Geology topics

Jay E. Diffendorfer

Publications and source records attributed to Jay E. Diffendorfer.

At least 37 records · Page 2Linked to original sources

USGS2024

The interplay of future solar energy, land cover change, and their projected impacts on natural lands and croplands in the US

Projections for deep decarbonization require large amounts of solar energy, which may compete with other land uses such as agriculture, urbanization, and conservation of natural lands. Existing capacity expansion models do not integrate land use land cover change (LULC) dynamics into projections. We explored the interaction between projected LULC, solar photovoltaic (PV) deployment, and solar impacts on natural lands and croplands by integrating projections of LULC with a model that can project future deployment of solar PV with high spatial resolution for the conterminous United States. We used scenarios of LULC projections from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios from 2010 to 2050 and two electricity grid scenarios to model future PV deployment and compared those results against a baseline that held 2010 land cover constant through 2050. Though solar PV's overall technical potential was minimally impacted by LULC scenarios, deployed PV varied by −16.5 to 11.6 % in 2050 from the baseline scenario. Total land requirements for projected PV were similar to other studies, but measures of PV impacts on natural systems depended on the underlying land change dynamics occurring in a scenario. The solar PV deployed through 2050 resulted in 1.1 %–2.4 % of croplands and 0.3 %–0.7 % of natural lands being converted to PV. However, the deepest understanding of PV impacts and interactions with land cover emerged when the complete net gains and losses from all land cover change dynamics, including PV, were integrated. For example, one of the four LULC projections allows for high solar development and a net gain in natural lands, even though PV drives a larger percentage of natural land conversion. This paper shows that integrating land cover change dynamics with energy expansion models generates new insights into trade offs between decarbonization, impacts of renewables, and ongoing land cover change.

conterminous United States↗

USGS2024

Urban tree cover provides consistent mitigation of extreme heat in arid but not humid cities

Urban land cover types influence the urban microclimates. However, recent work indicates the magnitude of land cover's microclimate influence is affected by aridity. Moreover, this variation in cooling and warming potentials of urban land cover types can substantially alter the exposure of urban areas to extreme heat. Our goal is to understand both the relative influences of urban land cover on local air temperature, as well as how these influences vary during periods of extreme heat. To do so we apply predictive machine learning models to an extensive in-situ microclimate and 1 m land cover dataset across eight U.S. cities spanning a wide aridity gradient during typical and extreme heat conditions. We demonstrate how the cooling influence of tree canopy and the warming influence of buildings on microclimate linearly scales with regional aridity, while the influence of turf and impervious surfaces does not. These interactions lead tree canopy to consistently mitigate to air temperature increases during periods extreme heat in arid cities, while the influence of urban tree canopy on extreme heat in humid regions is varied, suggesting that mitigation is possible, but tree canopy can also aggravate extreme heat or have no significant effect.

Sustainable Cities and Society↗

USGS2023

Georectified polygon database of ground-mounted large-scale solar photovoltaic sites in the United States

Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 MWdc or more. Ground mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. Analysts from U.S. Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 megawatt direct current (MW dc ) or more. Ground-mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. The United States Large-Scale Solar Photovoltaic Database (USPVDB) was developed to fill this gap. Using US Energy Information Administration (EIA) data, locations of 3,699 LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available via an interactive web application and multiple downloadable formats, including: comma-separated value (CSV), application programming interface (API), and GIS shapefile and GeoJSON. Survey and Lawrence Berkeley National Laboratory collaborated to develop the United States Large-Scale Solar Photovoltaic Database (USPVDB). Using Energy Information Administration (EIA) data, locations of LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available in an interactive web application, and a number of downloadable formats, including: comma-separated value spreadsheet (CSV), application programming interface (API), and GIS shapefile.

Scientific Data↗

USGS2023

The benefits of big-team science for conservation: Lessons learned from trinational monarch butterfly collaborations

Many pressing conservation issues are complex problems caused by multiple social and environmental drivers; their resolution is aided by interdisciplinary teams of scientists, decision makers, and stakeholders working together. In these situations, how do we generate science to effectively guide conservation (resource management and policy) decisions? This paper describes elements of successful big-team science in conservation, as well as shortcomings and lessons learned, based on our work with the monarch butterfly ( Danaus plexippus ) in North America. We summarize literature on effective science teams, extracting information about elements of success, effective implementation approaches, and barriers or pitfalls. We then describe recent and ongoing conservation science for the monarch butterfly in North America. We focus primarily on the activities of the Monarch Conservation Science Partnership–an international collaboration of interdisciplinary scientists, policy experts and natural resource managers spanning government, non-governmental and academic institutions—which developed science to inform imperilment status, recovery options, and monitoring strategies. We couch these science efforts in the adaptative management framework of Strategic Habitat Conservation, the business model for conservation employed by the US Fish and Wildlife Service to inform decision-making needs identified by stakeholders from Canada, the United States, and Mexico. We conclude with elements critical to effective big-team conservation science, discuss why science teams focused on applied conservation problems are unique relative to science teams focusing on traditional or theoretical research, and list benefits of big team science in conservation.

