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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.

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.

Species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to climate change, reflecting an underlying hypothesis that species will move to cooler locations. However, there is significant variability in observed species range shifts and differences in exposure to climate change may explain some of the variability amongst species. But this requires identifying regions that have experienced detectable changes in those aspects of the climate system that species are sensitive to. To better understand species exposure to climate change, we estimate the time of emergence of climate change for 19 biologically relevant climate variables using observations and initial condition large ensembles from five different climate models. The time of emergence (ToE) is calculated using Signal/Noise (S/N) thresholds. The S/N threshold applied in this study is >=2, but this threshold can be easily modified to represent species that are more or less sensitive to climate change. Preliminary findings from the initial condition large ensembles indicates the strongest emergence for the temperature metrics within the tropical oceanic regions in the absence of upwelling. The earliest emergence over the oceans is found within the western warm pool of the Pacific. Notable places that haven’t emerged for the temperature metrics include both the North Atlantic and Pacific. The ToE of a climate change signal for the temperature metrics over land is spatially complex, which may partially explain the complex observed range shifts for terrestrial species. For instance, multiple initial condition large ensembles indicate a signal has emerge in the most recent decades only for the western and northeastern parts United States.

Most studies of the ecological effects of climate change consider only a limited number of weather drivers that could affect populations, though we know that multiple weather drivers can simultaneously affect population growth rate. Multiple drivers could simultaneously increase/decrease one vital rate, or one may increase a vital rate while another decreases the same vital rate. Considering the impact of multiple weather drivers on vital rates is particularly important in a changing climate, in which correlations among drivers may not be preserved in the future. We used a long-term dataset on the endangered red-cockaded woodpecker (Dryobates borealis) to understand how multiple weather drivers jointly affect survival and reproductive vital rates and then assessed the contributions of individual weather drivers to historical trends in vital rates over time. We found that vital rates were often influenced by more than one weather driver and that weather drivers most commonly exerted opposing effects. For instance, some weather drivers increased vital rates over time, while others acted in the opposite direction, decreasing vital rates over time. Importantly, the historical correlations among weather drivers are almost always projected to change in the future climate, such that future trends in vital rates may not match historical trends. For example, we do not find historical trends in adult survival, but changing correlations among weather drivers could generate future trends in this vital rate. Our work provides an example of how multiple weather drivers can control a variety of vital rates and also illustrates how changes in the correlation structure of weather drivers through time might substantially affect future trends in individual and population performance. Department of DefenseUnited States Department of Defense [W912HQ-15-C-0051]; National Science Foundation (NSF)National Science Foundation (NSF) [BSR-8307090, BSR-8717683]; Harold H. Bailey Fund (Virginia Tech); Sandhills Ecological Institute; U.S. Army Fort Bragg Endangered species branch; United States Department of AgricultureUnited States Department of Agriculture (USDA); National Institute of Food and Agriculture Hatch project [1016746]; NSF Division of Environmental BiologyNational Science Foundation (NSF)NSF - Directorate for Biological Sciences (BIO) [1753980]; North Carolina Sandhills Gamelands and Wildlife Resources Commission Published version We acknowledge financial support from the Department of Defense (Strategic Environmental Resource and Development Program W912HQ-15-C-0051, Marine Corps Base Camp Lejeune, Department of the Army, Fort Bragg, and Eglin Air Force Base), National Science Foundation (NSF, BSR-8307090, BSR-8717683), the Harold H. Bailey Fund (Virginia Tech), Sandhills Ecological Institute, U.S. Army Fort Bragg Endangered species branch, United States Department of Agriculture, National Institute of Food and Agriculture Hatch project 1016746, NSF Division of Environmental Biology 1753980, and North Carolina Sandhills Gamelands and Wildlife Resources Commission. Work was permitted by Federal Endangered Species Permit TE070846, Federal Banding Permit 21544, and Institutional Animal Care Committee Permits 16-209. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This publication is contribution number 22-014-J from the Kansas Agricultural Experiment Station.

