Geology topics

Adam J. Terando

Publications and source records attributed to Adam J. Terando.

At least 19 recordsLinked to original sources

USGS2026

An overview and participatory framework for choosing spatial boundaries in social–ecological systems modeling

A common challenge when modeling social–ecological systems (SESs) is defining the spatial extent of the system. Boundaries that do not adequately capture both social and ecological processes and their interactions can lead to mischaracterization of the system, while expanding boundaries too widely can impact model complexity and required resources. Socially, boundaries can invoke and influence identity, culture, power, and sense of place. Boundary decisions benefit from flexible, iterative approaches and the expertise of local communities. Here, we use a structured database search supplemented with citation searching to identify and review the literature that addresses choosing or defining spatial boundaries in SESs mapping or modeling and, when applicable, how participatory methods were used in the research process. In a review of the resulting 79 studies, we discovered that pre-existing social or ecological boundaries were used most frequently (36 and 18 publications, respectively). Twenty-one publications combined social and ecological boundaries or data to create custom boundaries, and four studies used an alternative approach to conventional boundaries. Informed by the literature review, we present a general framework for defining boundaries at the outset of SES research. We then connect the framework to a specific case study based on a collaborative project with Tribal, university, and federal scientists to develop a social–ecological climate adaptation plan. We present guiding questions alongside candidate boundaries for our study system and explore the tradeoffs of these boundary options, which can function as a useful template for other social–ecological research collaborations.

ISPRS International Journal of Geo-Information↗

USGS2026

Design, construction and application of an automated thermal chamber to determine critical thermal maxima in small anurans

Critical thermal maximum (CTMax) is a thermal threshold useful for understanding physiological boundaries and predicting anuran vulnerability to climate warming. Given the value of this metric, a comprehensive review of the literature on CTMax protocols (1962-2025) revealed widespread methodological inconsistencies and rare verification of ramping rate accuracy, essential to gauge the reliability of estimates and facilitate comparisons among studies. We engineered an inexpensive (<300 USD) automated CTMax chamber incorporating dual thermocouples, a programmable proportional integral derivative (PID) controller, and real-time software monitoring to address these deficiencies. Performance validation across eight trials demonstrated alignment between programmed and observed temperature ramping rate (0.40 vs. 0.39 °C/min; Z = 1.58, P > 0.05). As a proof-of-concept, we used the proposed system to generate CTMax estimates of six species of Eleutherodactylus in Puerto Rico, including first reports for E. brittoni, E. cochranae, E. wightmanae, and E. juanariveroi. CTMax values ranged from 36.30°C ± 0.52 (E. wightmanae) to 42.10°C ± 0.66 (E. cochranae), with warming tolerances ranging from 15.00°C ± 0.63 to 18.18°C ± 0.73. Our design provides an accurate, replicable, and ethical approach for CTMax measurement and enhances the capacity to assess small amphibian vulnerability to global warming.

SSRN↗

USGS2024

Applying portfolio theory to benefit endangered amphibians in coastal wetlands threatened by climate change, high uncertainty, and significant investment risk

