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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 73 records · Page 4Linked to original sources
USGS2020

A pheromone antagonist liberates female sea lamprey from a sensory trap to enable reliable communication

The evolution of male signals and female preferences remains a central question in the study of animal communication. The sensory trap model suggests males evolve signals that mimic cues used in nonsexual contexts and thus manipulate female behavior to generate mating opportunities. Much evidence supports the sensory trap model, but how females glean reliable information from both mimetic signals and their model cues remains unknown. We discovered a mechanism whereby a manipulative male signal guides reliable communication in sea lamprey ( Petromyzon marinus ). Migratory sea lamprey follow a larval cue into spawning streams; once sexually mature, males release a pheromone that mimics the larval cue and attracts females. Females conceivably benefit from the mimetic pheromone during mate search but must discriminate against the model cue to avoid orienting toward larvae in nearby nursery habitats. We tested the hypothesis that spawning females respond to petromyzonol sulfate (PZS) as a behavioral antagonist to avoid attraction to the larval cue while tracking the male pheromone despite each containing attractive 3-keto petromyzonol sulfate (3kPZS). We found 1) PZS inhibited electrophysiological responses to 3kPZS and abated preferences for 3kPZS when mixed at the same or greater concentrations, 2) larvae released more PZS than 3kPZS whereas males released more 3kPZS than PZS, and 3) mixtures of 3kPZS and PZS applied at ratios measured in larval and male odorants resulted in the discrimination observed between the natural odors. Our study elucidates how communication systems that arise via deception can facilitate reliable communication.

Proceedings of the National Academy of Sciences of↗
USGS2017

Computer modelling for ecosystem service assessment

Computer models are simplified representations of the environment that allow biophysical, ecological, and/or socio-economic characteristics to be quantified and explored. Modelling approaches differ from mapping approaches (Chapter 5) as (i) they are not forcibly spatial (although many models do produce spatial outputs); (ii) they focus on understanding and quantifying the interactions between different components of social and/or environmental systems and (iii) by changing parameters within models, they are capable of exploring both alternative scenarios and internal model dynamics. When applied to the assessment of ecosystem services (ES), models are important tools which can quantify the relationships that underpin ES supply, demand and flows and, in some cases, produce maps representing these factors. Furthermore, as models can explore scenarios, trade-offs that result from different scenarios can be assessed. This chapter provides a broad overview of different types of models that have been applied to ES assessments and discusses, with examples, the ways that these models have the potential to be used in practice. In the context of ES, there are a number of ways of distinguishing between different types of models. Here, we distinguish between individual models focussing on single ES and modelling frameworks that can assess multiple ES within the framework of a single modelling tool.

Book chapter↗
USGS2023

Developing a stochastic hydrological model for informing lake water level drawdown management

Winter drawdown (WD) is a common lake management tool for multiple purposes such as flood control, aquatic vegetation reduction, and lake infrastructure maintenance. To minimize adverse impacts to a lake’s ecosystem, regulatory agencies may provide managers with general guidelines for drawdown and refill timing, drawdown magnitude, and outflow limitations. However, there is significant uncertainty associated with the potential to meet management targets due to variability in lake characteristics and hydrometeorology of each lake’s basin, making the use of modeling tools a necessity. In this context, we developed a hydrological modeling framework for lake water level drawdown management (HMF-Lake) and evaluated it at 15 Massachusetts lakes where WDs have been applied over multiple years for vegetation control. HMF-Lake is based on the daily lake water balance, with inflows simulated by a lumped rainfall-runoff model (Cemaneige-GR4J) and outflow rate calculated by a modified Target Storage and Release Based Method (TSRB). The model showed a satisfactory performance of simulating historical water levels (0.53 ≤ NSE ≤ 0.86), however, uncertainties from meteorological inputs and TSRB determined lake outflow rate affected the result accuracy. To account for these uncertainties, the model was executed stochastically to assess the ability of study lakes to follow the Massachusetts’ general WD guidelines: drawdown by Dec 1 and fully refilled by Apr 1. By using the stochastic HMF-Lake, the probabilities of each lake to reach the drawdown level by Dec 1 were calculated for different drawdown magnitudes (1–6 ft). The probability results suggest it was generally less possible for most of study lakes to achieve a drawdown of 3 ft or more by Dec 1. Moreover, we employed the stochastic model to derive the annual latest refill starting dates that ensure a 95 % probability of reaching the normal water level by Apr 1. We found starting a refill in March for drawdowns up to 6 ft was feasible for most of study lakes. These results provide lake managers with a quantitative understanding of the lake’s ability to follow the state guidelines. The model may be used to systematically evaluate current WD management strategies at state or regional scales and support adaptive WD management under changing climates.