Conducting managed species translocations and establishing climate change refugia are adaptation strategies to cope with projected consequences of global warming, but successful implementation requires on-the-ground validation of demographic responses to transient climate conditions. Here we estimated the effect of nine abiotic and biotic factors on local occupancy and an index of abundance (few or chorus) for four amphibian species (Eleutherodactylus wightmanae, E. brittoni, E. antillensis, and E. coqui) in Puerto Rico, USA. We also assessed how the same factors influenced reproductive activity of E. coqui and how species responded to hurricane María (20 September 2017). As predicted, occupancy and abundance of E. wightmanae, E. brittoni and E. coqui were positively and strongly influenced by abiotic covariates (e.g., relative humidity) that characterize high elevation, mesic habitats. E. antillensis exhibited the opposite pattern, with highest probabilities (≥0.6) recorded at ≤300 m and with average relative humidity80% and temperature of ≤26 °C. Moderate to high probabilities of detecting a chorus (0.4–0.7) were recorded at sites with average temperatures>26 °C, but no reproductive activity was detected, implying that monitoring abundance alone could misrepresent the capacity of a local population to sustain itself. The possibility underscores the importance of understanding the interplay between local demographic and environmental parameters in the advent of global warming to help guide monitoring and management decisions, especially for high elevation specialists. Hurricanes can inflict marked reductions in population numbers, but impacts vary by location and species. We found that the abundance (chorus) of E. antillensis and E. brittoni increased after the hurricane, but the abundance of the other two species did not differ between years. Lack of impacts was probably mediated by low structural damage to forest tracts (e.g., 9% canopy loss). Our findings help assess habitat suitability in terms of parameters that foster local population growth, which provides a basis for testing spatio-temporal predictions about demographic rates in potential climate refugia and for designing criteria to help guide managed translocations.

Maintenance of the natural flow regime is essential for continued wetland integrity; however, the flow regime is greatly influenced by both natural and anthropogenic forces. Wetlands may be particularly susceptible to altered flow regimes as they are directly impacted by water flows at a variety of time scales. In Puerto Rico, contemporary water management is decreasing freshwater recharge to wetlands and contributes to the salinization of important coastal wetlands as sea levels rise. Further, downscaled climate models predict an increase in drought frequency, intensity, and duration by mid-century. Conflicts over water allocation seem imminent between human and ecological needs. Current minimum flow policies are insufficient given the complexities of ecosystem processes and the changes in precipitation patterns and sea level rise that are expected in the future. Improved flow policies need to be established that reflect the functional relationships between specific representative ecological resources and components of the natural flow regime across all relevant time scales. Similarly, flow policies need to be developed within a landscape scale to implicitly address the socio-ecological trade-offs as well as the complexities of water management. Multi-disciplinary collaborations will be essential for increasing our resiliency to anticipated future changes.

We examined recent literature on promoting habitat connectivity in the context of climate change (CC) and land-use change (LUC). These two global change forcings have wide-reaching ecological effects that are projected to worsen in the future. Improving connectivity is a common adaptation strategy, but CC and LUC can also degrade planned connections, potentially reducing their effectiveness. We synthesize advances in connectivity design approaches, identify challenges confronted by researchers and practitioners, and offer suggestions for future research. Recent studies incorporated future CC into connectivity design more often than LUC and rarely considered the two drivers jointly. When considering CC, most studies have focused on relatively broad spatial and temporal extents and have included either species-based targets or coarse-filter targets like geodiversity and climate gradients. High levels of uncertainty about future LUC and lack of consistent, readily available model simulations are likely hindering its inclusion in connectivity modeling. This high degree of uncertainty extends to efforts to jointly consider future CC and LUC. We argue that successful promotion of connectivity as a means to adapt to CC and LUC will depend on (1) the velocity of CC, (2) the velocity of LUC, and (3) the degree of existing landscape fragmentation. We present a new conceptual framework to assist in identifying connectivity networks given these three factors. Given the high uncertainty associated with future CC and LUC, incorporating insights from decision science into connectivity planning will facilitate the development of more robust adaptation strategies.