The challenge of selecting strategies to adapt to climate change is complicated by the presence of irreducible uncertainties regarding future conditions. Decisions regarding long-term investments in conservation actions contain significant risk of failure due to these inherent uncertainties. To address this challenge, decision makers need an arsenal of sophisticated but practical tools to help guide spatial conservation strategies. Theory asserts that managing risks can be achieved by diversifying an investment portfolio to include assets – such as stocks and bonds – that respond inversely to one another under a given set of conditions. We demonstrate an approach for formalizing the diversification of conservation assets (land parcels) and actions (restoration, species reintroductions) by using correlation structure to quantify the degree of risk for any proposed management investment. We illustrate a framework for identifying future habitat refugia by integrating species distribution modeling, scenarios of climate change and sea level rise, and impacts to critical habitat. Using the plains coqui ( Eleutherodactylus juanariveroi ), an endangered amphibian known from only three small wetland populations on Puerto Rico’s coastal plains, we evaluate the distribution of potential refugia under two model parameterizations and four future sea-level rise scenarios. We then apply portfolio theory using two distinct objective functions and eight budget levels to inform investment strategies for mitigating risk and increasing species persistence probability. Models project scenario-specific declines in coastal freshwater wetlands from 2% to nearly 30% and concurrent expansions of transitional marsh and estuarine open water. Conditional on the scenario, island-wide species distribution is predicted to contract by 25% to 90%. Optimal portfolios under the first objective function – benefit maximization – emphasizes translocating frogs to existing protected areas rather than investing in the protection of new habitat. Alternatively, optimal strategies using the second objective function – a risk-benefit tradeoff framework – include significant investment to protect parcels for the purpose of reintroduction or establishing new populations. These findings suggest that leveraging existing protected areas for species persistence, while less costly, may contain excessive risk and could result in diminished conservation benefits. Although our modeling includes numerous assumptions and simplifications, we believe this framework provides useful inference for exploring resource dynamics and developing robust adaptation strategies using an approach that is generalizable to other conservation problems which are spatial or portfolio in nature and subject to unresolvable uncertainty.

Puerto Rico↗

USGS2024

Postrelease survival of Eleutherodactylus coqui: Advancing managed translocations as an adaptive tool for climate-vulnerable anurans

Translocating amphibians to alternative, suitable habitat is a climate adaptation strategy aimed at minimizing the risk of extinction due to projected global warming and drying. Projected conditions could undermine their physiological performance, and thus survival and reproduction. Translocations minimize risks of extinction by increasing spatial redundancy across climate-resilient habitats, particularly for dispersal-limited species. However, outcomes of amphibian translocation attempts are poorly documented, and their effectiveness remains unclear. We released and tracked 34 Eleutherodactylus coqui to determine early postrelease survival of a control (nontranslocated) group ( n = 14) and experimental (translocated) group ( n = 20) moved 0.8 km from their capture location in west-central Puerto Rico in 2021. We defined “initial” as the first 17 d postrelease, a period during which we hypothesized that experimental individuals would have lower survival rates because they transitioned from known-familiar to novel-unfamiliar habitat. We found no evidence in the data to support our hypothesis. Daily survival rates were better explained by a model with no group effect but negatively influenced by in situ temperature. However, the effect of in situ temperature (proxy of operative temperature) was weak (95% confidence intervals overlapped 0). After 17 d, all but one of the recaptured frogs lost weight for a combined weight loss of 0.28 ± 0.13 g. However, weight loss was significantly higher in translocated frogs (0.81 ± 0.33 g). Average daily movements did not hinder survival even though experimental individuals traveled farther (～ eight times) than control ones. Our findings suggested that managed translocations have the potential to become a useful conservation tool, not an additive source of mortality. We outline challenges that remain before translocations of Eleutherodactylus species can be broadly applied.

Puerto Rico↗

USGS2024

Projecting the long-term effects of large-scale human influence on the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome

Decades of human activities and fire suppression have adversely affected longleaf pine ( Pinus palustris ) ecosystems, which are home to high levels of diversity and endemism. These iconic ecosystems also now face challenges from urbanization and climate change, which will alter conservation outcomes over the remainder of the 21st century. To explore how long-term, large-scale human influences could affect the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome, we extracted a set of 2400 longleaf pine patches ≥40 ha in size from the Florida Longleaf Pine Ecosystem Geodatabase. Projections from the FUTURES urban growth model and the Florida 2070 project indicate that development will lead to losses of existing longleaf pine habitat, reductions in longleaf pine patch size, and patches that are predominantly located in close proximity to developed areas. Finer-scale patterns of longleaf pine loss in three focal landscapes highlighted differences in land protection, ecological setting, and development pressure and the value of using of multiple urbanization iterations. The occurrence of suitable conditions to conduct prescribed fires, a crucial tool for maintaining, improving, and restoring longleaf pine ecosystems, is projected to decrease seasonally throughout the study area. As a result, the functional persistence of ecosystems is at risk due to climate changes that increase barriers to the safe and reliable application of intentional fire. The long-term viability of this critical ecosystem will warrant the evaluation of adaptive strategies that explicitly account for the individual and compounding effects of urban development and changing fire management conditions when considering options for ecosystem protection, management, and restoration.

