Tremblings, May 2016

Aspen is not a tree, it\u27s a root system (Anonymous) Annie DesRochers While trying to verify if the decline of aspen stands in western Canada was due to diseased root systems, we carried out large-scale excavations that allowed us to discover something of utmost importance for these forests: old parental roots were present in the majority of tree root systems, confirming their sucker-origin, and these roots still connected trees with each other through stand maturity. Moreover, trees that were not originally connected through their parental root had formed root grafts with each other, further increasing the level of interconnection between trees. We also noticed that these connecting roots were quite large and must have represented a large energy demand on the trees for their maintenance. One might guess that if they constituted too large of an energy sink, trees could simply shed them, but their large woody nature and central position make them a foundation of their root system, not easily abandoned

Using custom scientific workflow software and GIS to inform protected area climate adaptation planning in the Greater Yellowstone Ecosystem

Anticipating the ecological effects of climate change to inform natural resource climate adaptation planning represents one of the primary challenges of contemporary conservation science. Species distribution models have become a widely used tool to generate first-pass estimates of climate change impacts to species probabilities of occurrence. There are a number of technical challenges to constructing species distribution models that can be alleviated by the use of scientific workflow software. These challenges include data integration, visualization of modeled predictor–response relationships, and ensuring that models are reproducible and transferable in an adaptive natural resource management framework. We used freely available software called VisTrails Software for Assisted Habitat Modeling (VisTrails:SAHM) along with a novel ecohydrological predictor dataset and the latest Coupled Model Intercomparison Project 5 future climate projections to construct species distribution models for eight forest and shrub species in the Greater Yellowstone Ecosystem in the Northern Rocky Mountains USA. The species considered included multiple species of sagebrush and juniper, Pinus flexilis, Pinus contorta, Pseudotsuga menziesii, Populus tremuloides, Abies lasciocarpa, Picea engelmannii, and Pinus albicaulis. Current and future species probabilities of occurrence were mapped in a GIS by land ownership category to assess the feasibility of undertaking present and future management action. Results suggested that decreasing spring snowpack and increasing late-season soil moisture deficit will lead to deteriorating habitat area for mountain forest species and expansion of habitat area for sagebrush and juniper communities. Results were consistent across nine global climate models and two representative concentration pathway scenarios. For most forest species their projected future distributions moved up in elevation from general federal to federally restricted lands where active management is currently prohibited by agency policy. Though not yet fully mature, custom scientific workflow software shows considerable promise to ease many of the technical challenges inherent in modeling the potential ecological impacts of climate change to support climate adaptation planning

Using custom scientific workflow software and GIS to inform protected area climate adaptation planning in the Greater Yellowstone Ecosystem

Anticipating the ecological effects of climate change to inform natural resource climate adaptation planning represents one of the primary challenges of contemporary conservation science. Species distribution models have become a widely used tool to generate first-pass estimates of climate change impacts to species probabilities of occurrence. There are a number of technical challenges to constructing species distribution models that can be alleviated by the use of scientific workflow software. These challenges include data integration, visualization of modeled predictor–response relationships, and ensuring that models are reproducible and transferable in an adaptive natural resource management framework. We used freely available software called VisTrails Software for Assisted Habitat Modeling (VisTrails:SAHM) along with a novel ecohydrological predictor dataset and the latest Coupled Model Intercomparison Project 5 future climate projections to construct species distribution models for eight forest and shrub species in the Greater Yellowstone Ecosystem in the Northern Rocky Mountains USA. The species considered included multiple species of sagebrush and juniper, Pinus flexilis, Pinus contorta, Pseudotsuga menziesii, Populus tremuloides, Abies lasciocarpa, Picea engelmannii, and Pinus albicaulis. Current and future species probabilities of occurrence were mapped in a GIS by land ownership category to assess the feasibility of undertaking present and future management action. Results suggested that decreasing spring snowpack and increasing late-season soil moisture deficit will lead to deteriorating habitat area for mountain forest species and expansion of habitat area for sagebrush and juniper communities. Results were consistent across nine global climate models and two representative concentration pathway scenarios. For most forest species their projected future distributions moved up in elevation from general federal to federally restricted lands where active management is currently prohibited by agency policy. Though not yet fully mature, custom scientific workflow software shows considerable promise to ease many of the technical challenges inherent in modeling the potential ecological impacts of climate change to support climate adaptation planning

Quantifying the Indirect Effect of Wolves on Aspen in Northern Yellowstone National Park: Evidence for a Trophic Cascade?