The Weather Research and Forecasting (WRF) model and a combination of the Regional Spectral Model (RSM) and the Japanese Meteorological Agency Non-Hydrostatic Model (NHM) were used to dynamically downscale selected CMIP5 global climate models to provide 2-km projections with hourly model output for Puerto Rico and the U.S. Virgin Islands. Two 20-year time slices were downscaled for historical (1986-2005) and future (2041-2060) periods following RCP8.5. Projected changes to mean and extreme temperature and precipitation were quantified for Holdridge life zones within Puerto Rico and for the U.S. Virgin Islands. The evaluation reveals a persistent cold bias for all islands in the U.S. Caribbean, a dry bias across Puerto Rico, and a wet bias on the windward side of mountains within the U.S. Virgin Islands. Despite these biases, model simulations show a robust drying pattern for all islands that is generally larger for Puerto Rico (25% annual rainfall reduction for some life zones) than the U.S. Virgin Islands (12% island average). The largest precipitation reductions are found during the more convectively active afternoon and evening hours. Within Puerto Rico, the model uncertainty increases for the wetter life zones, especially for precipitation. Across the life zones, both models project unprecedented maximum and minimum temperatures that may exceed 200 days annually above the historical baseline with only small changes to the frequency of extreme rainfall. By contrast, in the U.S. Virgin Islands, there is no consensus on the location of the largest drying relative to the windward and leeward side of the islands. However, the models project the largest increases in maximum temperature on the southern side of St. Croix and in higher elevations of St. Thomas and St. John.

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.

Background Among the most widely anticipated climate-related impacts to biodiversity are geographic range shifts, whereby species shift their spatial distribution in response to changing climate conditions. In particular, a series of commonly articulated hypotheses have emerged: species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to climate change, reflecting an underlying hypothesis that species will move to cooler locations to track spatial changes in the temperature of their current range. Yet, many species are not demonstrating range shifts consistent with these hypotheses. Resolving this discrepancy and providing effective explanations for the observed variability in species’ range shifts is urgently needed to help support a range of natural resource management decisions. Here, we propose a protocol to review the body of evidence for commonly-held climate change range shift hypotheses at the species level focusing on observed latitudinal, longitudinal, elevational, and depth shifts in response to temperature and precipitation changes. We aim to answer the question: what is the impact of anthropogenic climate change (specifically changes in temperature and precipitation) on species ranges? Methods In this review protocol, we propose to conduct a systematic search of literature from internet databases and search engines in English. Articles will be screened in a two-stage process (title/abstract and full text) to evaluate whether they meet a list of eligibility criteria (e.g., presents species-level data, compares > 1 time period). Initial data coding and extraction will be completed by four reviewers and checked by a secondary reviewer from among our co-authors. We will perform a formal meta-analysis to document estimated effect size using the subset of available range-shift data expressed in distance per time (e.g., km/decade). We will also use multinomial logistic regression models to assess the probability that species are shifting in a direction that supports our hypotheses (i.e. towards higher latitudes, greater elevations, and deeper depths). We will account for study methodology as a potential source of variation.

Urban growth and climate change together complicate planning efforts meant to adapt to increasingly scarce water supplies. Several studies have independently examined the impacts of urban planning and climate change on water demand, but little attention has been given to their combined impact. Here we forecast urban water demand using a Geographically Weighted Regression model informed by socio-economic, environmental and landscape pattern metrics. The purpose of our study is to evaluate how future scenarios of population densities and climate warming will jointly affect water demand across two rapidly growing U.S. states (North Carolina and South Carolina). Our forecasts indicate that regional water demand by 2065 will increase by 37%-383% relative to the baseline in 2010, across all scenarios of change. Our results show future water demand will increase under rising temperatures, but could be ameliorated by policies that promote higher density development and urban infill. These water-efficient land use policies show a 5% regional reduction in water demand and up to 25% reduction locally for counties with the highest expected population growth by 2065. For rural counties experiencing depopulation, the land use policies we considered are insufficient to significantly reduce water demand. For expanding communities seeking to increase their adaptive capacity to changing socio-environmental conditions, our framework can assist in developing sustainable solutions.

