Seasonal diet changes in elephant and impala in mopane woodland

Elephant and impala as intermediate feeders, having a mixed diet of grass and browse, respond to seasonal fluctuations of forage quality by changing their diet composition. We tested the hypotheses that (1) the decrease in forage quality is accompanied by a change in diet from more monocots in the wet season to more dicots in the dry season and that that change is more pronounced and faster in impala than in elephant; (2) mopane (Colophospermum mopane), the most abundant dicot species, is the most important species in the elephant diet in mopane woodland, whereas impala feed relatively less on mopane due to the high condensed tannin concentration; and (3) impala on nutrient-rich soils have a diet consisting of more grass and change later to diet of more browse than impala on nutrient-poor soils. The phosphorus content and in vitro digestibility of monocots decreased and the NDF content increased significantly towards the end of the wet season, whereas in dicots no significant trend could be detected. We argue that this decreasing monocot quality caused elephant and impala to consume more dicots in the dry season. Elephant changed their diet gradually over a 16-week period from 70% to 25% monocots, whereas impala changed diets rapidly (2-4 weeks) from 95% to 70% monocots. For both elephants and impala, there was a positive correlation between percentage of monocots and dicots in the diet and the in vitro digestibility of these forage items. Mopane was the most important dicot species in the elephant diet and its contribution to the diet increased significantly in the dry season, whereas impala selected other dicot species. On nutrient-rich gabbroic soils, impala ate significantly more monocots than impala from nutrient-poor granitic soils, which was related to the higher in vitro digestibility of the monocots on gabbroic soil. Digestibility of food items appears to be an important determinant of diet change from the wet to the dry season in impala and elephants

Dutch nature conservation objectives from a European perspective

In Dutch policy the European importance of species and habitats is one of the reasons to decide if a species or habitat should become a target species for Dutch policy. This study reviews the different philosophies behind previous studies on the international or European importance of Dutch species and habitats. It furthermore analysis the consequences of changing the criteria or thresholds for determining species of European importance for the number and type of species selected

Can point shear wave elastography differentiate focal nodular hyperplasia from hepatocellular adenoma

Purpose: Focal nodular hyperplasia (FNH) and hepatocellular adenoma (HCA) are liver tumors that require different management. We assessed the potential of point shear wave elastography (pSWE) to differentiate FNH from HCA and the interobserver and intraobserver reliability of pSWE in the examination of these lesions and of native liver tissue (NLT). Methods: The study included 88 patients (65 FNH, 23 HCA). pSWE was performed by two experienced liver sonographers (observers 1 [O1] and 2 [O2]) and acquired within the lesion of interest and NLT. Group differences, optimal cutoff for characterization and interobserver reliability was assessed with Mann-Whitney-U, area under the ROC curce (AUROC) and intraclass correlation coefficient (ICC). Intraobserver reliability in NLT was assessed in 20 healthy subjects using ICC. Results: Median stiffness was significantly higher in FNH than in HCA (7.01 kPa vs 4.98 kPa for O1 (P=0.017) and 7.68 kPa vs 6.00 kPa for O2 (P=0.031)). A cutoff point for differentiation between the two entities could not be determined with an AUROC of 0.67 (O1) and 0.69 (O2). Interobserver reliability was good for lesion- stiffness (ICC=0.86) and poor for NLT stiffness (ICC=0.09). In healthy subjects, intraobserver reliability for NLT-stiffness was poor for O1 (ICC=0.23) and moderate for O2 (ICC=0.62). Conclusion: This study shows that pSWE cannot reliably differentiate FNH from HCA. Interobserver and intraobserver reliability for pSWE in NLT were insufficient. Interpretation of results gained with this method should be done with great caution

Costs and benefits of plant defence suppression by Tetranychus evansi spider mites

Herbivores eat plants, and plants defend their tissues. To overcome these plant defences, herbivores have evolved a variety of offensive strategies. Some herbivores use enzymes to detoxify defensive compounds of their hosts, and others suppress plant defences by manipulating a plant’s physiological processes. How and why has this diversity of herbivore offensive strategies evolved? In this thesis, I studied evolutionary costs and benefits of plant defence suppression by the herbivorous spider mite Tetranychus evansi. By quantitatively reviewing past evidence, I confirmed that T. evansi can attain a considerable fecundity benefit by suppressing the inducible defences of their tomato hosts. However, competing herbivores can impose considerable costs on this offensive strategy, because they can also benefit from the suppressed defences of a shared host plant, and can subsequently induce these defences to the disadvantage of T. evansi. Furthermore, I investigated T. evansi populations from several locations around the world, and found that suppression of plant defences is variable within and among T. evansi populations. Given this variation, I allowed a genetically diverse T. evansi population to evolve on plants with different levels of defence, and expected T. evansi to evolve lower levels of suppression on plants without functional defences. However, I found that T. evansi retained its level of defence suppression on such plants, indicating that metabolic costs are likely low. I discuss the evolution of defence suppression from the perspective of costs and benefits, highlight gaps in our knowledge of defence suppression by T. evansi, and provide detailed suggestions to tackle remaining questions