Florida↗

USGS2024

A fire-use decision model to improve the United States’ wildfire management and support climate change adaptation

The US faces multiple challenges in facilitating the safe, effective, and proactive use of fire as a landscape management tool. This intentional fire use exposes deeply ingrained communication challenges and distinct but overlapping strategies of prescribed fire, cultural burning, and managed wildfire. We argue for a new conceptual model that is organized around ecological conditions, capacity to act, and motivation to use fire and can integrate and expand intentional fire use as a tool. This result emerges from more considered collaboration and communication of values and needs to address the negative consequences of contemporary fire use. When applied as a communication and translation tool, there is potential to lower barriers to faster and more successful collaboration among stakeholders. Such improvements are a vital part of strategies to address climate adaptation, wildfire mitigation, and the well-being of ecosystems.

Cell Reports Sustainability↗

USGS2024

Approaches for using CMIP projections in climate model ensembles to address the ‘hot model’ problem

Several recent generation global-climate models were found to have anomalously high climate sensitivities and may not be useful for certain applications. Four approaches for developing ensembles of climate projections for applications that address this issue are: Using an “all models” approach; Screening using equilibrium climate sensitivity and (or) transient climate response; Bayesian model averaging; and Using global warming levels. Advantages and disadvantages of each approach are described by using example applications to study the effects of climate change on an imaginary at-risk species. Choosing the right approach is dependent on the location, goals, and system focus of each application and the risk-tolerance and resource-management context.

Open-File Report↗

USGS2024

A spatiotemporal optimization engine for prescribed burning in the Southeast US

Many ecosystems in the Southeast US are dependent upon frequent low-intensity surface fires to sustain native biodiversity, ecosystem services, and endangered species populations. Today, landscape-scale prescribed fire is required to manage these systems for conservation objectives and to mitigate wildland fire risk. Successful application of prescribed fire in this region requires careful planning and assessment of the risks and tradeoffs involved when deciding whether or not to conduct a burn. Many of these risks are closely tied to ambient environmental conditions and are reflected in sets of ‘prescription’ parameters that define safe and effective operating conditions to meet objectives or regulatory requirements. To facilitate effective decision making and acknowledge growing uncertainties related to climate change effects on wildland fire operations, we developed a spatiotemporal optimization engine to identify near-term optimal burning opportunities for prescribed fire implementation. By mining historical 3-day numerical weather forecasts and observation-based weather data for 2015–2021, we have developed a Bayesian hierarchical model for forecast verification that provides calibrated daily weather forecasts and joint uncertainty estimates on meteorological variables of interest, with the latter serving as a measure of risk associated with prescribed fire activities. Burn allocation decisions are then optimized by considering this risk jointly with the utility of burning a particular habitat parcel. The initial iteration of the optimization engine is demonstrated through a case study of short-term meteorological conditions for the Eglin Air Force Base, located in Florida, USA. Results indicate agreement between the optimization engine and the observed past decision-making, with the largest divergences likely arising primarily from differences between utility functions presumed important and used to develop the optimization engine versus the true utility functions driving management behavior in practice.

Florida↗

USGS2024

Using climate-fire analog mapping to inform climate change adaptation strategies for wildland fire in protected areas of the conterminous US