Designing conservation strategies in human-dominated landscapes is challenging, owing to complex human-natural systems and evolving societal values. To meet this challenge, a robust, adaptive strategy should have a process for flexible implementation of incremental actions. We describe a hypothetical example for the Rio Grande de Arecibo watershed and coastal wetlands in Puerto Rico to address the first component. The process begins by identifying shared stakeholder objectives. This process benefits from a review of foundational research and knowledge base that includes global forcings and vulnerability of resources of interest. Forcings include climate change and pervasive urban sprawl. We focus on two taxonomic groups with differing life histories but strong dependence on water resource dynamics, another resource valued by humans. We stipulate objectives and multiple actions, but focus on those pertaining to hydro-management as the common thread in our example. We advanced two decision contexts of contrasting complexity, illustrated links between objectives and actions, and highlighted trade-offs triggered by varying resource valuation. Our focus was to highlight various components necessary to frame a resilience-based strategy, but we cannot overemphasize the importance of accommodating institutional and stakeholder changing priorities and values to ensure its successful implementation.

We present results from 20-year “high-resolution” regional climate model simulations of precipitation change for the sub-tropical island of Puerto Rico. The Japanese Meteorological Agency Non-Hydrostatic Model (NHM) operating at a 2-km grid resolution is nested inside the Regional Spectral Model (RSM) at 10-km grid resolution, which in turn is forced at the lateral boundaries by the Community Climate System Model (CCSM4). At this resolution, the climate change experiment allows for deep convection in model integrations, which is an important consideration for sub-tropical regions in general, and on islands with steep precipitation gradients in particular that strongly influence local ecological processes and the provision of ecosystem services. Projected precipitation change for this region of the Caribbean is simulated for the mid-twenty-first century (2041–2060) under the RCP8.5 climate-forcing scenario relative to the late twentieth century (1986–2005). The results show that by the mid-twenty-first century, there is an overall rainfall reduction over the island for all seasons compared to the recent climate but with diminished mid-summer drought (MSD) in the northwestern parts of the island. Importantly, extreme rainfall events on sub-daily and daily time scales also become slightly less frequent in the projected mid-twenty-first-century climate over most regions of the island.

In recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios is typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast for precipitation and ~30% for extreme heat days (>35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a subsample of all models is available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. This article concludes with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

The sensitivity of the precipitation over Puerto Rico that is simulated by the Weather Research and Forecasting (WRF) Model is evaluated using multiple combinations of cumulus parameterization (CP) schemes and interior grid nudging. The NCEP–DOE AMIP-II reanalysis (R-2) is downscaled to 2-km horizontal grid spacing both with convective-permitting simulations (CP active only in the middle and outer domains) and with CP schemes active in all domains. The results generally show lower simulated precipitation amounts than are observed, regardless of WRF configuration, but activating the CP schemes in the inner domain improves the annual cycle, intensity, and placement of rainfall relative to the convective-permitting simulations. Furthermore, the use of interior-grid-nudging techniques in the outer domains improves the placement and intensity of rainfall in the inner domain. Incorporating a CP scheme at convective-permitting scales (4 km) improves the island average correlation of precipitation by 0.05–0.2 and reduces the island average RMSE by up to 40 mm on average over relying on the explicit microphysics at convective-permitting scales with grid nudging. Projected changes in summer precipitation between 2040–42 and 1985–87 using WRF to downscale CCSM4 range from a 2.6-mm average increase to an 81.9-mm average decrease, depending on the choice of CP scheme. The differences are only associated with differences between WRF configurations, which indicates the importance of CP scheme for projected precipitation change as well as historical accuracy.

The potential ecological and economic effects of climate change for tropical islands were studied using output from 12 statistically downscaled general circulation models (GCMs) taking Puerto Rico as a test case. Two model selection/model averaging strategies were used: the average of all available GCMs and the average of the models that are able to reproduce the observed large-scale dynamics that control precipitation over the Caribbean. Five island-wide and multidecadal averages of daily precipitation and temperature were estimated by way of a climatology-informed interpolation of the site-specific downscaled climate model output. Annual cooling degree-days (CDD) were calculated as a proxy index for air-conditioning energy demand, and two measures of annual no-rainfall days were used as drought indices. Holdridge life zone classification was used to map the possible ecological effects of climate change. Precipitation is predicted to decline in both model ensembles, but the decrease was more severe in the “regionally consistent” models. The precipitation declines cause gradual and linear increases in drought intensity and extremes. The warming from the 1960–90 period to the 2071–99 period was 4.6°–9°C depending on the global emission scenarios and location. This warming may cause increases in CDD, and consequently increasing energy demands. Life zones may shift from wetter to drier zones with the possibility of losing most, if not all, of the subtropical rain forests and extinction risks to rain forest specialists or obligates.

Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria (a ‘burn window’) are met. Here, we evaluate the potential impacts of projected climatic change on prescribed burning in the south-eastern United States by applying a set of burn window criteria that capture temperature, relative humidity and wind speed to projections from an ensemble of Global Climate Models under two greenhouse gas emission scenarios. Regionally, the percentage of suitable days for burning changes little during winter but decreases substantially in summer owing to rising temperatures by the end of the 21st century compared with historical conditions. Management implications of such changes for six representative land management units include seasonal shifts in burning opportunities from summer to cool-season months, but with considerable regional variation. We contend that the practical constraints of rising temperatures on prescribed fire activities represent a significant future challenge and show that even meeting basic burn criteria (as defined today) will become increasingly difficult over time, which speaks to the need for adaptive management strategies to prepare for such changes.

Low cost temperature sensors are increasingly used by ecologists to assess climatic variation and change on ecologically relevant scales. Although cost-effective, if not deployed with proper solar radiation shielding, the observations recorded from these sensors will be biased and inaccurate. Manufactured radiation shields are effective at minimizing this bias, but are expensive compared to the cost of these sensors. Here, we provide a detailed methodology for constructing a compact version of a previously described custom fabricated radiation shield, which is more accurate than other published shielding methods that attempt to minimize shield size or construction costs. The method requires very little material: corrugated plastic sheets, aluminum foil duct tape, and cable ties. One 15 cm and two 10 cm squares of corrugated plastic are used for each shield. After cutting, scoring, taping and stapling of the sheets, the 10 cm squares form the bottom two layers of the solar radiation shield, while the 15 cm square forms the top layer. The three sheets are held together with cable ties. This compact solar radiation shield can be suspended, or placed against any flat surface. Care must be taken to ensure that the shield is completely parallel to the ground to prevent direct solar radiation from reaching the sensor, possibly causing increased warm biases in sun-exposed sites in the morning and afternoon relative to the original, larger design. Even so, differences in recorded temperatures between the smaller, compact shield design and the original design were small (mean daytime bias = 0.06 °C). Construction costs are less than half of the original shield design, and the new design results in a less conspicuous instrument that may be advantageous in many field ecology settings.

Understanding the relative impact of climate change and land cover change on changes in avian distribution has implications for the future course of avian distributions and appropriate management strategies. Due to the dynamic nature of climate change, our goal was to investigate the processes that shape species distributions, rather than the current distributional patterns. To this end, we analyzed changes in the distribution of Eastern Wood Pewees (Contopus virens) and Red‐eyed Vireos (Vireo olivaceus) from 1997 to 2012 using Breeding Bird Survey data and dynamic correlated‐detection occupancy models. We estimated the local colonization and extinction rates of these species in relation to changes in climate (hours of extreme temperature) and changes in land cover (amount of nesting habitat). We fit six nested models to partition the deviance explained by spatial and temporal components of land cover and climate. We isolated the temporal components of environmental variables because this is the essence of global change. For both species, model fit was significantly improved when we modeled vital rates as a function of spatial variation in climate and land cover. Model fit improved only marginally when we added temporal variation in climate and land cover to the model. Temporal variation in climate explained more deviance than temporal variation in land cover, although both combined only explained 20% (Eastern Wood Pewee) and 6% (Red‐eyed Vireo) of temporal variation in vital rates. Our results showing a significant correlation between initial occupancy and environmental covariates are consistent with biological expectation and previous studies. The weak correlation between vital rates and temporal changes in covariates indicated that we have yet to identify the most relevant components of global change influencing the distributions of these species and, more importantly, that spatially significant covariates are not necessarily driving temporal shifts in avian distributions.