Timely poacher detection and localization using sentinel animal movement

Wildlife crime is one of the most profitable illegal industries worldwide. Current actions to reduce it are far from effective and fail to prevent population declines of many endangered species, pressing the need for innovative anti-poaching solutions. Here, we propose and test a poacher early warning system that is based on the movement responses of non-targeted sentinel animals, which naturally respond to threats by fleeing and changing herd topology. We analyzed human-evasive movement patterns of 135 mammalian savanna herbivores of four different species, using an internet-of-things architecture with wearable sensors, wireless data transmission and machine learning algorithms. We show that the presence of human intruders can be accurately detected (86.1% accuracy) and localized (less than 500 m error in 54.2% of the experimentally staged intrusions) by algorithmically identifying characteristic changes in sentinel movement. These behavioral signatures include, among others, an increase in movement speed, energy expenditure, body acceleration, directional persistence and herd coherence, and a decrease in suitability of selected habitat. The key to successful identification of these signatures lies in identifying systematic deviations from normal behavior under similar conditions, such as season, time of day and habitat. We also show that the indirect costs of predation are not limited to vigilance, but also include (1) long, high-speed flights; (2) energetically costly flight paths; and (3) suboptimal habitat selection during flights. The combination of wireless biologging, predictive analytics and sentinel animal behavior can benefit wildlife conservation via early poacher detection, but also solve challenges related to surveillance, safety and health.</p

Improving the precision and accuracy of animal population estimates with aerial image object detection

Animal population sizes are often estimated using aerial sample counts by human observers, both for wildlife and livestock. The associated methods of counting remained more or less the same since the 1970s, but suffer from low precision and low accuracy of population estimates. Aerial counts using cost-efficient Unmanned Aerial Vehicles or microlight aircrafts with cameras and an automated animal detection algorithm can potentially improve this precision and accuracy. Therefore, we evaluated the performance of the multi-class convolutional neural network RetinaNet in detecting elephants, giraffes and zebras in aerial images from two Kenyan animal counts. The algorithm detected 95% of the number of elephants, 91% of giraffes and 90% of zebras that were found by four layers of human annotation, of which it correctly detected an extra 2.8% of elephants, 3.8% giraffes and 4.0% zebras that were missed by all humans, while detecting only 1.6 to 5.0 false positives per true positive. Furthermore, the animal detections by the algorithm were less sensitive to the sighting distance than humans were. With such a high recall and precision, we posit it is feasible to replace manual aerial animal count methods (from images and/or directly) by only the manual identification of image bounding boxes selected by the algorithm and then use a correction factor equal to the inverse of the undercounting bias in the calculation of the population estimates. This correction factor causes the standard error of the population estimate to increase slightly compared to a manual method, but this increase can be compensated for when the sampling effort would increase by 23%. However, an increase in sampling effort of 160% to 1,050% can be attained with the same expenses for equipment and personnel using our proposed semi-automatic method compared to a manual method. Therefore, we conclude that our proposed aerial count method will improve the accuracy of population estimates and will decrease the standard error of population estimates by 31% to 67%. Most importantly, this animal detection algorithm has the potential to outperform humans in detecting animals from the air when supplied with images taken at a fixed rate.</p

Improving the precision and accuracy of animal population estimates with aerial image object detection

Animal population sizes are often estimated using aerial sample counts by human observers, both for wildlife and livestock. The associated methods of counting remained more or less the same since the 1970s, but suffer from low precision and low accuracy of population estimates. Aerial counts using cost-efficient Unmanned Aerial Vehicles or microlight aircrafts with cameras and an automated animal detection algorithm can potentially improve this precision and accuracy. Therefore, we evaluated the performance of the multi-class convolutional neural network RetinaNet in detecting elephants, giraffes and zebras in aerial images from two Kenyan animal counts. The algorithm detected 95% of the number of elephants, 91% of giraffes and 90% of zebras that were found by four layers of human annotation, of which it correctly detected an extra 2.8% of elephants, 3.8% giraffes and 4.0% zebras that were missed by all humans, while detecting only 1.6 to 5.0 false positives per true positive. Furthermore, the animal detections by the algorithm were less sensitive to the sighting distance than humans were. With such a high recall and precision, we posit it is feasible to replace manual aerial animal count methods (from images and/or directly) by only the manual identification of image bounding boxes selected by the algorithm and then use a correction factor equal to the inverse of the undercounting bias in the calculation of the population estimates. This correction factor causes the standard error of the population estimate to increase slightly compared to a manual method, but this increase can be compensated for when the sampling effort would increase by 23%. However, an increase in sampling effort of 160% to 1,050% can be attained with the same expenses for equipment and personnel using our proposed semi-automatic method compared to a manual method. Therefore, we conclude that our proposed aerial count method will improve the accuracy of population estimates and will decrease the standard error of population estimates by 31% to 67%. Most importantly, this animal detection algorithm has the potential to outperform humans in detecting animals from the air when supplied with images taken at a fixed rate.</p

Toets herijking EHS Gelderland

De provincie Gelderland heeft samen met gemeenten en Manifestpartners een herijkte provinciale EHS uitgewerkt. De herijkte EHS is getoetst op de internationale doelen in het kader van (1) de Vogel- en Habitatrichtlijn (VHR), (2) de Kaderrichtlijn Water (KRW), en (3) de ambities die voortvloeien uit het Gelders Coalitieakkoord Uitdagend Gelderland. Uit de analyses blijkt dat de herijkte EHS een verbetering oplevert ten opzichte van de huidige EHS. Voor het behalen van deze verbetering is het noodzakelijk dat de plannen van Gelderland en de Manifestpartners in zijn geheel worden uitgevoerd. Met de oorspronkelijke EHS zouden verdere verbeteringen kunnen worden gerealiseerd, maar ook hiervoor geldt dat zonder meer oppervlak voor natuur de doelstellingen van de VHR niet volledig gerealiseerd kunnen worden