Frontiers in Environmental Science↗

USGS2023

Potential economic consequences along migratory flyways from reductions in breeding habitat of migratory waterbirds

The migration of species, often across continents, makes it difficult to quantify the cumulative effects of local- and regional-scale conservation actions. Further, variation in stakeholder interests, differing jurisdictional governance processes, priorities, and monitoring abilities across the migratory range shapes place-specific differences in management actions. These differences may lead management of migratory species to benefit both species and stakeholders in some places more than others. In the case of North American waterfowl, possible reduction of wetland protection in breeding areas may lead to substantive shifts in benefits among stakeholders across their range by adversely affecting recreational viewing and hunting opportunities for these species. To understand possible consequences of wetland loss in the U.S. Prairie Pothole Region, the breeding region for 12 focal species of waterfowl, on the recreation economics for these species, we modeled a causal pathway linking wetland loss in the breeding grounds to changes in duck abundance and then assessed the consequences of that change in abundance on recreational hunting and viewing within migratory flyways. Under a scenario where wetland protections cease, we find annual economic activity associated with recreation may decrease as much as \$489 million at the highest levels of predicted wetland loss, the majority of it coming from impacts to viewing behavior in the Mississippi Flyway. The number of hunters may decline by as much as 18,000, leading to \$32 million less in annual economic activity. At highest levels of wetland loss, viewing value is expected to decline by more than one-quarter. Lost economic value associated with reductions in recreation in the Mississippi and Central Flyway states is not likely to be overcome by increases in agricultural economic output in drained wetlands of the Prairie Pothole Region. Our analyses indicate local effects of national water policies likely have far-reaching consequences because of the multi-dimensional connections arising from place-specific differences in management action, global and national agricultural economic drivers of crop expansion, and the biotic phenomena of transcontinental avian migration. Reductions in habitat in one location could ramify to economic consequences throughout the continent through connections fostered by migrating waterfowl.

Biological Conservation↗

USGS2023

Bridging the gap between mathematical biology and undergraduate education using applicable natural resource modeling

Mathematical biology is a wide field of study with many venues that undergraduate students can access through research. However, the topics of study for these students can be overwhelming, and many topics of study yield either only trivial results or abstract outcomes that are nonintuitive and diffcult to understand. We have used natural resource modeling, and more specifically, a partnership between academic researchers and federal scientists, as a bridge between undergraduate research and mathematical biology. Our collaboration is an interdisciplinary team that combines biology, mathematics, and statistics professors with government research scientists. As a team, we have mentored students through opportunities such as a Research Experiences for Undergraduates and other projects. In this article, we provide an overview of how we develop questions for undergraduates and outline two case studies, both of which resulted in peer reviewed journal articles. Last, we describe how we also transfer the results from these undergraduate projects to resource managers so the results may be applied to real world problems.

Letters in Biomathematics↗

USGS2022

Wind turbine wakes can impact down-wind vegetation greenness

Global wind energy has expanded 5-fold since 2010 and is predicted to expand another 8–10-fold over the next 30 years. Wakes generated by wind turbines can alter downwind microclimates and potentially downwind vegetation. However, the design of past studies has made it difficult to isolate the impact of wake effects on vegetation from land cover change. We used hourly wind data to model wake and non-wake zones around 17 wind facilities across the U.S. and compared remotely-sensed vegetation greenness in wake and non-wake zones before and after construction. We located sampling sites only in the dominant vegetation type and in areas that were not disturbed before or after construction. We found evidence for wake effects on vegetation greenness at 10 of 17 facilities for portions of, or the entire growing season. Evidence included statistical significance in Before After Control Impact statistical models, differences >3% between expected and observed values of vegetation greenness, and consistent spatial patterns of anomalies in vegetation greenness relative to turbine locations and wind direction. Wakes induced both increases and decreases in vegetation greenness, which may be difficult to predict prior to construction. The magnitude of wake effects depended primarily on precipitation and to a lesser degree aridity. Wake effects did not show trends over time following construction, suggesting the changes impact vegetation greenness within a growing season, but do not accrue over years. Even small changes in vegetation greenness, similar to those found in this study, have been seen to affect higher trophic levels. Given the rapid global growth of wind energy, and the importance of vegetation condition for agriculture, grazing, wildlife, and carbon storage, understanding how wakes from wind turbines impact vegetation is essential to exploit or ameliorate these effects.