In light of global climate change, ecological studies increasingly address effects of temperature on organisms and ecosystems. To measure air temperature at biologically relevant scales in the field, ecologists often use small, portable temperature sensors. Sensors must be shielded from solar radiation to provide accurate temperature measurements, but our review of 18 years of ecological literature indicates that shielding practices vary across studies (when reported at all), and that ecologists often invent and construct ad hoc radiation shields without testing their efficacy. We performed two field experiments to examine the accuracy of temperature observations from three commonly used portable data loggers (HOBO Pro, HOBO Pendant, and iButton hygrochron) housed in manufactured Gill shields or ad hoc, custom‐fabricated shields constructed from everyday materials such as plastic cups. We installed this sensor array (five replicates of 11 sensor‐shield combinations) at weather stations located in open and forested sites. HOBO Pro sensors with Gill shields were the most accurate devices, with a mean absolute error of 0.2°C relative to weather stations at each site. Error in ad hoc shield treatments ranged from 0.8 to 3.0°C, with the largest errors at the open site. We then deployed one replicate of each sensor‐shield combination at five sites that varied in the amount of urban impervious surface cover, which presents a further shielding challenge. Bias in sensors paired with ad hoc shields increased by up to 0.7°C for every 10% increase in impervious surface. Our results indicate that, due to variable shielding practices, the ecological literature likely includes highly biased temperature data that cannot be compared directly across studies. If left unaddressed, these errors will hinder efforts to predict biological responses to climate change. We call for greater standardization in how temperature data are recorded in the field, handled in analyses, and reported in publications.

Ecologists who specialize in translational ecology (TE) seek to link ecological knowledge to decision making by integrating ecological science with the full complement of social dimensions that underlie today's complex environmental issues. TE is motivated by a search for outcomes that directly serve the needs of natural resource managers and decision makers. This objective distinguishes it from both basic and applied ecological research and, as a practice, it deliberately extends research beyond theory or opportunistic applications. TE is uniquely positioned to address complex issues through interdisciplinary team approaches and integrated scientist–practitioner partnerships. The creativity and context-specific knowledge of resource managers, practitioners, and decision makers inform and enrich the scientific process and help shape use-driven, actionable science. Moreover, addressing research questions that arise from on-the-ground management issues – as opposed to the top-down or expert-oriented perspectives of traditional science – can foster the high levels of trust and commitment that are critical for long-term, sustained engagement between partners.

We define a translational ecologist as a professional ecologist with diverse disciplinary expertise and skill sets, as well as a suitable personal disposition, who engages across social, professional, and disciplinary boundaries to partner with decision makers to achieve practical environmental solutions. Becoming a translational ecologist requires specific attention to obtaining critical non-scientific disciplinary breadth and skills that are not typically gained through graduate-level education. Here, we outline a need for individuals with broad training in interdisciplinary skills, use our personal experiences as a basis for assessing the types of interdisciplinary skills that would benefit potential translational ecologists, and present steps that interested ecologists may take toward becoming translational. Skills relevant to translational ecologists may be garnered through personal experiences, informal training, short courses, fellowships, and graduate programs, among others. We argue that a translational ecology workforce is needed to bridge the gap between science and natural resource decisions. Furthermore, we argue that this task is a cooperative responsibility of individuals interested in pursuing these careers, educational institutions interested in training scientists for professional roles outside of academia, and employers seeking to hire skilled workers who can foster stakeholder-engaged decision making.

Author(s): Terando, Adam; Youngsteadt, Elsa; Meineke, Emily; Prado, Sara | Abstract: Linked Article: https://doi.org/10.1002/ece3.3965.

Future land-use development has the potential to profoundly affect the health of aquatic ecosystems in the coming decades. We developed regression models predicting the loss of sensitive fish (R2 = 0.39) and macroinvertebrate (R2 = 0.64) taxa as a function of urban and agricultural land uses and applied them to projected urbanization of the rapidly urbanizing Piedmont ecoregion of the southeastern USA for 2030 and 2060. The regression models are based on a 2014 investigation of water quality and ecology of 75 wadeable streams across the region. Based on these projections, stream kilometers experiencing >50% loss of sensitive fish and invertebrate taxa will nearly quadruple to 19,500 and 38,950 km by 2060 (16 and 32% of small stream kilometers in the region), respectively. Uncertainty was assessed using the 20 and 80% probability of urbanization for the land-use projection model and using the 95% confidence intervals for the regression models. Adverse effects on stream health were linked to elevated concentrations of contaminants and nutrients, low dissolved oxygen, and streamflow alteration, all associated with urbanization. The results of this analysis provide a warning of potential risks from future urbanization and perhaps some guidance on how those risks might be mitigated.

