Southeast Climate Adaptation Science Center

Refugia have long been studied from paleontological and biogeographical perspectives to understand how populations persisted during past periods of unfavorable climate. Recently, researchers have applied the idea to contemporary landscapes to identify climate change refugia, here defined as areas relatively buffered from contemporary climate change over time that enable persistence of valued physical, ecological, and socio-cultural resources. We differentiate historical and contemporary views, and characterize physical and ecological processes that create and maintain climate change refugia. We then delineate how refugia can fit into existing decision support frameworks for climate adaptation and describe seven steps for managing them. Finally, we identify challenges and opportunities for operationalizing the concept of climate change refugia. Managing climate change refugia can be an important option for conservation in the face of ongoing climate change.

Although ecological forces are known to shape the expression of sociality across a broad range of biological taxa, their role in shaping human behavior is currently disputed. Both comparative and experimental evidence indicate that beliefs in moralizing high gods promote cooperation among humans, a behavioral attribute known to correlate with environmental harshness in nonhuman animals. Here we combine fine-grained bioclimatic data with the latest statistical tools from ecology and the social sciences to evaluate the potential effects of environmental forces, language history, and culture on the global distribution of belief in moralizing high gods (n = 583 societies). After simultaneously accounting for potential nonindependence among societies because of shared ancestry and cultural diffusion, we find that these beliefs are more prevalent among societies that inhabit poorer environments and are more prone to ecological duress. In addition, we find that these beliefs are more likely in politically complex societies that recognize rights to movable property. Overall, our multimodel inference approach predicts the global distribution of beliefs in moralizing high gods with an accuracy of 91%, and estimates the relative importance of different potential mechanisms by which this spatial pattern may have arisen. The emerging picture is neither one of pure cultural transmission nor of simple ecological determinism, but rather a complex mixture of social, cultural, and environmental influences. Our methods and findings provide a blueprint for how the increasing wealth of ecological, linguistic, and historical data can be leveraged to understand the forces that have shaped the behavior of our own species.

Recent technological and methodological advances have revolutionized wildlife monitoring. Although most biodiversity monitoring initiatives are geared towards focal species of conservation concern, researchers are increasingly studying entire communities, specifically the spatiotemporal drivers of community size and structure and interactions among species. This has resulted in the emergence of multi‐species occupancy models (MSOMs) as a promising and efficient approach for the study of community ecology. Given the potential of MSOMs for conservation and management action, it is critical to know whether study design and model assumptions are consistent with inference objectives. This is especially true for studies that are designed for a focal species but can give insights about a community. Here, we review the recent literature on MSOMs, identify areas of improvement in the multi‐species study workflow, and provide a reference model for best practices for focal species and community monitoring study design. We reviewed 92 studies published between 2009 and early 2018, spanning 27 countries and a variety of taxa. There is a consistent under‐reporting of details that are central to determining the adequacy of designs for generating data that can be used to make inferences about community‐level patterns of occupancy, including the spatial and temporal extent, types of detectors used, covariates considered, and choice of field methods and statistical tools. This reporting bias could consequently result in skewed estimates, affecting conservation actions and management plans. On the other hand, comprehensive reporting is likely to help researchers working on MSOMs assess the robustness of inferences, in addition to making strides in terms of reproducibility and reusability of data. We use our literature review to inform a roadmap with best practices for MSOM studies, from simulations to design considerations and reporting, for the collection of new data as well as those involving existing datasets.

