Clarifying the role of the resist–accept–direct framework in supporting resource management planning processes
No abstract available.
Geology topics
Publications and source records attributed to John E. Gross.
No abstract available.
We more accurately locate the boundary between current practice and research priorities regarding climate change scenario planning in U.S. federal land management agencies by supplementing the characterization in a recent article (“Understanding perceptions of climate change scenario planning in United States public land management agencies”) of its use in the U.S. National Park Service. Accurately reflecting the full depth and breadth of efforts to streamline and mainstream the method for climate change adaptation planning in the U.S. National Park Service provides readers helpful guidance and resources called for by Murphy et al.
Resource managers have rarely accounted for evolutionary dynamics in the design or implementation of climate change adaptation strategies. We brought the research and management communities together to identify challenges and opportunities for applying evidence from evolutionary science to support on-the-ground actions intended to enhance species' evolutionary potential. We amalgamated input from natural-resource practitioners and interdisciplinary scientists to identify information needs, current knowledge that can fill those needs, and future avenues for research. Three focal areas that can guide engagement include: (1) recognizing when to act, (2) understanding the feasibility of assessing evolutionary potential, and (3) identifying best management practices. Although researchers commonly propose using molecular methods to estimate genetic diversity and gene flow as key indicators of evolutionary potential, we offer guidance on several additional attributes (and their proxies) that may also guide decision-making, particularly in the absence of genetic data. Finally, we outline existing decision-making frameworks that can help managers compare alternative strategies for supporting evolutionary potential, with the goal of increasing the effective use of evolutionary information, particularly for species of conservation concern. We caution, however, that arguing over nuance can generate confusion; instead, dedicating increased focus on a decision-relevant evidence base may better lend itself to climate adaptation actions.
Adaptive capacity (AC)—the ability of a species to cope with or accommodate climate change—is a critical determinant of species vulnerability. Using information on species’ AC in conservation planning is key to ensuring successful outcomes. We identified connections between a list of species’ attributes (e.g., traits, population metrics, and behaviors) that were recently proposed for assessing species’ AC and management actions that may enhance AC for species at risk of extinction. Management actions were identified based on evidence from the literature, a review of actions used in other climate adaptation guidance, and our collective experience in diverse fields of global-change ecology and climate adaptation. Selected management actions support the general AC pathways of persist in place or shift in space, in response to contemporary climate change. Some actions, such as genetic manipulations, can be used to directly alter the ability of species to cope with climate change, whereas other actions can indirectly enhance AC by addressing ecological or anthropogenic constraints on the expression of a species’ innate abilities to adapt. Ours is the first synthesis of potential management actions directly linked to AC. Focusing on AC attributes helps improve understanding of how and why aspects of climate are affecting organisms, as well as the mechanisms by which management interventions affect a species’ AC and climate change vulnerability. Adaptive-capacity-informed climate adaptation is needed to build connections among the causes of vulnerability, AC, and proposed management actions that can facilitate AC and reduce vulnerability in support of evolving conservation paradigms.
Earth is experiencing widespread ecological transformation in terrestrial, freshwater, and marine ecosystems that is attributable to directional environmental changes, especially intensifying climate change. To better steward ecosystems facing unprecedented and lasting change, a new management paradigm is forming, supported by a decision-oriented framework that presents three distinct management choices: resist, accept, or direct the ecological trajectory. To make these choices strategically, managers seek to understand the nature of the transformation that could occur if change is accepted while identifying opportunities to intervene to resist or direct change. In this article, we seek to inspire a research agenda for transformation science that is focused on ecological and social science and based on five central questions that align with the resist–accept–direct (RAD) framework. Development of transformation science is needed to apply the RAD framework and support natural resource management and conservation on our rapidly changing planet.
