Allocating Harvests among Polar Bear Stocks in the Beaufort Sea

Recognition that polar bears are shared by hunters in Canada and Alaska prompted development of the “Polar Bear Management Agreement for the Southern Beaufort Sea.” Under this Agreement, the harvest of polar bears from the southern Beaufort Sea (SBS) is shared between Inupiat hunters of Alaska and Inuvialuit hunters of Canada. Quotas for each jurisdiction are to be reviewed annually in light of the best available scientific information. Ideal implementation of the Agreement has been hampered by the inability to quantify geographic overlap among bears from adjacent populations. We applied new analytical procedures to a more extensive radiotelemetry data set than has previously been available to quantify that overlap and thereby improve the efficacy of the Agreement. We constructed a grid over the eastern Chukchi Sea and Beaufort Sea and used twodimensional kernel smoothing to assign probabilities to the distributions of all instrumented bears. A cluster analysis of radio relocation data identified three relatively discrete groups or “populations” of polar bears: the SBS, Chukchi Sea (CS), and northern Beaufort Sea (NBS) populations. With kernel smoothing, we calculated relative probabilities of occurrence for individual members of each population in each cell of our grid. We estimated the uncertainty in probabilities by bootstrapping. Availability of polar bears from each population varied geographically. Near Barrow, Alaska, 50% of harvested bears are from the CS population and 50% from the SBS population. Nearly 99% of the bears taken by Kaktovik hunters are from the SBS. At Tuktoyaktuk, Northwest Territories, Canada, 50% are from the SBS and 50% from the NBS population. We displayed the occurrence of bears from each population as probabilities for each cell in our grid and as maps with contour lines delineating changes in relative probability. This new analytical approach will greatly improve the accuracy of allocating harvest quotas among hunting communities and jurisdictions while assuring that harvests remain within the bounds of sustainable yield.La reconnaissance du fait que l’ours polaire est chassé tant au Canada qu’en Alaska a initié la création de l’«Accord de gestion de l’ours polaire dans le sud de la mer de Beaufort». En vertu de cet accord, le prélèvement de l’ours polaire du sud de la mer de Beaufort est partagé entre les chasseurs inupiat de l’Alaska et les chasseurs inuvialuit du Canada. Les quotas pour chaque territoire de compétence doivent être révisés sur une base annuelle à la lumière de la meilleure information scientifique disponible. Une parfaite mise en oeuvre de l’accord a été rendue difficile en raison de l’impossibilité de quantifier le chevauchement géographique des populations d’ours voisines. En vue de quantifier ce chevauchement et d’améliorer ainsi l’efficacité de l’accord, on a appliqué de nouvelles procédures analytiques à un plus vaste ensemble de données télémétriques qu’on n’avait pu le faire auparavant. On a construit une grille recouvrant l’est de la mer des Tchouktches et la mer de Beaufort, et on a utilisé une méthode de lissage bidimensionnel par noyaux afin d’assigner des probabilités aux distributions de tous les ours appareillés. Une analyse de groupage des données de déplacement obtenues par radiocommunication a révélé trois groupes relativement distincts ou «populations» d’ours polaires, soit celles du sud de la mer de Beaufort (SMB), de la mer des Tchouktches (MT) et du nord de la mer de Beaufort (NMB). En recourant à la méthode de lissage par noyaux, on a calculé les probabilités relatives de présence des membres individuels de chaque population dans chacune des mailles de notre grille. On a évalué l’incertitude dans les probabilités par la méthode de bootstrapping. La disponibilité d’ours polaires au sein de chacune des populations variait géographiquement. Près de Barrow en Alaska, 50 % des ours prélevés viennent de la population MT, et 50 %, de la population SMB. Près de 99 % des ours abattus par les chasseurs de Kaktovik proviennent de la SMB. À Tuktoyaktuk, dans les Territoires du Nord-Ouest au Canada, 50 % des prises proviennent de la population SMB et 50 % de celle de la NMB. On a représenté la présence des ours de chaque population sous la forme de probabilités pour chaque maille de notre grille et sous celle de cartes avec courbes de niveau délimitant les changements dans la probabilité relative. Cette nouvelle approche analytique va grandement améliorer la justesse de l’attribution des quotas de prélèvement parmi les communautés de chasseurs et les territoires dont ils relèvent, tout en garantissant que les prélèvements restent dans les limites d’un rendement durable

Climate change threatens the world's marine protected areas

Marine protected areas (MPAs) are a primary management tool for mitigating threats to marine biodiversity 1,2 . MPAs and the species they protect, however, are increasingly being impacted by climate change. Here we show that, despite local protections, the warming associated with continued business-as-usual emissions (RCP8.5) 3 will likely result in further habitat and species losses throughout low-latitude and tropical MPAs 4,5 . With continued business-as-usual emissions, mean sea-surface temperatures within MPAs are projected to increase 0.035 °C per year and warm an additional 2.8 °C by 2100. Under these conditions, the time of emergence (the year when sea-surface temperature and oxygen concentration exceed natural variability) is mid-century in 42% of 309 no-take marine reserves. Moreover, projected warming rates and the existing 'community thermal safety margin' (the inherent buffer against warming based on the thermal sensitivity of constituent species) both vary among ecoregions and with latitude. The community thermal safety margin will be exceeded by 2050 in the tropics and by 2150 for many higher latitude MPAs. Importantly, the spatial distribution of emergence is stressor-specific. Hence, rearranging MPAs to minimize exposure to one stressor could well increase exposure to another. Continued business-as-usual emissions will likely disrupt many marine ecosystems, reducing the benefits of MPAs

Relating polar bears killed, human presence, and ice conditions in Svalbard 1987–2019

Conflicts between humans and polar bears have been predicted to increase as polar bear prime habitat, sea ice, is decreasing. In Svalbard, a strict protection and control schemes have secured near complete records of bears killed and found dead since 1987. We analyzed the trend in the number of kills and related this to human visitation to the archipelago. We found a slight decrease in the number of kills in the period 1987-2019, and a decrease in per capita number of kills when monthly kills were compared to the monthly number of visitors disembarking in the main settlement. We then used a discrete choice resource selection model to assess whether polar bear kill events are related to attributes of the kill sites and environmental conditions at the time. We divided Svalbard in four sectors, North, East, South, and West, and monthly average ice cover was calculated in 25-km rings around Svalbard, rings that were further delineated by the four sectors. We found that the odds of a kill was greater along the shoreline, and that the odds would be reduced by 50% when moving only 900 m from the shoreline when all sectors were included. Distance from other covariates like settlements, trapper’s cabins, and landing sites for tourists did for the most part not have a significant impact on the odds of a kill. Sectorwise, ice cover had no significant impact on the odds for a kill. The decreasing trend in kills of polar bears might partly be explained by the success of strict protection and management regimes of Svalbard wilderness

Bayesian networks to predict data mining algorithm behavior in ubiquitous computing environments

The growing demand of data mining services for ubiquitous computing environments necessitates deployment of appropriate mechanisms that make use of circumstantial factors to adapt the data mining behavior. Despite the efforts and results so far for efficient parameter tuning, incorporating dynamically changing context information on the parameter setting decision is lacking in the present work. Thus, Bayesian networks are used to learn, in possible situations the effects of data mining algorithm parameters on the final model obtained. Based on this knowledge, we propose to infer future algorithm configurations appropriate for situations. Instantiation of the approach for association rules is also shown in the paper and the feasibility of the approach is validated by the experimentation