Yellowstone National Park is renowned for its incredible wildlife, and perhaps the most famous of these species is the gray wolf, which was reintroduced to the Park in the mid-1990s. After reintroduction, it was highly publicized by scientists, journalists, and environmentalists that the wolf both decreased elk density and changed elk behavior in a way that reduced elk effects on plants, a process known as a “trophic cascade.” Aspen, which is eaten by elk in winter, is one species at the forefront of Yellowstone trophic cascade research because it has been in decline across the Park for over a century. However, due to the challenges of measuring trophic cascades, there is continued uncertainty regarding the effects of wolves on aspen in northern Yellowstone. Thus, the purpose of my dissertation was to provide a comprehensive test of a trophic cascade in this system. Specifically, I used 20 years of data on aspen, elk, and wolves in Yellowstone to: 1) clarify annual trends in browsing and height of young aspen (a proxy for regeneration) after wolf reintroduction, 2) assess the influence of wolves scaring elk on aspen (“trait-mediated indirect effects”), and 3) evaluate the effect of wolves killing elk on aspen (“density-mediated indirect effects”). My research suggests that wolves indirectly contributed to increased aspen over story recruitment following their reintroduction by helping to reduce the elk population size, but elk response to the risk of wolf predation did not reduce elk foraging in a way that measurably increased aspen recruitment. Additionally, hunter harvest of elk north of the park was twice as important as wolf predation in causing increased aspen recruitment. However, despite wolves and hunters limiting elk abundance, it is still uncommon for young aspen to grow past peak browsing height (120-cm), indicating that many stands remain vulnerable to elk herbivory nearly 30 years after wolf reintroduction. These results highlight that the strength and mechanism of predator effects on plant communities are context-specific. Thus, using predator reintroduction as a tool for ecosystem restoration without considering the many factors that shape trophic cascades may result in different management and conservation outcomes than intended

Razvoj modela za integrisano upravljanje izvorom mera prilagođavanja na klimatske promene na lokalnom nivou

In synergy with other socio-economic risks, the effects of climate change pose contemporary structural challenges that can not be considered only as an environmental issue. They affect the general development and therefore make the adaptive capacity of a population uncertain in the following decades. The subject of this dissertation comprises the development of a new decision support model for the selection of local level climate change adaptation measures. Considering the nature of management issues in climate policies, which involves decision-making under the conditions of uncertainty, the model employs adaptive management principles. It was designed to help decision-makers in selection of adequate adaptation measures, and to enable monitoring of the implementation process. The key objective of the research is fulfilled by developing a model for the selection of priority adaptation measures. The model is based on scenarios of the synergistic influence of diverse sets of measures on the observed system vulnerability. It takes into account climate projections and relevant biophysical and anthropogenic factors. The model relies on a combination of several methodological approaches. The scenario method was used for the selection of adaptation measures. It is based on the assessment of the simultaneous contribution of a group of measures to the reduction of vulnerability of the observed climate impact, by forming a conditional probability diagram using Bayesian networks. Through the analysis of the likelihood of diverse states of the observed group of criteria, it is possible to examine the effect of individual measures (or sets of measures) adaptation capacity, as a result of the joint probability distribution of all criteria in the network. The analytical hierarchical process (AHP) was used to quantify the distinct qualitative relationships between the risk criteria of the observed climate impact and the adaptation measures. A GIS is used to calculate the specific values of the criteria on the network, to profile the vulnerability, sensitivity, adaptation capacity and exposure index, as well as for data integration. The model can improve the decision-making in adaptation planning process. As the results are expressed as a probability distribution for each alternative, the model can help decision makers predict the chances of achieving desired effects of selected measures, and develop detailed programs at the local level to increase their efficiency. The model is also capable to transparently monitor the application process and facilitate the development of appropriate capacities for the purpose in local communities. In this respect, the developed model also provides a methodological contribution for improving the planning framework for the local adaptation project management