Ecological differences and coexistence in a guild of microparasites: Bartonella in wild rodents

The study of ecological differences among coexisting microparasites has been largely neglected, but it addresses important and unusual issues because there is no clear distinction in such cases between conventional (resource) and apparent competition. Here patterns in the population dynamics are examined for four species of Bartonella (bacterial parasites) coexisting in two wild rodent hosts, bank voles (Clethrionomys glareolus) and wood mice (Apodemus sylvaticus). Using generalized linear modeling and mixed effects models, we examine, for these four species, seasonal patterns and dependencies on host density (both direct and delayed) and, having accounted for these, any differences in prevalence between the two hosts. Whereas previous studies had failed to uncover species differences, here all four were different. Two, B. doshiae and B. taylorii, were more prevalent in wood mice, and one, B. birtlesii, was more prevalent in bank voles. B. birtlesii, B. grahamii, and B. taylorii peaked in prevalence in the fall, whereas B. doshiae peaked in spring. For B. birtlesii in bank voles, density dependence was direct, but for B. taylorii in wood mice density dependence was delayed. B. birtlesii prevalence in wood mice was related to bank vole density. The implications of these differences for species coexistence are discussed.     

Climate change risk management: a Mental Modeling application

The potential impacts of climate change are varied and highly uncertain, and pose a significant challenge to agencies charged with managing environmental risks. This paper presents a comprehensive and structured Mental Modeling approach to elicit, organize and present relevant information from experts and stakeholders about the factors influencing environmental risk management in the face of climate change. We present and review an initiative undertaken by the United States Army Corps of Engineers (USACE) to characterize climate change challenges to USACE environmental risk management activities, and to identify gaps with respect to science, engineering, and organizational processes for addressing these challenges. By employing Mental Modeling, the research has characterized the influences of climate change on USACE environmental risk management, and aggregating recommendations from 28 experts. In addition, the study identifies the most important opportunities to improve organizational response to climate change, ranging from focused research and development of technical capabilities to broad paradigm shifts and systemic organizational improvements within the USACE environmental risk management programs. This study demonstrates that Mental Modeling is a useful tool for understanding complex problems, identifying gaps, and formulating strategies, and can be used by a multitude of organizations and agencies.     

Fisheries regulatory regimes and resilience to climate change

Climate change is already producing ecological, social, and economic impacts on fisheries, and these effects are expected to increase in frequency and magnitude in the future. Fisheries governance and regulations can alter socio-ecological resilience to climate change impacts via harvest control rules and incentives driving fisher behavior, yet there are no syntheses or conceptual frameworks for examining how institutions and their regulatory approaches can alter fisheries resilience to climate change. We identify nine key climate resilience criteria for fisheries socio-ecological systems (SES), defining resilience as the ability of the coupled system of interacting social and ecological components (i.e., the SES) to absorb change while avoiding transformation into a different undesirable state. We then evaluate the capacity of four fisheries regulatory systems that vary in their degree of property rights, including open access, limited entry, and two types of rights-based management, to increase or inhibit resilience. Our exploratory assessment of evidence in the literature suggests that these regulatory regimes vary widely in their ability to promote resilient fisheries, with rights-based approaches appearing to offer more resilience benefits in many cases, but detailed characteristics of the regulatory instruments are fundamental.     

Status and trends of terrestrial arthropod abundance and diversity in the North Atlantic region of the Arctic

Ambio - The Circumpolar Biodiversity Monitoring Programme (CBMP) provides an opportunity to improve our knowledge of Arctic arthropod diversity, but initial baseline studies are required to...     

Multimodel inference and adaptive management

Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study's inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.     

TRENDS IN PRECIPITATION AND STREAM CHEMISTRY IN A PRISTINE OLD-GROWTH FOREST WATERSHED,OLYMPIC NATIONAL PARK,WASHINGTON1

ABSTRACT: Human induced long-term changes in precipitation and stream chemistry have been observed in eastern North America and Europe, but few long-term studies have been conducted in coastal western North America. The objectives of this research were to determine: (1) time trends in precipitation and stream chemistry in a pristine old-growth forest watershed, and (2) seasonal patterns in precipitation and stream chemistry. It was conducted in 58 ha West Twin Creek Watershed, Hoh River Valley, Olympic National Park, Washington from 1984 to 1993. Vegetation consists of old-growth forest, with western hemlock, Douglas-fir, western redcedar, Pacific silver fir, and Sitka spruce being the dominant tree species. Annual precipitation varied from 2336 to 4518 mm during the study period with the majority of the rain falling between October and May. Chemistry of precipitation was strongly dominated by oceanic influences with Na and Cl being the dominant ions. The chemistry of the stream was influenced by bedrock weathering and was dominated by Ca, HCO₃, and SO₄ and was not strongly related to precipitation chemistry. The pH of precipitation averaged 5.3 over time and ranged from 4.3 to 7.1, while the stream pH averaged 7.5 and ranged from 5.5 to 9.0. There were few long-term trends in the chemical constituents of bulk precipitation or stream water with the exception of a slight decrease in NO₃ in precipitation and an increase of SO₄ in stream water. A trend of decreasing concentrations of Ca, Mg and Na in precipitation also occurred. There were no significant seasonal patterns in precipitation although the highest SO₄ concentrations usually occurred in late spring and summer perhaps due marine algal activity. Strong seasonal trends occurred in concentrations of HCO₃, SO₄, Ca, Mg, and Na in stream water resulting from weathering and stream flow patterns, with highest ion concentrations occurring just before the onset of the rainy season. Pulses of NO₃ in the stream were observed during fall and early winter resulting from the release of NO₃ which had accumulated in soils or sediments.