The influence of climate on forest change during the past century in the eastern United States was evaluated in a recent paper (Nowacki & Abrams, 2014) that centers on an increase in ‘highly competitive mesophytic hardwoods’ (Nowacki & Abrams, 2008) and a concomitant decrease in the more xerophytic Quercus species. Nowacki & Abrams (2014) concluded that climate change has not contributed significantly to observed changes in forest composition. However, the authors restrict their focus to a single element of climate: increasing temperature since the end of the Little Ice Age ca. 150 years ago. In their study, species were binned into four classifications (e.g., Acer saccharum – ‘cool-adapted’, Acer rubrum – ‘warm-adapted’) based on average annual temperature within each species range in the United States, reducing the multifaceted character of climate into a single, categorical measure. The broad temperature classes not only veil the many biologically relevant aspects of temperature (e.g., seasonal and extreme temperatures) but they may also mask other influences, both climatic (e.g., moisture sensitivity) and nonclimatic (e.g., competition). Understanding the primary drivers of forest change is critically important. However, using annual temperature reduces the broad spectrum of climatic influence on forests (e.g., Jackson & Overpeck, 2000; Jackson et al., 2009) to a single variable. Tsuga canadensis illustrates one example of the complex interaction between trees and temperature. In the southern part of its range, Tsuga canadensis growth is weakly, but positively correlated with early growing-season temperature. However, this relationship becomes stronger and shifts to later in the season toward the northern part of its range (Cook & Cole, 1991). Moreover, Tsuga canadensis growth is significantly and negatively correlated with just May temperatures during the current growing season in the northeastern United States (Cook, 1991; Cook & Cole, 1991; Vaganov et al., 2011), while in the southeastern United States it is strongly and negatively correlated with summer (June–August) temperatures (Hart et al., 2010). Trees can also be sensitive to diverse and often interacting climate variables at various stages of their life cycles (Jackson et al., 2009). Interactions between precipitation and temperature are clearly important (Harsch & Hille Ris Lambers, 2014; Martin-Benito & Pederson, accepted), and often lead to counterintuitive responses. For example, some plant species that would have been expected to move north and upslope with increasing temperature have in fact moved south during periods of warming, both recently and in the Holocene (Webb, 1986; Jackson & Overpeck, 2000; Crimmins et al., 2011; Harsch & Hille Ris Lambers, 2014). We argue here that moisture availability has strongly influenced forest dynamics and suggest that elimination of climate as a driver of recent forest change in eastern North America is premature. Important to this discussion is the fact that our current reference point, the late 20th century, is among the wettest periods since 1500 CE over much of the eastern United States (Pederson et al., 2013) (Fig. 1). Multiple lines of evidence indicate that moisture availability has been and continues to be a critical factor in forest dynamics of eastern North America. Early growing-season moisture availability is critical for seedling germination and establishment, particularly for fall-dispersed species, with spring drought events often filtering species based on germination phenology (De Steven, 1991). Mature trees can persist in the canopy for decades to centuries in the face of significant temperature increases, inhibiting replacement by other trees and imparting substantial inertia (Davis & Botkin, 1985; Loehle, 2000). Severe and repeated drought has been shown to increase tree mortality and open the canopy (Clinton et al., 1993; Parshall, 1995; Pedersen, 1998; Jackson & Booth, 2002; Klos et al., 2009; Shuman et al., 2009; Booth et al., 2012; Cavin et al., 2013; Pederson et al., 2014). Responses of mesic forests to changes in effective moisture span multiple time scales. For example, dendroecological and forest inventory data reveal tree growth and forest compositional responses from years to decades (e.g., Pederson et al., 2012; Gustafson & Sturtevant, 2013), and parallel trends in Holocene water-level and pollen records reveal that forest composition closely tracked effective moisture changes over centuries to millennia (e.g., Booth et al., 2012; Marsicek et al., 2013). The similarities of findings across time scales support the importance of moisture as a control on forest processes whether they apply over decades or millennia. Drought-induced mortality creates opportunities for canopy accession by understory trees, including species that were not canopy dominants before the drought. For canopy trees, moisture is widely documented as an important control of tree growth (Davis, 1912; Douglass, 1920; Lyon, 1936; Fritts, 1962; Cook, 1991; Stahle & Cleaveland, 1992; Orwig & Abrams, 1997; Rubino & McCarthy, 2000; Tardif et al., 2006; Kardol et al., 2010; Leblanc & Terrell, 2011; Anning et al., 2013; Brzostek et al., 2014; Clark et al., 2014; Voelker et al., 2014). Even trees in mesic settings show growth responses to moisture variability at interannual to decadal timescales over the last 200 years (Pederson et al., 2012). For mesophytes like Acer rubrum or Liriodendron tulipifera, growing-season moisture is the most important climatic driver of growth (Hart et al., 2012; Martin-Benito & Pederson, accepted). In comparison to Quercus, growing-season moisture is generally more important for the growth of mesophytic species (Pederson et al., 2013; Brzostek et al., 2014; Clark et al., 2014; Maxwell et al., 2014; Martin-Benito & Pederson, accepted with minor revision). Moisture may be the strongest climate-related driver of forest dynamics not only in eastern North America, but in most regions of the globe (Allen et al., 2010). In their evaluation of forest change in the transition from the Little Ice Age to the present, Nowacki & Abrams (2014) focus exclusively on an inferred increase in annual temperature. However, multiple paleoclimatic records indicate an increase in moisture availability during this same transition that could be as ecologically important as warming (Stahle et al., 1988, 2013; Stahle & Cleaveland, 1992; Cook et al., 2010; Hubeny et al., 2011; McEwan et al., 2011; Pederson et al., 2013; Newby et al., 2014). The long-term trend of increased moisture has persisted to the present in most areas (Fig. 1b); for example, regional-scale water table levels in the northeastern United States are at their highest since the 1950s (Weider & Boutt, 2010). The North American Drought Atlas (Cook & Krusic, 2004) shows that 1930–2005 is one of the wettest periods since 1500 CE over much of the eastern United States (Fig. 1a). The frequency of moderately to extremely wet years (PDSI value ≥ 2) is unusually high during this 75 year period despite significant droughts in the central region (1930s, 1950s, and 1980s), the 1960s drought in the Northeast, and recent drying in the Southeast (Fig. 1b). A long-term, broad-scale increase in moisture should favor species with physiological affinities for moisture. Indeed, many of the traits used to characterize the fire sensitivity of mesophytic species are traits that make them vulnerable to drought (Abrams, 1990, 1996; Bond & Midgley, 2001; Hallik et al., 2009). Liriodendron tulipifera experienced higher mortality than Quercus during the short, but severe 1980s drought in the southeastern United States (Elliott & Swank, 1994). Conversely, the strong response of mesophytic species to moisture would confer a competitive advantage over Quercus during times of sufficient moisture. Nowacki & Abrams (2014) assert that global-change forecasts largely predict reduction and contraction of mesophytic species and increase and expansion of drought-tolerant species and that so far, observed trends are opposite. They also identify the need for such models to include better ecophysiological requirements and disturbance to improve their predictive power and relevance. We agree on the latter count, and note that many such improvements are already being implemented (Iverson et al., 2011; Matthews et al., 2011; Xu et al., 2012; Gustafson & Sturtevant, 2013; Brandt et al., 2014). In addition, Gustafson & Sturtevant (2013) find that drought-induced mortality can be detected in the region from forest inventory data. Other considerations are required for the lack of predicted habitat loss for mesophytic species. First, the southeastern United States has experienced little warming outside of the cool season (Melillo et al., 2014). In fact, temperatures from 1971 to 2000 during the growing season were cooler vs. 1911–1940 over a most of the eastern United States (fig. 3 in McEwan et al., 2011). If warming had occurred during the growing season, we might have expected greater change in the Southeast because the growth of broadleaf species are more limited by high summer temperatures than populations to the north (Martin-Benito & Pederson, accepted). Warmer winters and a lack of warming during the growing season would have likely benefited, not aggravated, the growth of mesophytic species in the southern portion of the eastern United States (Martin-Benito & Pederson, accepted). Second, physiological drought and extreme events are projected to become increasingly frequent and severe across the eastern United States by middle of the 21st century (Melillo et al., 2014). Third, these projected droughts and extreme events have been largely absent since the 1930s (Fig. 1). Finally, the long-lived nature of trees ensures that even as climate is expected to shift to favor drought-tolerant species (Melillo et al., 2014), large-scale changes will be delayed in the absence of major disturbance events. Therefore, conditions promoting an increase in drought-tolerant species may eventually overtake the increase in mesophytic species, but it might not occur until later in the 21st century. Modeling responses of mesophytic species to future droughts is challenging given that many calibrations are based on observations during one of the wettest periods of the past several centuries (Fig. 1a). Regardless, it is important to include moisture in analyses of past, current, and future trends in vegetation composition. Forest dynamics in a changing climate will be influenced by multiple interacting factors (McEwan et al., 2011). We agree with Nowacki & Abrams (2014) that altered disturbance regimes, largely instituted by humans, have been an important driver of compositional change in eastern forests (cf. Foster & Aber, 2004), even predominating in the century following land clearance and agricultural abandonment. Changes in land use and moisture are both necessary to explain past and ongoing changes, but neither is independently sufficient. Given the varied influences of temperature, it is premature to rule it out as an influence for past changes, and it will certainly play a role in the future as growing-season temperature increases impart moisture stress to trees, from seedlings to adults. Humans are altering forests in an environment of changing temperature, precipitation, and natural disturbance regimes, and these, in turn, are interacting with newly arriving or spreading pests and pathogens. A multivariate approach that includes quantitative measures and examines interactions across multiple scales should aid understanding of the past and future evolution of forests. Future analyses of climate as a driver of forest change should include a spectrum of ecologically meaningful and independent measures of climate variation that are relevant to the establishment, growth, and mortality of trees. Comments by an anonymous reviewer and Craig Allen improved our letter.