Massachusetts↗
USGS2019

An evaluation of debris-flow runout model accuracy and complexity in Montecito, CA: Towards a framework for regional inundation-hazard forecasting

Numerous debris-flow inundation models have been applied retroactively to noteworthy events around the world. While such studies can be useful in identifying controlling factors, calibrating model parameters, and assessing future hazards in specific study areas, model parameters tailored to individual events can be difficult to apply regionally. The advancement of debris-flow modeling applications from post-event model validation of individual case studies to pre-event forecasting that can be implemented rapidly and at regional scales is critical considering the fatalities and extensive infrastructure damage caused by debris flows that inundated a developed fan in Montecito, CA following heavy rain on 9 January 2018. In this study, we evaluated the tradeoffs between model accuracy and simplicity in the context of the need for a framework that can be used in conjunction with initiation models and storm predictions for rapid, large-scale inundation hazard mapping as a component of post-fire debris-flow hazard assessments. We used numerical (FLO-2D) and empirical (LAHARZ) models to simulate debris flows from one of the drainages upstream of Montecito that was burned in the Thomas Fire in December 2017 and compared model results with field observations and building damage assessments collected immediately following the event. Initial testing demonstrated that LAHARZ can simulate channelized flow but is not able to replicate flow bifurcations or avulsions, which are critical aspects of flows travelling over populated fans. FLO-2D simulations matched well with observed inundation area data, but variably under and overpredicted inundation height, deposit depth, and velocity. We found that FLO-2D and LAHARZ had true positive rates of 0.84 and 0.6, respectively, and both models had similar false positive rates (0.3 and 0.35, respectively). Our model evaluation framework allowed us to compare model results with detailed field observations and will serve as a platform for more extensive model testing in the future.

California↗
USGS2016

Decision-making for foot-and-mouth disease control: Objectives matter

Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.

Epidemics↗
USGS2014

Predicting occupancy for pygmy rabbits in Wyoming: an independent evaluation of two species distribution models

Species distribution models are an important component of natural-resource conservation planning efforts. Independent, external evaluation of their accuracy is important before they are used in management contexts. We evaluated the classification accuracy of two species distribution models designed to predict the distribution of pygmy rabbit Brachylagus idahoensis habitat in southwestern Wyoming, USA. The Nature Conservancy model was deductive and based on published information and expert opinion, whereas the Wyoming Natural Diversity Database model was statistically derived using historical observation data. We randomly selected 187 evaluation survey points throughout southwestern Wyoming in areas predicted to be habitat and areas predicted to be nonhabitat for each model. The Nature Conservancy model correctly classified 39 of 77 (50.6%) unoccupied evaluation plots and 65 of 88 (73.9%) occupied plots for an overall classification success of 63.3%. The Wyoming Natural Diversity Database model correctly classified 53 of 95 (55.8%) unoccupied plots and 59 of 88 (67.0%) occupied plots for an overall classification success of 61.2%. Based on 95% asymptotic confidence intervals, classification success of the two models did not differ. The models jointly classified 10.8% of the area as habitat and 47.4% of the area as nonhabitat, but were discordant in classifying the remaining 41.9% of the area. To evaluate how anthropogenic development affected model predictive success, we surveyed 120 additional plots among three density levels of gas-field road networks. Classification success declined sharply for both models as road-density level increased beyond 5 km of roads per km-squared area. Both models were more effective at predicting habitat than nonhabitat in relatively undeveloped areas, and neither was effective at accounting for the effects of gas-energy-development road networks. Resource managers who wish to know the amount of pygmy rabbit habitat present in an area or wanting to direct gas-drilling efforts away from pygmy rabbit habitat may want to consider both models in an ensemble manner, where more confidence is placed in mapped areas (i.e., pixels) for which both models agree than for areas where there is model disagreement.