Environmental Research Letters↗

USGS2022

Using ecosystem services to identify inequitable outcomes in migratory species conservation

Biodiversity conservation efforts have been criticized for generating inequitable socio-economic outcomes. These equity challenges are largely analyzed as place-based problems affecting local communities directly impacted by conservation programs. The conservation of migratory species extends this problem geographically since people in one place may benefit while those in another bear the costs of conservation. The spatial subsidies approach offers an effective tool for analyzing such relationships between places connected by migratory species. Designed to quantify ecosystem services provided and received in specific locations across a migratory species’ range—and the disparities between them—the spatial subsidies approach highlights three axes of inequity: between indigenous and settler colonial societies, between urban and rural populations, and between the Global North and Global South. Recognizing these relationships is critical to achieving two mutually reinforcing policy goals: avoiding inequitable conservation outcomes in efforts to conserve migratory species, and ensuring effective long-term conservation of migratory species. In demonstrating how the spatial subsidies approach enables the identification and quantification of inequities involving three migratory species (northern pintail ducks, monarch butterflies, and Mexican free-tailed bats), we argue that a spatial subsidies approach could apply to migratory species conservation efforts worldwide under the context of “payments for ecosystem services.”

Conservation Letters↗

USGS2022

Counterfactuals to assess effects to species and systems from renewable energy development

Renewable energy production, mostly via wind, solar, and biofuels, is central to goals worldwide to reduce carbon emissions and mitigate anthropogenic climate change (IPCC, 2014; Pörtner et al., 2021). Nevertheless, adverse impacts to natural systems, especially fatalities of wildlife and alteration of habitat, are key challenges for renewable energy production (Allison et al., 2019; Katzner et al., 2019). Because of the magnitude of these challenges, extensive effort has been invested in surveys and science to understand the environmental effects of renewable energy on species and systems. Nevertheless, these impacts have not been formally compared relative to counterfactual conditions (Bull et al., 2021; Coetzee and Gaston, 2021), i.e., those occurring in the absence of renewable energy. As such, cumulative ecological impact assessments required by many regulating agencies typically only consider the adverse impacts of renewables, without evaluating whether mitigative effects of current and planned build-out (e.g., Larson et al., 2020) will offset their adverse impacts to species and natural systems (Allison et al., 2014). Accordingly, these critical decision processes have an insufficient perspective to foster fully informed decisions, and, for some species or systems, renewable energy could lead to more profound impacts than those it is intended to prevent. Furthermore, because of this approach and, despite the well-studied benefits to society of renewable energy development (IPCC, 2014), the ecological value of renewable energy is often premised on the plausible but untested assumption that its negative effects to natural populations and systems are less consequential than the negative effects in alternative scenarios with less renewable energy and greater climate change. A more comprehensive framing of the counterfactual in cumulative ecological impact assessments would evaluate, for each species or system, the incremental effects of renewables over their full life cycle against the incremental effects they provide by mitigating climate change. This framing is important because a given species or system may see net positive or net negative effects from either renewables or climate change. Furthermore, cumulative impact assessments could identify optimized tradeoffs that balance, for each species or system, the effects of both climate change and renewable energy.

Frontiers in Conservation Science↗

USGS2022

Vulnerability of avian populations to renewable energy production

Renewable energy production can kill individual birds, but little is known about how it affects avian populations. We assessed the vulnerability of populations for 23 priority bird species killed at wind and solar facilities in California, USA. Bayesian hierarchical models suggested that 48% of these species were vulnerable to population-level effects from added fatalities caused by renewables and other sources. Effects of renewables extended far beyond the location of energy production to impact bird populations in distant regions across continental migration networks. Populations of species associated with grasslands where turbines were located were most vulnerable to wind. Populations of nocturnal migrant species were most vulnerable to solar, despite not typically being associated with deserts where the solar facilities we evaluated were located. Our findings indicate that addressing declines of North American bird populations requires consideration of the effects of renewables and other anthropogenic threats on both nearby and distant populations of vulnerable species.

