Managing for the unexpected: Building resilient forest landscapes to cope with global change

Natural disturbances exacerbated by novel climate regimes are increasing worldwide, threatening the ability of forest ecosystems to mitigate global warming through car-bon sequestration and to provide other key ecosystem services. One way to cope with unknown disturbance events is to promote the ecological resilience of the forest by increasing both functional trait and structural diversity and by fostering functional connectivity of the landscape to ensure a rapid and efficient self- reorganization of the system. We investigated how expected and unexpected variations in climate and biotic disturbances affect ecological resilience and carbon storage in a forested region in southeastern Canada. Using a process- based forest landscape model (LANDIS-II), we simulated ecosystem responses to climate change and insect outbreaks under dif-ferent forest policy scenarios—including a novel approach based on functional diver-sification and network analysis—and tested how the potentially most damaging insect pests interact with changes in forest composition and structure due to changing cli-mate and management. We found that climate warming, lengthening the vegetation season, will increase forest productivity and carbon storage, but unexpected impacts of drought and insect outbreaks will drastically reduce such variables. Generalist, non- native insects feeding on hardwood are the most damaging biotic agents for our region, and their monitoring and early detection should be a priority for forest au-thorities. Higher forest diversity driven by climate-smart management and fostered by climate change that promotes warm-adapted species, might increase disturbance severity. However, alternative forest policy scenarios led to a higher functional and structural diversity as well as functional connectivity—and thus to higher ecological resilience—than conventional management. Our results demonstrate that adopting a landscape-scale perspective by planning interventions strategically in space and adopting a functional trait approach to diversify forests is promising for enhancing ecological resilience under unexpected global change stressors.MM received funding from the Swiss National Science Foundation (grant n.175101) and the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie framework (grant n.891671, REINFORCE project). NA was supported by a Juan de la Cierva fellowship of the Spanish Ministry of Science and Innovation (FCJ2020-046387-I). This work has also been supported by funding to NA and MM from the Canada Research Chair in Forest Resilience to Global Changes attributed to CM. MJF acknowledges the support of the Canada Research Chair in Spatial Ecology

Forecasting species distributions : correlation does not equal causation

This research was funded by the U.S. Department of the Interior Northeast Climate Adaptation Science Center, which is managed by the U.S. Geological Survey National Climate Adaptation Science Center. Additional funding was provided by T-2- 3R grants for Nongame Species Monitoring and Management through the New Hampshire Fish and Game Department and E-1- 25 grants for Investigations and Population Recovery through the Vermont Fish and Wildlife Department.Aim Identifying the mechanisms influencing species' distributions is critical for accurate climate change forecasts. However, current approaches are limited by correlative models that cannot distinguish between direct and indirect effects. Location New Hampshire and Vermont, USA. Methods Using causal and correlational models and new theory on range limits, we compared current (2014?2019) and future (2080s) distributions of ecologically important mammalian carnivores and competitors along range limits in the northeastern US under two global climate models (GCMs) and a high-emission scenario (RCP8.5) of projected snow and forest biomass change. Results Our hypothesis that causal models of climate-mediated competition would result in different distribution predictions than correlational models, both in the current and future periods, was well-supported by our results; however, these patterns were prominent only for species pairs that exhibited strong interactions. The causal model predicted the current distribution of Canada lynx (Lynx canadensis) more accurately, likely because it incorporated the influence of competitive interactions mediated by snow with the closely related bobcat (Lynx rufus). Both modeling frameworks predicted an overall decline in lynx occurrence in the central high-elevation regions and increased occurrence in the northeastern region in the 2080s due to changes in land use that provided optimal habitat. However, these losses and gains were less substantial in the causal model due to the inclusion of an indirect buffering effect of snow on lynx. Main conclusions Our comparative analysis indicates that a causal framework, steeped in ecological theory, can be used to generate spatially explicit predictions of species distributions. This approach can be used to disentangle correlated predictors that have previously hampered understanding of range limits and species' response to climate change.Publisher PDFPeer reviewe

Beyond ecosystem modeling: a roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundationsof community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is key to meeting the pressing needs of science and society in the 21st century

Wildlife resistance and protection in a changing New England landscape.

Rapid changes in climate and land use threaten the persistence of wildlife species. Understanding where species are likely to occur now and in the future can help identify areas that are resistant to change over time and guide conservation planning. We estimated changes in species distribution patterns and spatial resistance in five future scenarios for the New England region of the northeastern United States. We present scenario-specific distribution change maps for nine harvested wildlife species, identifying regions of increasing, decreasing, or stable habitat suitability within each scenario. Next, we isolated areas where species occurrence probability is high (p > 0.7) and resistant to change across all future scenarios. Resistance was also evaluated relative to current land protection to identify patterns in and out of Protected Areas (PAs). Generally, species distributions declined in area over the 50-year assessment period (2010-2060), with the greatest average declines occurring for moose (-40.9%) and wild turkey (-22.1%). Species resistance varied considerably across the region, with coyote demonstrating the highest average regional resistance (91.81% of the region) and moose demonstrating the lowest (0.76% of the region). At the state level, average focal species resistance was highest in Maine (the largest state) and lowest in Massachusetts. Many of the focal species showed high overlap in resistance and land protection. Coyote, white-tailed deer, and black bear had the highest probability of resistance, given protection, while moose and wild turkey had the highest probability of protection, given resistance. Overall, relatively small portions of New England-ranging between 0.25% and 21.12%-were both protected and resistant for the focal species. Our results provide estimates of resistance that can inform conservation planning for commonly harvested species that are important ecologically, economically, and culturally to the region. Expanding protected area coverage to include resistant areas may provide longer term benefits to these species

Epigastric pain as presentation of an addisonian crisis in a patient with Schmidt syndrome.

A 39-year-old woman presented with a 10-day history of epigastric pain accompanied by persistent fatigue and loss of appetite for 3 months. She had presented several weeks earlier with adhesive capsulitis, treated by local infiltration of corticosteroids. She was not taking any other medications. Results of heart, lung, and abdominal examinations were unremarkable, except for mild epigastric tenderness. Purple stretch marks were observed on examination of the skin. The only blood chemistry abnormalities were hyponatremia (125 mEq/L) and hyperkalemia (6.8 mEq/L). Based on the clinical and biologic picture, adrenal insufficiency was suspected. The patient was transferred to the intensive care unit and received hydrocortisone intravenously for 3 days. She was then given oral hydrocortisone and fludrocortisone. Biologic abnormalities reversed entirely; the final diagnosis was primary autoimmune adrenal insufficiency (Addison's disease) associated with autoimmune hypothyroidism (Schmidt syndrome). Adrenal insufficiency should be considered in patients with abdominal pain, especially when associated with electrolyte abnormalities.Case ReportsJournal Articleinfo:eu-repo/semantics/publishe