The authors examine recent changes in three agro-climate indices (frost days, thermal time, and heat stress index) in North America (centered around the continental United States) using observations from a historical climate network and an ensemble of 17 global climate models (GCMs) from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Agro-climate indices provide the basis for analyzing agricultural time series that are unbiased by long-term technological intervention. Observations from the last 60 years (1951–2010) confirm conclusions of previous studies showing continuing declines in the number of frost days and increases in thermal time. Increases in heat stress are largely confined to the western half of the continent. The authors do not observe accelerating agro-climate warming trends in the most recent decade of observations. The spatial variability of the temporal trends in GCMs is lower compared to the observed patterns, which still show some regional cooling trends. GCM skill, defined as the ability to reproduce observed patterns (i.e., correlation and error) and variability, is highest for frost days and lowest for heat stress patterns. Individual GCM skill is incorporated into two model weighting schemes to gauge their ability to reduce predictive uncertainty for agro-climate indices. The two weighted GCM ensembles do not substantially improve results compared to the unweighted ensemble mean. The lack of agreement between simulated and observed heat stress is relatively robust with respect to how the heuristic is defined and appears to reflect a weakness in the ability of this last generation of GCMs to reproduce this impact-relevant aspect of the climate system. However, it remains a question for future work as to whether the discrepancies between observed and simulated trends primarily reflect fundamental errors in model physics or an incomplete treatment of relevant regional climate forcings.

Projections of future climatic changes are a key input to the design of climate change mitigation and adaptation strategies. Current climate change projections are deeply uncertain. This uncertainty stems from several factors, including parametric and structural uncertainties. One common approach to characterize and, if possible, reduce these uncertainties is to confront (calibrate in a broad sense) the models with historical observations. Here, we analyze the problem of combining multiple climate models using Bayesian Model Averaging (BMA) to derive future projections and quantify uncertainty estimates of spatiotemporally resolved temperature hindcasts and projections. One advantage of the BMA approach is that it allows the assessment of the predictive skill of a model using the training data, which can help identify the better models and discard poor models. Previous BMA approaches have broken important new ground, but often neglected space–time dependencies and/or imposed prohibitive computational demands. Here we improve on the current state-of-the-art by incorporating space–time dependence while using historical data to estimate model weights. We achieve computational efficiency using a kernel mixing approach for representing a space–time process. One key advantage of our new approach is that it enables us to incorporate multiple sources of uncertainty and biases, while remaining computationally tractable for large data sets. We introduce and apply our approach using BMA to an ensemble of Global Circulation Model output from the Intergovernmental Panel on Climate Change Fourth Assessment Report of surface temperature on a grid of space–time locations.

Path dependence of farmers’ technical choices for managing climate risk combined with farmers’ difficulties in discerning climate change from natural variability might hamper adaptation to climate ...

Shaded coffee plantations are of conservation value for many taxa, particularly for resident avifauna in the face of extensive landscape changes. Yet, little is known about the value of coffee plantations for amphibians because there are scant demographic data to index their value among species with different habitat preferences. We estimated the probability of occupancy of three frog species: Eleutherodactylus wightmanae, a forest species; E. brittoni, a grassland species; and E. antillensis, an open habitat species. Occupancy was estimated in sun and shaded plantations, and in secondary forest, in the west-central mountains of Puerto Rico. We also estimated the probability that a survey station was occupied by no individuals, one, or >1 individual, as a proxy of abundance. The aforementioned parameters, and local colonization and extinction probability, were modeled as a function of weather conditions (temperature, humidity) and vegetation cover at the sampling station (5 m) and contextual (100...