Winter is an understudied but key period for the socioecological systems of northeastern North American forests. A growing awareness of the importance of the winter season to forest ecosystems and surrounding communities has inspired several decades of research, both across the northern forest and at other mid- and high-latitude ecosystems around the globe. Despite these efforts, we lack a synthetic understanding of how winter climate change may impact hydrological and biogeochemical processes and the social and economic activities they support. Here, we take advantage of 100 years of meteorological observations across the northern forest region of the northeastern United States and eastern Canada to develop a suite of indicators that enable a cross-cutting understanding of (1) how winter temperatures and snow cover have been changing and (2) how these shifts may impact both ecosystems and surrounding human communities. We show that cold and snow covered conditions have generally decreased over the past 100 years. These trends suggest positive outcomes for tree health as related to reduced fine root mortality and nutrient loss associated with winter frost but negative outcomes as related to the northward advancement and proliferation of forest insect pests. In addition to effects on vegetation, reductions in cold temperatures and snow cover are likely to have negative impacts on the ecology of the northern forest through impacts on water, soils, and wildlife. The overall loss of coldness and snow cover may also have negative consequences for logging and forest products, vector-borne diseases, and human health, recreation, and tourism, and cultural practices, which together represent important social and economic dimensions for the northern forest region. These findings advance our understanding of how our changing winters may transform the socioecological system of a region that has been defined by the contrasting rhythm of the seasons. Our research also identifies a trajectory of change that informs our expectations for the future as the climate continues to warm.

