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BackgroundThe invasion of rangelands by invasive plant species is a major threat to biodiversity in most parts of Zimbabwe posing not only an ecological challenge but a growing management and eradication challenge as well. However, there is sparse information relating to the spatial distribution of these species and the conditions promoting their growth and development particularly in Sothern Africa. The study assessed the spatial distribution of the invasive large fever berry tree and associated soil type in Sengwa Wildlife Research Area. A mixed methods research design triangulating qualitative and quantitative methods was utilized. All known sites occupied by large fever berry trees were obtained from the Sengwa Wildlife Research Institute’s records on invasive plant species. Ground truthing was done for all sites and global positioning system coordinates of occupied areas were collected. Mapping of invaded areas by the large fever berry tree was done using Quantum GIS software. Coordinates were imported to show points with the large fever berry tree. Altitude of invaded areas and soil samples were also collected for soil analysis and a soil texture triangle was used to come up with the soil type associated with the growth and spread of the large fever berry trees.ResultsThe results show that the large fever berry tree occupied areas along major rivers and streams on loam soils. An area of 16.5km2 which is 4.4% of the Sengwa Wildlife Research Area is invaded by the large fever berry tree. Results further indicated that sandy- loam soils were associated with the growth and development of the large fever berry tree in Sengwa Wildlife Research Area. Veld fires were also identified as a factor influencing the spread of the large fever berry tree species in the Sengwa Wildlife Research Area.ConclusionIn conclusion, a holistic framework was developed to curb the invasion of the large fever berry tree in Sengwa Wildlife Research Area. It is recommended that further studies be conducted outside the protected area to establish soil characteristics and invasion rates in order to fully understand drivers of its invasion.

Examines Inuit relations with the Canadian state, with a particular focus on regulating Inuit based on government animal counting methods, and the emerging regime of government intervention.

A primary consideration of abundance studies that use unmarked animals is whether survey counts accurately reflect the population size or if unknown variation in animal movement or detection probability biases counts irrespective of population size. We posited that high repeatability in counts among temporally replicated surveys would indicate that counts are a good index of abundance. We temporally replicated 49 nocturnal spotlight surveys of white-tailed deer (Odocoileus virginianus) up to three times each (n = 128 total samples) to test the repeatability of this commonly used wildlife monitoring technique. Repeatability was high (R = 0.86), suggesting spring spotlight surveys provide a reliable index of deer population size in Iowa, USA. Fourteen percent of the variation among replicated counts was explained by day of year and, to a lesser degree, a vegetation green-up index. Detection probability was high (~0.70) early in the sampling season and declined considerably during the following 6 weeks. Deer abundance was greater at sites with higher percent landcovers of forest and hay/pasture and was lower at sites with higher landcover in crops. Our findings suggest deer managers should sample prior to green-up in the spring to maximize the proportion of the population that is detectable, and that accounting for seasonality on detection estimation is important for reliable abundance estimates if sampling occurs over a range of phenological progression. Finally, we show that temporal replication of surveys is a logistically feasible method to assess the reliability of abundance estimates from study designs that are normally conducted with single visits.

Satellite telemetry is an increasingly utilized technology in wildlife research, and current devices can track individual animal movements at unprecedented spatial and temporal resolutions. However, as we enter the golden age of satellite telemetry, we need an in-depth understanding of the main technological, species-specific and environmental factors that determine the success and failure of satellite tracking devices across species and habitats. Here, we assess the relative influence of such factors on the ability of satellite telemetry units to provide the expected amount and quality of data by analyzing data from over 3,000 devices deployed on 62 terrestrial species in 167 projects worldwide. We evaluate the success rate in obtaining GPS fixes as well as in transferring these fixes to the user and we evaluate failure rates. Average fix success and data transfer rates were high and were generally better predicted by species and unit characteristics, while environmental characteristics influenced the variability of performance. However, 48% of the unit deployments ended prematurely, half of them due to technical failure. Nonetheless, this study shows that the performance of satellite telemetry applications has shown improvements over time, and based on our findings, we provide further recommendations for both users and manufacturers.