California↗

USGS2022

Multi-species, multi-country analysis reveals North Americans are willing to pay for transborder migratory species conservation

Migratory species often provide ecosystem service benefits to people in one country while receiving habitat support in other countries. The multinational cooperation that could help ensure continued provisioning of these benefits by migration may be informed by understanding the economic values people in different countries place on the benefits they derive from migratory wildlife. We conducted contingent valuation surveys to estimate the willingness of 3733 respondents from Canada, the United States and México to invest in conservation for two disparate migratory species, the northern pintail duck Anas acuta and the Mexican free-tailed bat Tadarida brasiliensis mexicana . With zero-inflated mixed-effects negative binomial regression (explaining 87% of the variation in willingness to pay for conservation), we found that respondents from each nation, after controlling for both household income and per capita national Gross Domestic Product, were willing to invest in conservation in other countries. This willingness to pay for conservation, even when respondents knew that funds would be used to support benefits accruing primarily in other countries, demonstrates the potential for support of multinational conservation policies and programmes that direct resources to locations where the most critical habitat is located, rather than where the funding is generated. These findings could be used to support the development or expansion of new and existing international conservation programmes for migratory species.

People and Nature↗

USGS2022

Urban landcover differentially drives day and nighttime air temperature across a semi-arid city

Semi-arid urban environments are undergoing an increase in both average air temperatures and in the frequency and intensity of extreme heat events. Within cities, different composition and densities of urban landcovers (ULC) influence local air temperatures, either mitigating or increasing heat. Currently, understanding how combinations of ULC influence air temperature at the block to neighborhood scale is necessary for heat mitigation plans, and yet limited due to the complexities integrating high-resolution ULC with spatial and temporally high-resolution microclimate data. We quantify how ULC influences air temperature at 60 m resolution for day and nighttime climate normals and extreme heat conditions by integrating microclimate sensor data sensor and high-resolution (1 m 2 ) ULC for Denver, Colorado's urban core. We derive ULC drivers of air temperature using a structural equation model, then use a random forest algorithm to predict air temperatures for 30-year climate normals and an extreme heat condition. We find that, in conjunction with other ULC, urban tree canopy reduces daytime air temperatures (−0.026 °C per % cover), and the combination of impervious surfaces and buildings increases daytime air temperature (0.021 °C per % cover). Compared to daytime hours, nighttime irrigated turf temperature cooling effects are increased from being non-significant to −0.022 °C per % cover, while tree canopy effects are reduced from −0.026 °C during the day to −0.016 °C at night. Overall, ULC drives ~17% and 25% of local air temperature during the day and night, respectively. ULC influence on daytime air temperatures is altered in extreme heat events, both depending on the ULC type and time of day. Our findings inform urban planners seeking to identify potential hot and cool spots within a semi-arid city and mitigate high urban air temperatures through using ULC within larger urban climate mitigation strategies.

Colorado↗

USGS2021

Demographic and potential biological removal models identify raptor species sensitive to current and future wind energy

A central challenge in applied ecology is understanding the effect of anthropogenic fatalities on wildlife populations and predicting which populations may be particularly vulnerable and in greatest need of management attention. We used 3 approaches to investigate potential effects of fatalities from collisions with wind turbines on 14 raptor species for both current (106 GW) and anticipated future (241 GW) levels of installed wind energy capacity in the United States. Our goals were to identify species at relatively high vs low risk of experiencing population declines from turbine collisions and to also compare results generated from these approaches. Two of the approaches used a calculated turbine-caused mortality rate to decrement population growth, where population trends were derived either from the North American Breeding Bird Survey or a matrix model parameterized from literature-derived demographic values. The third approach was potential biological removal, which estimates the number of fatalities that allow a population to reach and maintain its optimal sustainable population set by management concerns. Different results among the methods reveal substantial gaps in knowledge and uncertainty in both demographic parameters and species-specific estimates of fatalities from wind turbines. Our results suggest that, of the 14 species studied, those with relatively higher potential of population-level impacts from wind turbine collisions included barn owl, ferruginous hawk, golden eagle, American kestrel, and red-tailed hawk. Burrowing owl, Cooper’s hawk, great horned owl, northern harrier, turkey vulture, and osprey had a relatively lower potential for population impacts, and results were not easily interpretable for merlin, prairie falcon, and Swainson’s hawk. Projections of current levels of fatalities to future wind energy scenarios at 241 GW of installed capacity suggest some species could experience population declines because of turbine collisions. Populations of those species may benefit from research to identify tools to prevent or reduce raptor collisions with wind turbines.