Novel forms of drought are emerging globally, due to climate change, shifting teleconnection patterns, expanding human water use, and a history of human influence on the environment that increases the probability of transformational ecological impacts. These costly ecological impacts cascade to human communities, and understanding this changing drought landscape is one of today's grand challenges. By using a modified horizon-scanning approach that integrated scientists, managers, and decision-makers, we identified the emerging issues in ecological drought that represent key challenges to timely and effective responses. Here we review the themes that most urgently need attention, including novel drought conditions, the potential for transformational drought impacts, and the need for anticipatory drought management. This horizon scan and review provides a roadmap to facilitate the research and management innovations that will support forward-looking, co-developed approaches to reduce the risk of drought to our socio-ecological systems during the 21st century. We used a modified horizon-scanning approach that brought together scientists, managers, and decision-makers to identify the emerging issues around the ecological impacts from drought that represent key challenges to effective response. We found three broad themes within ecological drought that need attention, including novel drought conditions, transformational drought impacts, and anticipatory drought management. This horizon scan and integrated review provides a roadmap to inspire the needed research and management innovations to reduce the risk of 21st century droughts.

Globally increasing wildfires have been attributed to anthropogenic climate change. However, providing decision makers with a clear understanding of how future planetary warming could affect fire regimes is complicated by confounding land use factors that influence wildfire and by uncertainty associated with model simulations of climate change. We use an ensemble of statistically downscaled Global Climate Models in combination with the Physical Chemistry Fire Frequency Model (PC2FM) to project changing potential fire probabilities in the conterminous United States for two scenarios representing lower (RCP 4.5) and higher (RCP 8.5) greenhouse gas emission futures. PC2FM is a physically-based and scale-independent model that predicts mean fire return intervals from both fire reactant and reaction variables, which are largely dependent on a locale's climate. Our results overwhelmingly depict increasing potential fire probabilities across the conterminous US for both climate scenarios. The primary mechanism for the projected increases is rising temperatures, reflecting changes in the chemical reaction environment commensurate with enhanced photosynthetic rates and available thermal molecular energy. Existing high risk areas, such as the Cascade Range and the Coastal California Mountains, are projected to experience greater annual fire occurrence probabilities, with relative increases of 122% and 67%, respectively, under RCP 8.5 compared to increases of 63% and 38% under RCP 4.5. Regions not currently associated with frequently occurring wildfires, such as New England and the Great Lakes, are projected to experience a doubling of occurrence probabilities by 2100 under RCP 8.5. This high resolution, continental-scale modeling study of climate change impacts on potential fire probability accounts for shifting background environmental conditions across regions that will interact with topographic drivers to significantly alter future fire probabilities. The ensemble modeling approach presents a useful planning tool for mitigation and adaptation strategies in regions of increasing wildfire risk.