Potential changes in wildland fire regimes due to anthropogenic climate change can be projected using data from climate models, but directly applying these meteorological variables to long-term planning and adaptive management activities may be difficult for decision makers. Analog mapping, in contrast, creates more intuitive assessments of changing fire regimes that also recognize the complex, multivariate, and multi-scalar nature of ecosystems. Here, we use data from 20 downscaled climate models under two climate forcing scenarios, Representative Concentration Pathways (RCP 4.5 and 8.5), to identify and map future climate-fire analogs for 655 protected areas in the conterminous U.S. based on annual temperature, cumulative precipitation amount and seasonality, and fire regime potentials derived from a simple process-based fire frequency model. Patterns of analogs were heavily influenced by gradients in latitude and topography, with longer time frames (end-of-century conditions) and the more extreme climate forcing scenario resulting in greater analog distances and more ensemble entropy (i.e., less consensus among climate models regarding the closest analog for a given management unit). Finer scale analyses for three protected areas (Yellowstone and Great Smoky Mountains National Parks, White Mountain National Forest) illustrate how climate-fire analog mapping can improve insight into the types of ecosystem responses that might occur under similar management conditions. Federally protected areas such as national parks, forests, and wildlife refuges have long served as reference sites for the study of fire regimes, a role that is likely to continue because many of these units are managed to allow at least some ecosystem processes to operate independently. The results suggest that analog mapping approaches are well-suited as part of qualitative assessments within climate- and fire-aware adaptive management processes. The use of analogs to depict relatable, real-world depictions of possible ecosystem changes in a given place, can help managers make more strategic choices about when and where to resist, accept, or direct climate change-driven ecological change.

Global Environmental Change Advances↗

USGS2024

Best practices for incorporating climate change science into Department of the Interior analyses, consultations, and decision making

The purpose of this document is to provide technical guidance, practical application examples, and resource lists for those who conduct, manage, and/or interpret technical workflows within the Department of the Interior. This document is intended to support implementation of Department of the Interior policy 526 DM 1 and establish best practices for using climate change science to inform analysis, consultation, and decision making. The Earth’s climate is an interconnected system that distributes energy, heat, and water around the planet. Due to human-driven increases in long-lived greenhouse gases, the Earth’s climate is now changing. For Departmental decision-making purposes, assuming a static, unchanging baseline climate is no longer consistent with current knowledge about the climate system. There are uncertainties about future climate and how resources or assets (RoAs) will respond to new conditions. To depict the possibilities, the global climate science community develops scenarios and models to explore how future climate may respond to socioeconomic and technological development in the world. Principles for informing policy development, planning and decisions, and regulatory processes using climate change science must: 1) consider the effects of future climate change, 2) characterize the risks, and 3) characterize the uncertainties. Best practices include: Use multiple scenarios to assess risks from a range of plausible societal pathways. When constraints prevent the use of multiple scenarios or if decision makers are risk averse, ensure that the chosen scenario considers higher risk outcomes. This is particularly important for large investments or irreversible decisions and reduces the chances of overconfident decision making. Use multiple climate models within each scenario to account for the range of outcomes due to model uncertainty. Do not rely solely on a single model or an ensemble average. Use relevant climate data . Use a time-period for model projections of the future climate change consistent with the relevant timeframe of the policy, action, or decision being considered. Historical observations are useful for understanding past conditions and climate trends for the next several years, but not beyond the next decade. Consult with climate data and modeling experts to assess which data and model resources are most appropriate for any given application. Clearly describe key analysis uncertainties (including with any climate observations, models, and scenarios used), and how they were addressed in the analysis and/or decision process. This ensures transparency and learning among analysts and decision makers.

Report↗

USGS2023

Spatially interactive modeling of land change identifies location-specific adaptations most likely to lower future flood risk

Impacts of sea level rise will last for centuries; therefore, flood risk modeling must transition from identifying risky locations to assessing how populations can best cope. We present the first spatially interactive (i.e., what happens at one location affects another) land change model (FUTURES 3.0) that can probabilistically predict urban growth while simulating human migration and other responses to flooding, essentially depicting the geography of impact and response. Accounting for human migration reduced total amounts of projected developed land exposed to flooding by 2050 by 5%–24%, depending on flood hazard zone (50%–0.2% annual probability). We simulated various “what-if” scenarios and found managed retreat to be the only intervention with predicted exposure below baseline conditions. In the business-as-usual scenario, existing and future development must be either protected or abandoned to cope with future flooding. Our open framework can be applied to different regions and advances local to regional-scale efforts to evaluate potential risks and tradeoffs.