A robust method for characterizing the biophysical environment of terrestrial vegetation uses the relationship between Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD). These variables are usually estimated from a water balance model rather than measured directly and are often more representative of ecologically-significant changes than temperature or precipitation. We evaluate trends and spatial patterns in AET and CWD in the Continental United States (CONUS) during 1980–2019 using a gridded water balance model. The western US had linear regression slopes indicating increasing CWD and decreasing AET (drying), while the eastern US had generally opposite trends. When limits to plant performance characterized by AET and CWD are exceeded, vegetation assemblages change. Widespread increases in aridity throughout the west portends shifts in the distribution of plants limited by available moisture. A detailed look at Sequoia National Park illustrates the high degree of fine-scale spatial variability that exists across elevation and topographical gradients. Where such topographical and climatic diversity exists, appropriate use of our gridded data will require sub-setting to an appropriate area and analyzing according to categories of interest such as vegetation communities or across obvious physical gradients. Recent studies have successfully applied similar water balance models to fire risk and forest structure in both western and eastern U.S. forests, arid-land spring discharge, amphibian colonization and persistence in wetlands, whitebark pine mortality and establishment, and the distribution of arid-land grass species and landscape scale vegetation condition. Our gridded dataset is available free for public use. Our findings illustrate how a simple water balance model can identify important trends and patterns at site to regional scales. However, at finer scales, environmental heterogeneity is driving a range of responses that may not be simply characterized by a single trend.
Scenario planning has emerged as a widely used planning process for resource management in situations of consequential, irreducible uncertainty. Because it explicitly incorporates uncertainty, scenario planning is regularly employed in climate change adaptation. An early and essential step in developing scenarios is identifying “climate futures”—descriptions of the physical attributes of plausible future climates that could occur at a specific place and time. Divergent climate futures that describe the broadest possible range of plausible conditions support information needs of decision makers, including understanding the spectrum of potential resource responses to climate change, developing strategies robust to that range, avoiding highly consequential surprises, and averting maladaptation. Here, we discuss three approaches for generating climate futures: a Representative Concentration Pathway (RCP)-ensemble, a quadrant-average, and an individual-projection approach. All are designed to capture relevant uncertainty, but they differ in utility for different applications, complexity, and effort required to implement. Using an application from Big Bend National Park as an example of numerous similar efforts to develop climate futures for National Park Service applications over the past decade, we compare these approaches, focusing on their ability to capture among-projection divergence during early-, mid-, and late-twenty-first century periods to align with near-, mid-, and long-term planning efforts. The quadrant-average approach and especially the individual-projection approach captured a broader range of plausible future conditions than the RCP-ensemble approach, particularly in the near term. Therefore, the individual-projection approach supports decision makers seeking to understand the broadest potential characterization of future conditions. We discuss tradeoffs associated with different climate future approaches and highlight suitable applications.
There is an imperative for conservation practitioners to facilitate the ability of biodiversity to adapt to accelerating environmental change. Evolutionary biologists are well-positioned to inform the development of evidence-based management strategies that will effectively support the adaptive capacity of species and ecosystems. Conservation practitioners increasingly accept that management practices must change, but harbor concerns about how to apply recommended changes to their management contexts. Given the goodwill from both conservation practitioners and evolutionary biologists, we believe there is opportunity to accelerate the required changes by promoting closer collaboration between these two groups. We highlight how evolutionary biologists can harness lessons learned via the evidence-based conservation movement to make a substantive contribution to the development of effective conservation management practices. These lessons include: 1) recognising why practitioners do and do not use scientific evidence; 2) building an evidence base that will influence management decisions; 3) translating theory into a format that conservation practitioners can use to inform management practices; and 4) developing strategies for effective knowledge exchange. Although efforts will be required on both sides, we believe there are rewards for both practitioners and evolutionary biologists, not least of which is fostering practices that will help support the long-term persistence of species.
Assessing the vulnerability of species to climate change serves as the basis for climate‐adaptation planning and climate‐smart conservation, and typically involves an evaluation of exposure, sensitivity, and adaptive capacity (AC). AC is a species’ ability to cope with or adjust to changing climatic conditions, and is the least understood and most inconsistently applied of these three factors. We propose an attribute‐based framework for evaluating the AC of species, identifying two general classes of adaptive responses: “persist in place” and “shift in space”. Persist‐in‐place attributes enable species to survive in situ, whereas the shift‐in‐space response emphasizes attributes that facilitate tracking of suitable bioclimatic conditions. We provide guidance for assessing AC attributes and demonstrate the framework's application for species with disparate life histories. Results illustrate the broad utility of this generalized framework for informing adaptation planning and guiding species conservation in a rapidly changing climate.