Abstract Floods are the leading cause of natural disaster damages in the United States, with billions of dollars incurred every year in the form of government payouts, property damages, and agricultural losses. The Federal Emergency Management Agency oversees the delineation of floodplains to mitigate damages, but disparities exist between locations designated as high risk and where flood damages occur due to land use and climate changes and incomplete floodplain mapping. We harnessed publicly available geospatial datasets and random forest algorithms to analyze the spatial distribution and underlying drivers of flood damage probability (FDP) caused by excessive rainfall and overflowing water bodies across the conterminous United States. From this, we produced the first spatially complete map of FDP for the nation, along with spatially explicit standard errors for four selected cities. We trained models using the locations of historical reported flood damage events ( n = 71 434) and a suite of geospatial predictors (e.g. flood severity, climate, socio-economic exposure, topographic variables, soil properties, and hydrologic characteristics). We developed independent models for each hydrologic unit code level 2 watershed and generated a FDP for each 100 m pixel. Our model classified damage or no damage with an average area under the curve accuracy of 0.75; however, model performance varied by environmental conditions, with certain land cover classes (e.g. forest) resulting in higher error rates than others (e.g. wetlands). Our results identified FDP hotspots across multiple spatial and regional scales, with high probabilities common in both inland and coastal regions. The highest flood damage probabilities tended to be in areas of low elevation, in close proximity to streams, with extreme precipitation, and with high urban road density. Given rapid environmental changes, our study demonstrates an efficient approach for updating FDP estimates across the nation.

Abstract Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO 2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this ‘hot model’ problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble. Taking advantage of multiple lines of evidence used to construct the best estimate of the earth’s climate sensitivity, the Bayesian Model Averaging framework produces an unbiased posterior probability distribution of model weights. The updated multi-model ensemble projects end-of-century global mean surface temperature increases of 2 o C for a low emissions scenario (SSP1-2.6) and 5 o C for a high emissions scenario (SSP5-8.5). These estimates are lower than those produced using a simple multi-model mean for the CMIP6 ensemble. The results are also similar to results from a model culling approach, but retain some weight on low-probability models, allowing for consideration of the possibility that the true value could lie at the extremes of the assessed distribution. Our results showcase Bayesian Model Averaging as a path forward to project future climate change that is commensurate with the available scientific evidence.

Presently coastal areas globally are becoming unviable, with people no longer able to maintain livelihoods and settlements due to, for example, increasing floods, storm surges, coastal erosion, and sea level rise, yet there exist significant policy obstacles and practical and regulatory challenges to community-led and community-wide responses. For many receiving support only at the individual level for relocation or other adaptive responses, individual and community harm is perpetuated through the loss of culture and identity incurred through forced assimilation policies. Often, challenges dealt to frontline communities are founded on centuries of injustices. Can these challenges of both norms and policies be addressed? Can we develop socially, culturally, environmentally, and economically just sustainable adaptation processes that supports community responses, maintenance and evolution of traditions, and rejuvenates regenerative life-supporting ecosystems? This article brings together Indigenous community leaders, knowledge-holders, and allied collaborators from Louisiana, Hawai'i, Alaska, Borikén/Puerto Rico, and the Marshall Islands, to share their stories and lived experiences of the relocation and other adaptive challenges in their homelands and territories, the obstacles posed by the state or regional governments in community adaptation efforts, ideas for transforming the research paradigm from expecting communities to answer scientific questions to having scientists address community priorities, and the healing processes that communities are employing. The contributors are connected through the Rising Voices Center for Indigenous and Earth Sciences, which brings together Indigenous, tribal, and community leaders, atmospheric, social, biological, and ecological scientists, students, educators, and other experts, and facilitates intercultural, relational-based approaches for understanding and adapting to extreme weather and climate events, climate variability, and climate change.