Although most wildlife professionals agree that science should inform wildlife management decisions, disconnect still exists between researchers and managers. If researchers are not striving to incorporate their findings into management decisions, support for research programs by managers can wane. If managers are not using research findings to inform management decisions, those decisions may be less effective or more vulnerable to legal challenges. Both of these situations can have negative consequences for wildlife conservation. We outline a collaborative research-management approach to bridging the gap between wildlife managers and researchers. We describe differences in perspectives, perceptions, and priorities between managers and researchers; outline how and why the divide between researchers and managers has likely occurred and continues to grow; and present specific strategies and recommendations to foster stronger collaborations between managers and researchers. We advocate increased synergy between managers and researchers based on a shared vision of conservation and a collaborative structure that rewards researchers and managers. Most importantly, we suggest that relationships and communication between managers and researchers must be established early in research development and decision-making processes, fostering the trust needed for collaboration. Institutions and agencies can facilitate these relationships by creating opportunities and incentives for integrating collaborative research into management decisions. We suggest this approach will strengthen ties between researchers and managers, increase relevance of research to management decisions, promote effectiveness of management decisions, reduce legal challenges, and ultimately produce positive, tangible, and lasting effects on wildlife conservation.

A primary consideration of abundance studies that use unmarked animals is whether survey counts accurately reflect the population size or if unknown variation in animal movement or detection probability biases counts irrespective of population size. We posited that high repeatability in counts among temporally replicated surveys would indicate that counts are a good index of abundance. We temporally replicated 49 nocturnal spotlight surveys of white-tailed deer ( Odocoileus virginianus ) up to three times each ( n = 128 total samples) to test the repeatability of this commonly used wildlife monitoring technique. Repeatability was high ( R = 0.86), suggesting spring spotlight surveys provide a reliable index of deer population size in Iowa, USA. Fourteen percent of the variation among replicated counts was explained by day of year and, to a lesser degree, a vegetation green-up index. Detection probability was high (~0.70) early in the sampling season and declined considerably during the following 6 weeks. Deer abundance was greater at sites with higher percent landcovers of forest and hay/pasture and was lower at sites with higher landcover in crops. Our findings suggest deer managers should sample prior to green-up in the spring to maximize the proportion of the population that is detectable, and that accounting for seasonality on detection estimation is important for reliable abundance estimates if sampling occurs over a range of phenological progression. Finally, we show that temporal replication of surveys is a logistically feasible method to assess the reliability of abundance estimates from study designs that are normally conducted with single visits.

For wildlife inhabiting snowy environments, snow properties such as onset date, depth, strength, and distribution can influence many aspects of ecology, including movement, community dynamics, energy expenditure, and forage accessibility. As a result, snow plays a considerable role in individual fitness and ultimately population dynamics, and its evaluation is, therefore, important for comprehensive understanding of ecosystem processes in regions experiencing snow. Such understanding, and particularly study of how wildlife–snow relationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. However, studying and monitoring wildlife–snow relationships continue to be challenging because characterizing snow, an inherently complex and constantly changing environmental feature, and identifying, accessing, and applying relevant snow information at appropriate spatial and temporal scales, often require a detailed understanding of physical snow science and technologies that typically lie outside the expertise of wildlife researchers and managers. We argue that thoroughly assessing the role of snow in wildlife ecology requires substantive collaboration between researchers with expertise in each of these two fields, leveraging the discipline‐specific knowledge brought by both wildlife and snow professionals. To facilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five‐step protocol: (1) identify relevant snow property information; (2) specify spatial, temporal, and informational requirements; (3) build the necessary datasets; (4) implement quality control procedures; and (5) incorporate snow information into wildlife analyses. Additionally, we explore the types of snow information that can be used within this collaborative framework. We illustrate, in the context of two examples, field observations, remote‐sensing datasets, and four example modeling tools that simulate spatiotemporal snow property distributions and, in some cases, evolutions. For each type of snow data, we highlight the collaborative opportunities for wildlife and snow professionals when designing snow data collection efforts, processing snow remote sensing products, producing tailored snow datasets, and applying the resulting snow information in wildlife analyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best available snow science through collaboration with snow professionals.