Wyoming↗
USGS1998

The Fremont complex: A behavioral perspective

The Fremont complex is composed of farmers and foragers who occupied the Colorado Plateau and Great Basin region of western North America from about 2100 to 500 years ago. These people included both immigrants and indigenes who shared some material culture and symbolic attributes, but also varied in ways not captured by definitions of the Fremont as a shared cultural tradition. The complex reflects a mosaic of behaviors including full-time farmers, full-time foragers, part-time farmer/foragers who seasonally switched modes of production, farmers who switched to full-time foraging, and foragers who switched to full-time farming. Farming defines the Fremont, but only in the sense that it altered the matrix in which both farmers and foragers lived, a matrix which provided a variety of behavioral options to people pursuing an array of adaptive strategies. The mix of symbiotic and competitive relationships among farmers and between farmers and foragers presents challenges to detection in the archaeological record. Greater clarity results from use of a behavioral model which recognizes differing contexts of selection favoring one adaptive strategy over another. The Fremont is a case where the transition from foraging to farming is followed by a millennium of adaptive diversity and terminates with the abandonment of farming. As such, it serves as a potential comparison to other cases in the world during the early phases of the food producing transition.

Journal of World Prehistory↗
USGS2014

A methodology for adaptable and robust ecosystem services assessment

Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant “one model fits all” paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES - both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts.

PLoS ONE↗
USGS2024

When do we need multiple infectious disease models? Agreement between projection rank and magnitude in a multi-model setting

Mathematical models are useful for public health planning and response to infectious disease threats. However, different models can provide differing results, which can hamper decision making if not synthesized appropriately. To address this challenge, multi-model hubs convene independent modeling groups to generate ensembles, known to provide more accurate predictions of future outcomes. Yet, these hubs are resource intensive, and how many models are sufficient in a hub is not known. Here, we compare the benefit of predictions from multiple models in different contexts: (1) decision settings that depend on predictions of quantitative outcomes (e.g., hospital capacity planning), where assessments of the benefits of multi-model ensembles have largely focused; and (2) decisions settings that require the ranking of alternative epidemic scenarios (e.g., comparing outcomes under multiple possible interventions and biological uncertainties). We develop a mathematical framework to mimic a multi-model prediction setting, and use this framework to quantify how frequently predictions from different models agree. We further explore multi-model agreement using real-world, empirical data from 14 rounds of U.S. COVID-19 Scenario Modeling Hub projections. Our results suggest that the value of multiple models could be different in different decision contexts, and if only a few models are available, focusing on the rank of alternative epidemic scenarios could be more robust than focusing on quantitative outcomes. Although additional exploration of the sufficient number of models for different contexts is still needed, our results indicate that it may be possible to identify decision contexts where it is robust to rely on fewer models, a finding that can inform the use of modeling resources during future public health crises.

Epidemics↗
USGS2000

The use of mathematical models to predict beach behavior for U.S. coastal engineering: A critical review