Despite its successes, the U.S. Endangered Species Act (ESA) has proven challenging to implement due to funding limitations, workload backlog, and other problems. As threats to species survival intensify and as more species come under threat, the need for the ESA and similar conservation laws and policies in other countries to function efficiently has grown. Attempts by the U.S. Fish and Wildlife Service (USFWS) to streamline ESA decisions include multispecies recovery plans and habitat conservation plans. We address species status assessment (SSA), a USFWS process to inform ESA decisions from listing to recovery, within the context of multispecies and ecosystem planning. Although existing SSAs have a single-species focus, ecosystem-based research can efficiently inform multiple SSAs within a region and provide a foundation for transition to multispecies SSAs in the future. We considered at-risk grassland species and ecosystems within the southeastern United States, where a disproportionate number of rare and endemic species are associated with grasslands. To initiate our ecosystem-based approach, we used a combined literature-based and structured World Café workshop format to identify science needs for SSAs. Discussions concentrated on 5 categories of threats to grassland species and ecosystems, consistent with recommendations to make shared threats a focus of planning under the ESA: (1) habitat loss, fragmentation, and disruption of functional connectivity; (2) climate change; (3) altered disturbance regimes; (4) invasive species; and (5) localized impacts. For each threat, workshop participants identified science and information needs, including database availability, research priorities, and modeling and mapping needs. Grouping species by habitat and shared threats can make the SSA process and other planning processes for conservation of at-risk species worldwide more efficient and useful. We found a combination of literature review and structured discussion effective for identifying the scientific information and analysis needed to support the development of multiple SSAs. Article impact statement: Species status assessments can be improved by an ecosystem-based approach that groups imperiled species by shared habitats and threats.

Johnson, F. A., M. J. Eaton, J. Mikels-Carrasco, and D. Case. 2020. Building adaptive capacity in a coastal region experiencing global change. Ecology and Society 25(3):9. https://doi.org/10.5751/ES-11700-250309

ABSTRACT Many tropical islands have limited water resources with historically increasing demand, all potentially affected by a changing climate. The effects of climate change on island hydrology are difficult to model due to steep local precipitation gradients and sparse data. This work uses 10 statistically downscaled general circulation models ( GCMs ) under two greenhouse gas emission scenarios to evaluate the uncertainty propagated from GCMs in projecting the effects of climate change on water resources in a tropical island system. The assessment is conducted using a previously configured hydrologic model, the Precipitation Runoff Modelling System ( PRMS ) for Puerto Rico. Projected climate data and their modelled hydrologic variables versus historical measurements and their modelled hydrologic variables are found to have empirical distribution functions that are statistically different with less than 1 year of daily data aggregation. Thus, only annual averages of the projected hydrologic variables are employed as completely bias‐corrected model outputs. The magnitude of the projected total flow decreases in the four regions covering Puerto Rico, but with a large range of uncertainty depending on the makeup of the GCM ensemble. The multi‐model mean projected total flow decreases by 49–88% of historical amounts from the 1960s to the 2090s for the high emissions scenarios and by 39–79% for the low emissions scenarios. Subsurface flow contributions decreased the least and groundwater flow contributions decreased the most across the island. At locations critical to water supply for human use, projected streamflow is shown to decrease substantially below projected withdrawals by 2099.

Abstract Climate change is altering the spatial distribution of many species around the world. In response, we need to identify and protect suitable areas for a large proportion of the fauna so that they persist through time. This exercise must also evaluate the ability of existing protected areas to provide safe havens for species in the context of climate change. Here, we combined passive acoustic monitoring, semi-automatic species identification models, and species distribution models of 21 bird and frog species based on past (1980–1989), present (2005–2014), and future (2040–2060) climate scenarios to determine how species distributions relate to the current distribution of protected areas in Puerto Rico. Species detection/non-detection data were acquired across ~ 700 sampling sites. We developed always-suitable maps that characterized suitable habitats in all three time periods for each species and overlaid these maps to identify regions with high species co-occurrence. These distributions were then compared with the distribution of existing protected areas. We show that Puerto Rico is projected to become dryer by 2040–2060, and precipitation in the warmest quarter was among the most important variables affecting bird and frog distributions. A large portion of always-suitable areas (ASA) is outside of protected areas (> 80%), and the percent of protected areas that overlaps with always-suitable areas is larger for bird (75%) than frog (39%) species. Our results indicate that present protected areas will not suffice to safeguard bird and frog species under climate change; however, the establishment of larger protected areas, buffer zones, and connectivity between protected areas may allow species to find suitable niches to withstand environmental changes.