Managing climate impacts to natural resources in protected areas can be hampered by lack of monitoring data, poor understanding of natural resource responses to climate, or lack of timely condition assessments that can inform management actions. Here we demonstrate the utility of water balance as a tool for understanding natural resource responses to climate by developing case studies focused on stream flow, vegetation production, and wildfire ignition at Great Sand Dunes National Park and Preserve (GSDNP), U.S.A. The efficacy of water balance to predict these responses stems from the explicit integration of climate with site conditions that modify the effects of climate. This in turn results in estimates of water availability, water use, and water need that are proximal drivers of aquatic and terrestrial natural resource conditions. The water balance model successfully forecasted stream flow (r 2 = 0.69, P < 0.001); determined the critical water needs for maintaining annual vegetation production in different vegetation types spanning a large environmental gradient (r 2 = 0.18–0.71); and predicted proportion of historic wildfire ignitions in forest (r 2 = 0.96–0.99) and non-forest (r 2 = 0.96–0.97) vegetation types. Collectively, these case studies demonstrate practical approaches to translate climate data into assessments of natural resource condition that inform long-term planning and near-term strategic actions needed for conservation of protected areas.
Many U.S. national parks are already at the extreme warm end of their historical temperature distributions. With rapidly warming conditions, park resource management will be enhanced by information on seasonality of climate that supports adjustments in the timing of activities such as treating invasive species, operating visitor facilities, and scheduling climate-related events (e.g., flower festivals and fall leaf-viewing). Seasonal changes in vegetation, such as pollen, seed, and fruit production, are important drivers of ecological processes in parks, and phenology has thus been identified as a key indicator for park monitoring. Phenology is also one of the most proximate biological responses to climate change. Here, we use estimates of start of spring based on climatically modeled dates of first leaf and first bloom derived from indicator plant species to evaluate the recent timing of spring onset (past 10–30 yr) in each U.S. natural resource park relative to its historical range of variability across the past 112 yr (1901–2012). Of the 276 high latitude to subtropical parks examined, spring is advancing in approximately three-quarters of parks (76%), and 53% of parks are experiencing “extreme” early springs that exceed 95% of historical conditions. Our results demonstrate how changes in climate seasonality are important for understanding ecological responses to climate change, and further how spatial variability in effects of climate change necessitates different approaches to management. We discuss how our results inform climate change adaptation challenges and opportunities facing parks, with implications for other protected areas, by exploring consequences for resource management and planning.
Most of the western United States is experiencing the effects of rapid and directional climate change (Garfin et al. 2013). These effects, along with forecasts of profound changes in the future, provide strong motivation for resource managers to learn about and prepare for future changes. Climate adaptation plans are based on an understanding of historic climate variation and their effects on ecosystems and on forecasts of future climate trends. Frameworks for climate adaptation thus universally identify the importance of a summary of historical, current, and projected climates (Glick, Stein, and Edelson 2011; Cross et al. 2013; Stein et al. 2014). Trends in physical climate variables are usually the basis for evaluating the exposure component in vulnerability assessments. Thus, this chapter focuses on step 2 of the Climate-Smart Conservation framework (chap. 2): vulnerability assessment. We present analyses of historical and current observations of temperature, precipitation, and other key climate measurements to provide context and a baseline for interpreting the ecological impacts of projected climate changes.
No abstract available.
Phenology is the study of periodic plant and animal life cycle events, how these are influenced by seasonal and interannual variations in climate, and how they modulate the abundance, diversity, and interactions of organisms. The USA National Phenology Network (USA-NPN) is currently being organized to engage federal agencies, environmental networks and field stations, educational institutions, and citizen scientists. The first USA-NPN planning workshop was held August 2005, in Tucson, Ariz. (Betancourt et al. [2005]; http://www.uwm.edu/Dept/Geography/npn/; by 1 June 2007, also see http://www.usanpn.org). With sponsorship from the U.S. National Science Foundation, the U.S. Geological Survey (USGS), the U.S. Fish and Wildlife Service, and NASA, the second USA-NPN planning workshop was held at the University of Wisconsin-Milwaukee on 10–12 October 2006 to (1) develop lists of target species and observation protocols; (2) identify existing networks that could comprise the backbone of nationwide observations by 2008; (3) develop opportunities for education, citizen science, and outreach beginning in spring 2007; (4) design strategies for implementing the remote sensing component of USA-NPN; and (5) draft a data management and cyberinfrastructure plan.