South Carolina↗

USGS2023

Bayesian weighting of climate models based on climate sensitivity

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.

Communications Earth & Environment↗

USGS2023

Tourist perceptions of climate change impacts on mountain ecotourism in southern Mexico

Climate change impacts on tourism are well documented, with most studies focusing on challenges facing ski or beach tourism. While non-ski, mountain tourism accounts for almost one fifth of tourism worldwide, there is a dearth of research on tourists’ perceptions of climate change impacts and their effects on tourism demand in these areas. This study, conducted at the ecotourism destination of the Pueblos Mancomunados in the Sierra Norte Mountains of southern Mexico, helps to fill that gap by identifying important tourist decision factors and determining how tourists’ decisions to visit may change under different climatic conditions. Using on-site intercept survey research methodology involving 188 tourists, we found that some climate change scenarios affect tourists’ perceptions of the desirability of visiting nature-based tourism sites. Results indicate that community-based ecotourism businesses, such as the one that operates in the Pueblos Mancomunados, need to specifically plan for climate change impacts, as they may need to alter tourism offerings to sustain demand.

Oaxaca↗

USGS2023

Climate change and the global redistribution of biodiversity: Substantial variation in empirical support for expected range shifts

Background Among the most widely predicted climate change-related impacts to biodiversity are geographic range shifts, whereby species shift their spatial distribution to track their climate niches. A series of commonly articulated hypotheses have emerged in the scientific literature suggesting species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to rising temperatures associated with climate change. Yet, many species are not demonstrating range shifts consistent with these expectations. Here, we evaluate the impact of anthropogenic climate change (specifically, changes in temperature and precipitation) on species’ ranges, and assess whether expected range shifts are supported by the body of empirical evidence. Methods We conducted a Systematic Review, searching online databases and search engines in English. Studies were screened in a two-stage process (title/abstract review, followed by full-text review) to evaluate whether they met a list of eligibility criteria. Data coding, extraction, and study validity assessment was completed by a team of trained reviewers and each entry was validated by at least one secondary reviewer. We used logistic regression models to assess whether the direction of shift supported common range-shift expectations (i.e., shifts to higher latitudes and elevations, and deeper depths). We also estimated the magnitude of shifts for the subset of available range-shift data expressed in distance per time (i.e., km/decade). We accounted for methodological attributes at the study level as potential sources of variation. This allowed us to answer two questions: (1) are most species shifting in the direction we expect (i.e., each observation is assessed as support/fail to support our expectation); and (2) what is the average speed of range shifts? Review findings We found that less than half of all range-shift observations (46.60%) documented shifts towards higher latitudes, higher elevations, and greater marine depths, demonstrating significant variation in the empirical evidence for general range shift expectations. For the subset of studies looking at range shift rates, we found that species demonstrated significant average shifts towards higher latitudes (average = 11.8 km/dec) and higher elevations (average = 9 m/dec), although we failed to find significant evidence for shifts to greater marine depths. We found that methodological factors in individual range-shift studies had a significant impact on the reported direction and magnitude of shifts. Finally, we identified important variation across dimensions of range shifts (e.g., greater support for latitude and elevation shifts than depth), parameters (e.g., leading edge shifts faster than trailing edge for latitude), and taxonomic groups (e.g., faster latitudinal shifts for insects than plants). Conclusions Despite growing evidence that species are shifting their ranges in response to climate change, substantial variation exists in the extent to which definitively empirical observations confirm these expectations. Even though on average, rates of shift show significant movement to higher elevations and latitudes for many taxa, most species are not shifting in expected directions. Variation across dimensions and parameters of range shifts, as well as differences across taxonomic groups and variation driven by methodological factors, should be considered when assessing overall confidence in range-shift hypotheses. In order for managers to effectively plan for species redistribution, we need to better account for and predict which species will shift and by how much. The dataset produced for this analysis can be used for future research to explore additional hypotheses to better understand species range shifts.