Prioritizing climate adaptation actions is often made difficult by stakeholders and decision-makers having multiple objectives, some of which may be competing. Transparent, transferable, and objective methods are needed to assess and weight different objectives for complex decisions with multiple interests. In this study, the Analytic Hierarchy Process (AHP) was used to examine priorities in managing cultural resources in the face of climate change at Cape Lookout National Seashore on the Atlantic coast of the southeastern United States. In this process, we conducted facilitated discussion sessions with the selected stakeholder representatives to elicit a comprehensive list of management objectives. Objectives were then merged into three categories: 1) Maximizing Historic Character and Condition Retention (HCC); 2) Fostering Heritage Awareness (HA); and 3) Maximizing Financial Benefits (FB). We facilitated two AHP exercise sessions, both individually and in groups, to seek consensus on the relative importance of the objectives. The AHP process created a space for stakeholders (government agencies and local citizens) to consider and present arguments that we used to contextualize their trade-offs between the objectives. The stakeholders' top priority was to maximize the HCC. This objective was prioritized more than HA and FB in the individual trade-off choices, while HA was given nearly equal priority to FB. The consensus priority vectors of two management objectives (HCC and HA) differ significantly from FB, but the difference between HCC and HA is slight and not statistically different. FB and HA had larger changes in consensus priority vectors among the three objectives relative to individual priority vectors. For HCC, the difference between individual and consensus priority vectors was the smallest and nearly equal. Moreover, very high levels of consistency were found in consensus priority trade-off discussions and AHP application. Our research highlights the advantage of using a two-step AHP process in climate adaptation planning of vulnerable resources to enhance robustness in decision making. Coupling this approach with future efforts to develop management priorities would help estimate indices to determine the order in which adaptation treatments are applied to vulnerable cultural resources.

Predicting how species respond to changes in climate is critical to conserving biodiversity. Modeling efforts to date have largely centered on predicting the effects of warming temperatures on temperate species phenology. In and near the tropics, the effects of a warming planet on species phenology are more likely to be driven by changes in the seasonal precipitation cycle rather than temperature. To demonstrate the importance of considering precipitation-driven phenology in ecological studies, we present a case study wherein we construct a mechanistic population model for a rare subtropical butterfly (Miami blue butterfly, Cyclargus thomasi bethunebakeri) and use a suite of global climate models to project butterfly populations into the future. Across all iterations of the model, the trajectory of Miami blue populations is uncertain. We identify both biological uncertainty (unknown diapause survival rate) and climate uncertainty (ambiguity in the sign of precipitation change across climate models), and their interaction as key factors that determine persistence vs. extinction. Despite uncertainty, the most optimistic iteration of the model predicts that Miami blue butterfly populations will decline under the higher emissions scenario (RCP 8.5). The lack of climate model agreement across the projection ensemble suggests that investigations into the effect of climate change on precipitation-driven phenology require a higher level of rigor in the uncertainty analysis compared to analogous studies of temperature. For tropical species, a mechanistic approach that incorporates both biological and climate uncertainty is the best path forward to understand the effect shifting precipitation regimes have on phenology and population dynamics.

Abstract Background Projected trajectories of climate and land use change over the remainder of the twenty-first century may result in conditions and situations that require flexible approaches to conservation planning and practices. For example, prescribed burning is a widely used management tool for promoting longer-term resilience and sustainability in longleaf pine ecosystems of the southeastern United States, but regional stressors such as climatic warming, changing fire conditions, and an expanding wildland-urban interface may challenge its application. To facilitate the development of fire management strategies that account for such changes, we surveyed nearly 300 fire managers to elicit information on the criteria used for prioritizing burn sites, current burning practices and constraints, and expectations for changes in burning opportunities, including those pertaining to climate change and urban growth. Results Respondents noted that their most common criteria for selecting longleaf pine stands for burning were fire history, ecosystem health, and fuel reduction, with the presence of threatened and endangered species also given priority by public land managers. Many respondents (38%) cited recent burn frequencies that fall short of historic burn intervals. Barriers to burning included legal, institutional, and managerial constraints, such as proximity to human developments, public concerns, and risk aversion, as well as environmental and resource constraints, including weather, air quality restrictions, and lack of personnel, equipment, or funding. Roughly half of all respondents expect that opportunities to burn will be reduced over the next 30 years, particularly during the growing season. Fire manager perceptions of factors that will limit prescribed burning in the future include a similar suite of constraints, many of which will be affected by projected regional changes in land use and climate. Conclusions On an organizational level, burn window availability and resource limitations constrain prescribed burning practices. More broadly, policy and legal frameworks coupled with trends in urbanization and climate change are expected to interact with operational constraints to challenge managers’ abilities to implement landscape-scale burning strategies and achieve restoration goals. Additional research and engagement with fire managers are needed to investigate opportunities for introducing policy flexibility, leveraging shared management interests, and developing creative solutions to expand burning opportunities.