This e-book is the product of a second workshop that was funded and promoted by the United States Geological Survey to enhance cooperation between states for the management of chronic wasting disease (CWD). The first workshop addressed issues surrounding the statistical design and collection of surveillance data for CWD. The second workshop, from which this document arose, followed logically from the first workshop and focused on appropriate methods for analysis, interpretation, and use of CWD surveillance and related epidemiology data. Consequently, the emphasis of this e-book is on modeling approaches to describe and gain insight of the spatial epidemiology of CWD. We designed this e-book for wildlife managers and biologists who are responsible for the surveillance of CWD in their state or agency. We chose spatial methods that are popular or common in the spatial epidemiology literature and evaluated them for their relevance to modeling CWD. Our opinion of the usefulness and relevance of each method was based on the type of field data commonly collected as part of CWD surveillance programs and what we know about CWD biology, ecology, and epidemiology. Specifically, we expected the field data to consist primarily of the infection status of a harvested or culled sample along with its date of collection (not date of infection), location, and demographic status. We evaluated methods in light of the fact that CWD does not appear to spread rapidly through wild populations, relative to more highly contagious viruses, and can be spread directly from animal to animal or indirectly through environmental contamination. We discovered that many of the wellpublished methods were developed for fast-spreading human diseases, such as influenza and measles. While these methods are applicable to fast spreading wildlife diseases, such as foot-and-mouth disease or West Nile virus, many are not likely to work well for CWD. Only limited data exist to evaluate geographic and spatial spread because many locations where we find CWD tend to be locations where samples have just been taken or sample sizes have just become large enough to have a high probability of detecting a low prevalence. Consequently, methods that work well to describe or predict the spread of foot-and-mouth disease throughout England, which occurred within a year, do not work well for describing or predicting CWD spread. We did not exclude methods that we regarded as inappropriate; rather, we included methods that are commonly used for disease epidemiology and then discussed their applicability for modeling the spatial epidemiology of CWD. We hope including inappropriate methods with an explanation of why they are ill-suited for CWD will make it easier to drop them from consideration and explain to others why they were not recommended for spatial modeling of CWD. We organized the three chapters by scale and extent for which each method was developed or best suited. The first chapter covers methods appropriate to multi-jurisdictional or multi-state modeling, which we call “regional” scale. The second chapter covers methods appropriate for within state areas such as wildlife management units or metapopulations, which we call “landscape” scale. The third chapter covers methods appropriate for population or individual-based modeling, which we call “fine” scale. We know this rubric is somewhat artificial because many methods work at multiple scales. We hope, however, that this structure addresses some of the challenges faced by managers that work at local, regional, state, and national scales. Further, the resolution of empirical data often changes with spatial scale, which affects the utility of different modeling approaches. For example, individual-based models work best at modeling spread within populations, while risk analysis is most useful for summarizing data over larger scales such as a region. Because some methods are applicable at several scales, however, we included a graphic at the beginning of each method that indicates the range of scales for which it applies. For example, the graphic to the right indicates that the method is most applicable for regional-scale modeling. There is also a question of resolution as well as scale and extent for each method. CWD surveillance data have been collected over large areas, such as a wildlife management unit or state, but the resolution of the data may be fine scale with GPS locations for many samples. For each method, we described the required resolution of the data and describe the type of data required, as well as what questions the method could answer and how useful the method is, given typical CWD data. For each scale, we presented a focal approach that would be useful for understanding the spatial pattern and epidemiology of CWD, as well as being a useful tool for CWD management. The focal approaches include risk analysis and micromaps for the regional scale, cluster analysis for the landscape scale, and individual based modeling for the fine scale of within population. For each of these methods, we used simulated data and walked through the method step by step to fully illustrate the “how to”, with specifics about what is input and output, as well as what questions the method addresses. We also provided a summary table to, at a glance, describe the scale, questions that can be addressed, and general data required for each method described in this e-book. We hope that this review will be helpful to biologists and managers by increasing the utility of their surveillance data, and ultimately be useful for increasing our understanding of CWD and allowing wildlife biologists and managers to move beyond retroactive fire-fighting to proactive preventative action.