A number of assumed empirical relationships (e.g., the Bruun Rule, the equilibrium shoreface profile, longshore transport rate equation, beach length: durability relationship, and the renourishment factor) and deterministic numerical models (e.g., GENESIS, SBEACH) have become important tools for investigating coastal processes and for coastal engineering design in the U.S. They are also used as the basis for making public policy decisions, such as the feasibility of nourishing recreational beaches. A review of the foundations of these relationships and models, however, suggests that they are inadequate for the tasks for which they are used. Many of the assumptions used in analytical and numerical models are not valid in the context of modern oceanographic and geologic principles. We believe the models are oversimplifications of complex systems that are poorly understood. There are several reasons for this, including: (1) poor assumptions and important omissions in model formulation; (2) the use of relationships of questionable validity to predict the morphologic response to physical forcing; (3) the lack of hindsighting and objective evaluation of beach behavior predictions for engineering projects; (4) the incorrect use of model calibration and verification as assertions of model veracity; and (5) the fundamental inability to predict coastal evolution quantitatively at the engineering and planning time and space scales our society assumes and demands. It is essential that coastal geologists, beach designers and coastal modelers understand these model limitations. Each important model assumption must be examined in isolation; incorporating them into a model does not improve their validity. It is our belief that the models reviewed here should not be relied on as a design tool until they have been substantially modified and proven in real-world situations. The 'solution,' however, is not to increase the complexity of a model by increasing the number of variables. What is needed is a thoughtful review of what beach behavior questions should or could be answered by modeling. Viable alternatives to the use of models do exist to predict the behavior of beaches. Three such alternatives to models are discussed for nourished beach design.

Journal of Coastal Research↗
USGS2021

Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles

Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in aquatic resource management decisions. General Lake Model (GLM) is a state-of-the-art physics-based model used for addressing such problems. However, like other physics-based models used for studying scientific and engineering systems, it has several well-known limitations due to simplified representations of the physical processes being modeled or challenges in selecting appropriate parameters. While state-of-the-art machine learning models can sometimes outperform physics-based models given ample amount of training data, they can produce results that are physically inconsistent. This article proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improves the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models (by over 20% even with very little training data), while generating outputs consistent with physical laws. An important aspect of our PGRNN approach lies in its ability to incorporate the knowledge encoded in physics-based models. This allows training the PGRNN model using very few true observed data while also ensuring high prediction accuracy. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines where physics-based (also known as mechanistic) models are used.

ACM/IMS Transactions on Data Science↗
USGS2017

A spatiotemporal clustering model for the Third Uniform California Earthquake Rupture Forecast (UCERF3‐ETAS): Toward an operational earthquake forecast

We, the ongoing Working Group on California Earthquake Probabilities, present a spatiotemporal clustering model for the Third Uniform California Earthquake Rupture Forecast (UCERF3), with the goal being to represent aftershocks, induced seismicity, and otherwise triggered events as a potential basis for operational earthquake forecasting (OEF). Specifically, we add an epidemic‐type aftershock sequence (ETAS) component to the previously published time‐independent and long‐term time‐dependent forecasts. This combined model, referred to as UCERF3‐ETAS, collectively represents a relaxation of segmentation assumptions, the inclusion of multifault ruptures, an elastic‐rebound model for fault‐based ruptures, and a state‐of‐the‐art spatiotemporal clustering component. It also represents an attempt to merge fault‐based forecasts with statistical seismology models, such that information on fault proximity, activity rate, and time since last event are considered in OEF. We describe several unanticipated challenges that were encountered, including a need for elastic rebound and characteristic magnitude–frequency distributions (MFDs) on faults, both of which are required to get realistic triggering behavior. UCERF3‐ETAS produces synthetic catalogs of M ≥2.5 events, conditioned on any prior M ≥2.5 events that are input to the model. We evaluate results with respect to both long‐term (1000 year) simulations as well as for 10‐year time periods following a variety of hypothetical scenario mainshocks. Although the results are very plausible, they are not always consistent with the simple notion that triggering probabilities should be greater if a mainshock is located near a fault. Important factors include whether the MFD near faults includes a significant characteristic earthquake component, as well as whether large triggered events can nucleate from within the rupture zone of the mainshock. Because UCERF3‐ETAS has many sources of uncertainty, as will any subsequent version or competing model, potential usefulness needs to be considered in the context of actual applications.