Conservation practitioners, natural resource managers, and environmental stewards often seek out scientific contributions to inform decision-making. This body of science only becomes actionable when motivated by decision makers considering alternative courses of action. Many in the science community equate addressing stakeholder science needs with delivering actionable science. However, not all efforts to address science needs deliver actionable science, suggesting that the synonymous use of these two constructs (delivering actionable science and addressing science needs) is not trivial. This can be the case when such needs are conveyed by people who neglect decision makers responsible for articulating a priority management concern and for specifying how the anticipated scientific information will aid the decision-making process. We argue that the actors responsible for articulating these science needs and the process used to identify them are decisive factors in the ability to deliver actionable science, stressing the importance of examining the provenance and the determination of science needs. Guided by a desire to enhance communication and cross-literacy between scientists and decision makers, we identified categories of actors who may inappropriately declare science needs (e.g., applied scientists with and without regulatory affiliation, external influencers, reluctant decision makers, agents in place of decision makers, and boundary organization representatives). We also emphasize the importance of, and general approach to, undertaking needs assessments or gap analyses as a means to identify priority science needs. We conclude that basic stipulations to legitimize actionable science, such as the declaration of decisions of interest that motivate science needs and using a robust process to identify priority information gaps, are not always satisfied and require verification. To alleviate these shortcomings, we formulated practical suggestions for consideration by applied scientists, decision makers, research funding entities, and boundary organizations to help foster conditions that lead to science output being truly actionable.

Abstract Rockland habitat in South Florida, USA, is a threatened ecosystem that has been lost, fragmented, or degraded because of urbanization or other anthropogenic disturbance. Furthermore, low‐lying islands and coastal areas are experiencing sea level rise (SLR) and an increased frequency and intensity of tidal flooding, putting rockland habitats there at increasing risk of ecological change. We evaluated changes in the extent of rockland habitat under various scenarios of future SLR, tidal flooding, and human development for two endemic state‐listed threatened species of snakes, the Rim Rock Crowned Snake ( Tantilla oolitica ) and the Key Ring‐necked Snake ( Diadophis punctatus acricus ). Both snakes are restricted to South Florida. We used recent and historical species' records to determine each species' habitat range. We then estimated the extent of future habitat loss due to SLR and continued human development, as well as degradation of the remaining habitat. We also asked whether the future potential drivers of habitat loss and degradation differ between the two species and across their habitat ranges. We predicted that saltwater intrusion could negatively affect rocklands by 2050, resulting in degradation of 80% of the existing habitat because of an anticipated 42 cm of SLR. Moreover, our model suggests short‐term stochastic events such as storm surge and high tides may increasingly saturate the root zone of rockland vegetation before complete inundation. Under the extreme scenario, we predict most of the rockland habitat used by these two species of snakes may be inundated by 2080. Under the extreme SLR scenario, current rocklands are likely to convert to more halophytic habitat (mangrove or salt marsh wetland) within 50–60 years. Under the low scenario, 31% of rockland habitat may be lost due to human development by 2030. Therefore, mitigation actions may help to conserve specialist species within rockland habitat threatened by human activities and climate change.

Climate change may induce mismatches between wildlife reproductive phenology and temporal occurrence of resources necessary for reproductive success. Verifying and elucidating the causal mechanisms behind potential mismatches requires large-scale, longer-duration data. We used eastern wild turkey (Meleagris gallopavo silvestris) nesting data collected across the southeastern U.S. over eight years to investigate potential climatic drivers of variation in nest initiation dates. We investigated climactic relationships with two datasets, one inclusive of successful and unsuccessful nests (full dataset) and another of just successful nests (successfully hatched dataset), to determine whether successfully hatched nests responded differently to weather changes than all nests did. In the full dataset, each 10 cm increase in January precipitation was associated with nesting occurring 0.46–0.66 days earlier, and each 10 cm increase in precipitation during the 30 days preceding nesting was associated with nesting occurring 0.17–0.21 days later. In the successfully hatched dataset, a 10 cm increase in March precipitation was associated with nesting occurring 0.67–0.74 days earlier, and an increase of one unit of variation in February maximum temperature was associated with nesting occurring 0.02 days later. We combined the results of these modeled relationships with multiple climate scenarios to understand potential implications of future climate change on wild turkey nesting phenology; results indicated that mean nest initiation date is projected to change by <0.1 day by 2040–2060. Wild turkey nesting phenology did not track changes in spring green-up timing, which could result in phenological mismatch between the timing of nesting and the availability of resources critical for successful reproduction.