Journal of Environmental Evidence↗

USGS2023

Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use

Changes in climate and land-use and land-cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (a) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios, (b) sensitivity, and (c) adaptive capacity. The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increases are expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Strong variation in predicted estuarine nutrient loads was observed as a function of climate model projections, while projected LULC changes were more consistently associated with elevated loads. Our findings illustrate the benefits of integrating natural and socio-ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries.

Earth's Future↗

USGS2023

Compact or sprawling cities: Has the sparing-sharing framework yielded an ecological verdict?

Purpose of Review Global urban land area is growing faster than the urban population, raising concerns that sprawling, low-density development will reduce biodiversity and human wellbeing. The sparing-sharing framework, adapted from agroecology, provides one approach to assessing alternative urban growth patterns. It compares ecological outcomes in landscapes matched for total population and land area, but differing in configuration: land sparing (partitioned between densely urbanized and undeveloped areas) or land sharing (low-density development throughout). We reviewed the urban sparing-sharing literature since 2010 and recovered 15 studies conducted in 22 cities on four continents. Recent Findings Collectively, studies assessed effects of alternative development patterns on 296 species, 21 community metrics (such as species richness), and 26 indicators of ecosystem services or processes (such as carbon sequestration). Sparing was the best option for 51% of individual species; 43% of community metrics; and 27% of ecosystem service indicators. Summary Existing ecological research does not clearly favor one pattern or the other, and new approaches are needed to facilitate decision making and ecological insight. Specifically, future work could (1) explicitly evaluate optimized urban development patterns across multiple competing priorities (such as providing housing, delivering ecosystem services, and protecting priority species), (2) tackle issues of spatial scale and connectivity that are often ambiguous in sparing-sharing research, and (3) improve geographical representation. These advances can be made while preserving the key insight of the framework–that choices between alternative landscape configurations are only meaningful when those landscapes are matched for total area and the level of human needs met.

Current Landscape Ecology Reports↗

USGS2023

Minimal shift of eastern wild turkey nesting phenology associated with projected 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.

Climate Change Ecology↗

USGS2022

Climate change weakens the impact of disturbance interval on the growth rate of natural populations of Venus flytrap

Disturbances elicit both positive and negative effects on organisms; these effects vary in their strength and their timing. Effects of disturbance interval (i.e., the length of time between disturbances) on population growth will depend on both the timing and strength of positive and negative effects of disturbances. Climate change can modify the relative strengths of these positive and negative effects, leading to altered optimal disturbance intervals (the disturbance interval at which population growth rate is highest) and changes in the sensitivity of population growth rate to disturbance interval. While we know that climate may alter impacts of disturbance in some systems, we have a poor understanding of which effects of disturbance and which vital rates might drive an altered response to disturbance interval in a changing climate. We use demographic monitoring of natural populations of Dionaea muscipula , the Venus flytrap, that have experienced natural and managed fires, combined with realistic past and future climate projections, to construct climate- and fire-driven integral projection models (IPMs). We use these IPMs to compare the effect of fire return interval (FRI) on population growth rate in past and future climates. To dissect the mechanisms driving FRI response, we then construct IPMs with demographic data from an experimental manipulation of fire effects (ash addition, neighbor removal) and an accidental fire. Our results show that an FRI of 10 years is optimal for D. muscipula in past climate conditions, but a longer FRI (12 years) is optimal in future climate conditions. Further, deviations from optimal FRI reduce population growth rate dramatically in the past climate, but this reduction is muted in a future climate (future minus past sensitivity = 0.006, 95% CI [0.002, 0.011]). Finally, our experimental work suggests that fire effects are driven in part by positive, additive effects of competitor removal and ash addition immediately following a fire; for one population, both these treatments significantly increased population growth rate. Our work suggests that climate change can alter the response of populations to disturbance, highlighting the need to consider the interacting effects of multiple abiotic drivers when projecting future population growth and geographical distributions.

North Carolina↗