California↗
USGS2024

Assessing the population consequences of disturbance and climate change for the Pacific walrus

Climate change and anthropogenic disturbance are increasingly affecting wildlife at a global scale. Predicting how varying types and degrees of disturbance may interact to influence population dynamics is a key management challenge. Population consequences of disturbance (PCoD) models provide a framework to link effects of anthropogenic disturbance on an individual’s behavior and physiology to population-level changes. In the present study, we develop a Pacific walrus ( Odobenus rosmarus divergens ) PCoD model to encompass the population-level effects of both anthropogenic disturbance and climate change. As the Arctic becomes increasingly ice-free, walruses spend more time at coastal (vs. ice-based) haulouts, from which they must expend more energy to reach foraging areas and where they have an elevated risk of mortality. Concurrently, sea ice loss is increasing the anthropogenic footprint in the Arctic (e.g. fisheries, shipping, energy exploration), which creates additional disturbance. We applied the PCoD model to 4 scenarios (ranging from optimistic to pessimistic) which incorporate different global sea ice model projections along with varying degrees of anthropogenic disturbance. All scenarios indicated a decline in Pacific walrus vital rates by the end of the 21st century, but our results demonstrated that the intensity of that decline could be mitigated by global efforts to reduce carbon emissions, along with local management and conservation efforts to protect important coastal haulouts and foraging grounds. In summary, we introduce a flexible PCoD modeling framework in a novel context which will prove useful to researchers studying species threatened by rapid environmental change.

Marine Ecology Progress Series↗
USGS2012

Uncertainty

Management decisions will often be directly informed by model predictions. However, we now know there can be no expectation of a single ‘true’ model; thus, model results are uncertain. Understandable reporting of underlying uncertainty provides necessary context to decision-makers, as model results are used for management decisions. This, in turn, forms a mechanism by which groundwater models inform a risk-management framework because uncertainty around a prediction provides the basis for estimating the probability or likelihood of some event occurring. Given that the consequences of management decisions vary, it follows that the extent of and resources devoted to an uncertainty analysis may depend on the consequences. For events with low impact, a qualitative, limited uncertainty analysis may be sufficient for informing a decision. For events with a high impact, on the other hand, the risks might be better assessed and associated decisions made using a more robust and comprehensive uncertainty analysis. The purpose of this chapter is to provide guidance on uncertainty analysis through discussion of concepts and approaches, which can vary from heuristic (i.e. the modeller’s assessment of prediction uncertainty based on trial and error and experience) to a comprehensive, sophisticated, statistics-based uncertainty analysis. Most of the material presented here is taken from Doherty et al. (2010) if not otherwise cited. Although the treatment here is necessarily brief, the reader can find citations for the source material and additional references within this chapter.

Book chapter↗
USGS1999

Applications and issues of GIS as tool for civil engineering modeling

A tool that has proliferated within civil engineering in recent years is geographic information systems (GIS). The goal of a tool is to supplement ability and knowledge that already exists, not to serve as a replacement for that which is lacking. To secure the benefits and avoid misuse of a burgeoning tool, engineers must understand the limitations, alternatives, and context of the tool. The common benefits of using GIS as a supplement to engineering modeling are summarized. Several brief case studies of GIS modeling applications are taken from popular civil engineering literature to demonstrate the wide use and varied implementation of GIS across the discipline. Drawing from the case studies, limitations regarding traditional GIS data models find the implementation of civil engineering models within current GIS are identified and countered by discussing the direction of the next generation of GIS. The paper concludes by highlighting the potential for the misuse of GIS in the context of engineering modeling and suggests that this potential can be reduced through education and awareness. The goal of this paper is to promote awareness of the issues related to GIS-based modeling and to assist in the formulation of questions regarding the application of current GIS. The technology has experienced much publicity of late, with many engineers being perhaps too excited about the usefulness of current GIS. An undoubtedly beneficial side effect of this, however, is that engineers are becoming more aware of GIS and, hopefully, the associated subtleties. Civil engineers must stay informed of GIS issues and progress, but more importantly, civil engineers must inform the GIS community to direct the technology development optimally.

