machine Learning, Deep Learning has proven to successfully achieve excellent results. This study presents an adjusted Mask R-CNN Deep Learning approach for detecting and segmenting standing dead trees in a mixed dense forest from CIR aerial imagery using a limited (195 images) training dataset. First, transfer learning is considered coupled with the image augmentation technique to leverage the limitation of training datasets. Then, we strategically selected hyperparameters to suit appropriately our model's architecture that fits well with our type of data (dead trees in images). Finally, to assess the generalization capability of our model's performance, a test dataset that was not confronted to the deep neural network was used for comprehensive evaluation. Our model recorded promising results reaching a mean average precision, average recall, and average F1-Score of 0.85, 0.88, and 0.87 respectively, despite our relatively low resolution (20 cm) dataset. Consequently, our model could be used for automation in standing dead tree detection and segmentation for enhanced forest management. This is equally significant for biodiversity conservation, and forest Carbon storage estimation.publishedVersio

Determining Statistically Robust Changes in Ungulate Browsing Pressure as a Basis for Adaptive Wildlife Management

Ungulate browsing has a major impact on the composition and structure of forests. Repeatedly conducted, large-scale regeneration inventories can monitor the extent of browsing pressure and its impacts on forest regeneration development. Based on the respective results, the necessity and extent of wildlife management activities such as hunting, fencing, etc., can be identified at a landscape scale. However, such inventories have rarely been integrated into wildlife management decision making. In this article, we evaluate a regeneration inventory method which was carried out in the Bavarian Forest National Park between 2007 and 2018. We predict the browsing impact by calculating browsing probabilities using a logistic mixed effect model. To provide wildlife managers with feedback on their activities, we developed a test which can assess significant changes in browsing probability between different inventory periods. To find the minimum observable browsing probability change, we simulated ungulate browsing based on the data of a potential browsing indicator species (Sorbus aucuparia) in the National Park. Sorbus aucuparia is evenly distributed, commonly found, selectively browsed and meets the ecosystem development objectives in our study area. We were able to verify a browsing probability change down to ±5 percentage points with a sample size of about 1,000 observations per inventory run. In view of the size of the National Park and the annual fluctuations in browsing pressure, this estimation accuracy seems sufficient. In seeking the maximal cost-efficiency, we were able to reduce this sample size in a sensitivity analysis by about two thirds without severe loss of information for wildlife management. Based on our findings, the presented inventory method combined with our evaluation tool has the potential to be a robust and efficient instrument to assess the impact of herbivores that are in the National Park and other region.publishedVersio

Estimating fine-scale visibility in a temperate forest landscape using airborne laser scanning

Visibility is a key factor influencing animal behavior in forest ecosystems. Fine-scale visibility in forested areas has been measured by ground-based approaches at the plot level, using site-specific methods that have limited spatial coverage. Here we examine airborne laser scanning (ALS) as a novel tool to quantify fine-scale visibility in the temperate forests of Germany at a landscape scale. We validate the (vertically derived) ALS-derived visibility measures using proven (horizontally derived) terrestrial laser scanning (TLS) estimates of true visibility. Our results indicate that there is a good agreement between the visibility resulting from ALS and TLS with an R2 ranging from 0.53 to 0.84 and a normalized RMSE varying from 15.92% to 11.81% at various plot sizes, with the highest accuracy achieved using a plot size of 35 × 35 m. Our study demonstrates for the first time that ALS can be successfully applied to quantify fine-scale visibility in temperate forests at a landscape level. This approach holds potential for studying the spatial behavior of animals (e.g., habitat selection and predator–prey relationships) in forest ecosystems.publishedVersio

LiDAR reveals a preference for intermediate visibility by a forest-dwelling ungulate species

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological SocietyAbstract 1. Visibility (viewshed) plays a significant and diverse role in animals' behaviour and fitness. Understanding how visibility influences animal behaviour requires the measurement of habitat visibility at spatial scales commensurate to individual animal choices. However, measuring habitat visibility at a fine spatial scale over a landscape is a challenge, particularly in highly heterogeneous landscapes (e.g. forests). As a result, our ability to model the influence of fine-scale visibility on animal behaviour has been impeded or limited. 2. In this study, we demonstrate the application of the concept of three-dimensional (3D) cumulative viewshed in the study of animal spatial behaviour at a landscape level. Specifically, we employed a newly described approach that combines ter-restrial and airborne light detection and ranging (LiDAR) to measure fine-scale habitat visibility (3D cumulative viewshed) on a continuous scale in forested landscapes. We applied the LiDAR-derived visibility to investigate how visibility in forests affects the summer habitat selection and the movement of 20 GPS- collared female red deer Cervus elaphus in a temperate forest in Germany. We used integrated step selection analysis to determine whether red deer show any preference for fine-scale habitat visibility and whether visibility is related to the rate of movement of red deer. 3. We found that red deer selected intermediate habitat visibility. Their preferred level of visibility during the day was substantially lower than that of night and twilight, whereas the preference was not significantly different between night and twilight. In addition, red deer moved faster in high-visibility areas, possibly mainly to avoid predation and anthropogenic risk. Furthermore, red deer moved most rapidly between locations in the twilight. 4. For the first time, the preference for intermediate habitat visibility and the adap-tion of movement rate to fine-scale visibility by a forest-dwelling ungulate spe-cies at a landscape scale was revealed. The LiDAR technique used in this study offers fine-scale habitat visibility at the landscape level in forest ecosystems, which would be of broader interest in the fields of animal ecology and behaviour.publishedVersio

LiDAR reveals a preference for intermediate visibility by a forest-dwelling ungulate species

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological SocietyAbstract 1. Visibility (viewshed) plays a significant and diverse role in animals' behaviour and fitness. Understanding how visibility influences animal behaviour requires the measurement of habitat visibility at spatial scales commensurate to individual animal choices. However, measuring habitat visibility at a fine spatial scale over a landscape is a challenge, particularly in highly heterogeneous landscapes (e.g. forests). As a result, our ability to model the influence of fine-scale visibility on animal behaviour has been impeded or limited. 2. In this study, we demonstrate the application of the concept of three-dimensional (3D) cumulative viewshed in the study of animal spatial behaviour at a landscape level. Specifically, we employed a newly described approach that combines ter-restrial and airborne light detection and ranging (LiDAR) to measure fine-scale habitat visibility (3D cumulative viewshed) on a continuous scale in forested landscapes. We applied the LiDAR-derived visibility to investigate how visibility in forests affects the summer habitat selection and the movement of 20 GPS- collared female red deer Cervus elaphus in a temperate forest in Germany. We used integrated step selection analysis to determine whether red deer show any preference for fine-scale habitat visibility and whether visibility is related to the rate of movement of red deer. 3. We found that red deer selected intermediate habitat visibility. Their preferred level of visibility during the day was substantially lower than that of night and twilight, whereas the preference was not significantly different between night and twilight. In addition, red deer moved faster in high-visibility areas, possibly mainly to avoid predation and anthropogenic risk. Furthermore, red deer moved most rapidly between locations in the twilight. 4. For the first time, the preference for intermediate habitat visibility and the adap-tion of movement rate to fine-scale visibility by a forest-dwelling ungulate spe-cies at a landscape scale was revealed. The LiDAR technique used in this study offers fine-scale habitat visibility at the landscape level in forest ecosystems, which would be of broader interest in the fields of animal ecology and behaviour.publishedVersio