Managing ecosystems for resilience and sustainability requires understanding how they will respond to future anthropogenic drivers such as climate change and urbanization. In fire-dependent ecosystems, predicting this response requires a focus on how these drivers will impact fire regimes. Here, we use scenarios of climate change, urbanization and management to simulate the future dynamics of the critically endangered and fire-dependent longleaf pine (Pinus palustris) ecosystem. We investigated how climate change and urbanization will affect the ecosystem, and whether the two conservation goals of a 135% increase in total longleaf area and a doubling of fire-maintained open-canopy habitat can be achieved in the face of these drivers. Our results show that while climatic warming had little effect on the wildfire regime, and thus on longleaf pine dynamics, urban growth led to an 8% reduction in annual wildfire area. The management scenarios we tested increase the ecosystem's total extent by up to 62% and result in expansion of open-canopy longleaf by as much as 216%, meeting one of the two conservation goals for the ecosystem. We find that both conservation goals for this ecosystem, which is climate-resilient but vulnerable to urbanization, are only attainable if a greater focus is placed on restoration of non-longleaf areas as opposed to maintaining existing longleaf stands. Our approach demonstrates the importance of accounting for multiple relevant anthropogenic threats in an ecosystem-specific context in order to facilitate more effective management actions.

The future health of ecosystems is arguably as dependent on urban sprawl as it is on human-caused climatic warming. Urban sprawl strongly impacts the urban ecosystems it creates and the natural and agro-ecosystems that it displaces and fragments. Here, we project urban sprawl changes for the next 50 years for the fast-growing Southeast U.S. Previous studies have focused on modeling population density, but the urban extent is arguably as important as population density per se in terms of its ecological and conservation impacts. We develop simulations using the SLEUTH urban growth model that complement population-driven models but focus on spatial pattern and extent. To better capture the reach of low-density suburban development, we extend the capabilities of SLEUTH by incorporating street-network information. Our simulations point to a future in which the extent of urbanization in the Southeast is projected to increase by 101% to 192%. Our results highlight areas where ecosystem fragmentation is likely, and serve as a benchmark to explore the challenging tradeoffs between ecosystem health, economic growth and cultural desires.

[1] We develop probabilistic projections for three agro-climate indices (frost days, thermal time, and a heat stress index) for North America. The selected indices are important for understanding the potential impacts of future anthropogenic climate change on agricultural production. We use Bayesian Model Averaging (BMA) and bootstrapping to quantify the structural uncertainty in an ensemble of downscaled General Circulation Models (GCMs). The prior information contained in the observations and model hindcasts is used to construct physically meaningful temporal comparisons for the period 1961–2010. The comparisons are used to derive model-specific posterior weights to construct probabilistic projections of agro-climate change in the 21st century. A cross validation test covering the most recent 25 years of the observation period indicates considerable overconfidence in the projections when using the calibrated BMA approach. In contrast the probabilistic projections using equally weighted climate models are not overconfident. The strong consensus among the probabilistic projections that shows warming effects for all three agro-climate indices is tempered by the short 50-year calibration period and the small ensemble size. The short calibration period provides a relatively poor observational constraint on estimates of model weights and predictive variance, while the small ensemble size limits the climate sample space. However, the consensus that emerges in spite of the large uncertainties suggests large potential changes in the conditions that farmers will experience over the remainder of the 21st century. Of particular concern is the projected increase in the heat stress index which could lead to large crop damages and associated yield declines.

Forecasting ecological responses to climate change represents a challenge to the ecological community because models are often site-specific and climate data are lacking at appropriate spatial and temporal resolutions. We use a case study approach to demonstrate uncertainties in ecological predictions related to the driving climatic input data. We use observational records, derived observational datasets (e.g. interpolated observations from local weather stations and gridded data products) and output from general circulation models (GCM) in conjunction with site based phenology models to estimate the first flowering date (FFD) for three woody flowering species. Using derived observations over the modern time period, we find that cold biases and temperature trends lead to biased FFD simulations for all three species. Observational datasets resolved at the daily time step result in better FFD predictions compared to simulations using monthly resolution. Simulations using output from an ensemble of GCM and regional climate models over modern and future time periods have large intra-ensemble spreads and tend to underestimate observed FFD trends for the modern period. These results indicate that certain forcing datasets may be missing key features needed to generate accurate hindcasts at the local scale (e.g. trends, temporal resolution), and that standard modeling techniques (e.g. downscaling, ensemble mean, etc) may not necessarily improve the prediction of the ecological response. Studies attempting to simulate local ecological processes under modern and future climate forcing therefore need to quantify and propagate the climate data uncertainties in their simulations.