Journal of Computing in Civil Engineering↗
USGS2020

Flow‐ecology modelling to inform reservoir releases for riparian restoration and management

Linked hydrologic, hydraulic, and ecological models can facilitate planning and implementing water releases from reservoirs to achieve ecological objectives along rivers. We applied a flow‐ecology model, the Ecosystem Functions Model (HEC‐EFM), to the Bill Williams River in southwestern USA to estimate areas suitable for recruitment of riparian tree seedlings in the context of managing flow releases from a large dam for riparian restoration. Ecological variables in the model included timing of seed dispersal, tolerable rates of flow recession, and tolerable duration of inundation following germination and early seedling establishment for native Fremont cottonwood and Goodding's willow, and non‐native tamarisk. Hydrological variables included peak flow timing, rate of flow recession following the peak, and duration of inundation. A one‐dimensional hydraulic model was applied to estimate stage‐discharge relationships along ~58 river kilometres. We then used HEC‐EFM to apply relationships between seedling ecology and streamflow to link hydrological dynamics with ecological response. We developed and validated HEC‐EFM based on an examination of seedling recruitment following an experimental flow release from Alamo Dam in spring 2006. The model predicted the largest area of potential recruitment for cottonwood (280–481 ha), with smaller areas predicted for willow (174–188 ha) and tamarisk (59–60 ha). Correlations between observed and predicted patches with successful seedling recruitment for areas within 40 m of the main channel ranged from 0.66 to 0.94. Finally, we examined arrays of hydrographs to identify which are most conducive to seedling recruitment along the river, given different combinations of peak flow, recession rate, and water volume released. Similar application of this model could be useful for informing reservoir management in the context of riparian restoration along other rivers facing similar challenges.

Arizona↗
USGS2005

Evaluation of models and data for assessing whooping crane habitat in the central Platte River, Nebraska

The primary objectives of this evaluation were to improve the performance of the Whooping Crane Habitat Suitability model (C4R) used by the U.S. Fish and Wildlife Service (Service) for defining the relationship between river discharge and habitat availability, and to assist the Service in implementing improved model(s) with existing hydraulic files. The C4R habitat model is applied at the scale of individual river cross-sections, but the model outputs are scaledup to larger reaches of the river using a decision support “model” comprised of other data and procedures. Hence, the validity of the habitat model depends at least partially on how its outputs are incorporated into this larger context. For that reason, we also evaluated other procedures including the PHABSIM data files, the FORTRAN computer programs used to implement the model, and other parameters used to simulate the relationship between river flows and the availability of Whooping Crane roosting habitat along more than 100 miles of heterogeneous river channels. An equally important objective of this report was to fully document these related procedures as well as the model and evaluation results so that interested parties could readily understand the technical basis for the Service’s recommendations.

Nebraska↗
USGS2022

Climate refugia for Pinus spp. in topographic and bioclimatic environments of the Madrean sky islands of México and the United States

Climate refugia, or places where habitats are expected to remain relatively buffered from regional climate extremes, provide an important focus for science and conservation planning. Within high-priority, multi-jurisdictional landscapes like the Madrean sky islands of the United States and México, efforts to identify and manage climate refugia are hindered by the lack of high-quality and consistent transboundary datasets. To fill these data gaps, we assembled a bi-national field dataset ( n = 1416) for five pine species ( Pinus spp.) and used generalized boosted regression to model pine habitats in relation to topographic variability as a basis for identifying potential microrefugia at local scales in the context of current species’ distribution patterns. We developed additional models to quantify climatic refugial attributes using coarse scale bioclimatic variables and finer scale seasonal remote sensing indices. Terrain metrics including ruggedness, slope position, and aspect defined microrefugia for pines within elevation ranges preferred by each species. Response to bioclimatic variables indicated that small shifts in climate were important to some species (e.g., P. chihuahuana, P. strobiformis ), but others exhibited a broader tolerance (e.g., P. arizonica ). Response to seasonal climate was particularly important in modeling microrefugia for species with open canopy structure and where regular fires occur (e.g., P. engelmannii and P. chihuahuana ). Hotspots of microrefugia differed among species and were either limited to northern islands or occurred across central or southern latitudes. Mapping and validation of refugia and their ecological functions are necessary steps in developing regional conservation strategies that cross jurisdictional boundaries. A salient application will be incorporation of climate refugia in management of fire to restore and maintain pine ecology.

Arizona, Chihuahua, New Mexico, Sonora↗