Ecosphere↗

USGS2021

TrendPowerTool: A lookup tool for estimating the statistical power of a monitoring program to detect population trends

A simulation-based power analysis can be used to estimate the sample sizes needed for a successful monitoring program, but requires technical expertise and sometimes extensive computing resources. We developed a web-based lookup app, called TrendPowerTool ( https://www.usgs.gov/apps/TrendPowerTool/ ), to provide guidance for ecological monitoring programs when resources are not available for a simulation-based power analysis. TrendPowerTool is implemented through the shiny package in R, but is accessible through a webpage without the need for users to install any software. By drawing on results of 1.4 million scenarios that we simulated on a supercomputer, TrendPowerTool quickly and easily provides an estimate of the statistical power to detect a population trend of a particular magnitude with a planned monitoring program, based on user-specified parameters for the monitoring design and population of interest. TrendPowerTool provides a user-friendly interface that retrieves results instantaneously, facilitating the important step of conducting a power analysis when designing monitoring programs.

Conservation Science and Practice↗

USGS2020

Editorial: North American monarch butterfly ecology and conservation

Spanning Canada, the United States, and Mexico, North America contains two populations of the migratory monarch butterfly ( Danaus plexippus ). The smaller “western” population overwinters in groves along the California coast and breeds west of the Rocky Mountains, while the much larger “eastern” population breeds east of the Rocky Mountains and overwinters in Oyamel fir forests in central Mexico. Both populations have declined in the last 20 to 30 years, leading to a formal petition in 2014 to list the species as threatened or endangered under the US Endangered Species Act (ESA) and a recommendation in 2016 for listing as endangered under the Canadian Species at Risk act.

Frontiers in Ecology and Conservation↗

USGS2020

Limitations, lack of standardization, and recommended best practices in studies of renewable energy effects on birds and bats

Increasing global energy demand is fostering the development of renewable energy as an alternative to fossil fuels. However, renewable energy facilities may adversely affect wildlife. Facility siting guidelines recommend or require project developers complete pre‐ and postconstruction wildlife surveys to predict risk and estimate effects of proposed projects. Despite this, there are no published studies that have quantified the types of surveys used or how survey types are standardized within and across facilities. We evaluated 628 peer‐reviewed publications, unpublished reports, and citations, and we analyzed data from 525 of these sources (203 facilities: 193 wind and 10 solar) in the United States and Canada to determine the frequency of pre‐ and postconstruction surveys and whether that frequency changed over time; frequency of studies explicitly designed to allow before‐after or impact‐control analyses; and what types of survey data were collected during pre‐ and postconstruction periods and how those data types were standardized across periods and among facilities. Within our data set, postconstruction monitoring for wildlife fatalities and habitat use was a standard practice ( n = 446 reports), but preconstruction estimation of baseline wildlife habitat use and mortality was less frequently reported ( n = 84). Only 22% ( n = 45) of the 203 facilities provided data from both pre‐ and postconstruction, and 29% ( n = 59) had experimental study designs. Of 108 facilities at which habitat‐use surveys were conducted, only 3% estimated of detection probability. Thus, the available data generally preclude comparison of biological data across construction periods and among facilities. Use of experimental study designs and following similar field protocols would improve the knowledge of how renewable energy affects wildlife.

Conservation Biology↗

USGS2020

Assessing population-level consequences of anthropogenic stressors for terrestrial wildlife

Human activity influences wildlife. However, the ecological and conservation significances of these influences are difficult to predict and depend on their population‐level consequences. This difficulty arises partly because of information gaps, and partly because the data on stressors are usually collected in a count‐based manner (e.g., number of dead animals) that is difficult to translate into rate‐based estimates important to infer population‐level consequences (e.g., changes in mortality or population growth rates). However, ongoing methodological developments can provide information to make this transition. Here, we synthesize tools from multiple fields of study to propose an overarching, spatially explicit framework to assess population‐level consequences of anthropogenic stressors on terrestrial wildlife. A key component of this process is using ecological information from affected animals to upscale from count‐based field data on individuals to rate‐based demographic inference. The five steps to this framework are (1) framing the problem to identify species, populations, and assessment parameters; (2) field‐based measurement of the effect of the stressor on individuals; (3) characterizing the location and size of the populations of interest; (4) demographic modeling for those populations; and (5) assessing the significance of stressor‐induced changes in demographic rates. The tools required for each of these steps are well developed, and some have been used in conjunction with each other, but the entire group has not previously been unified together as we do in this framework. We detail these steps and then illustrate their application for two species affected by different anthropogenic stressors. In our examples, we use stable hydrogen isotope data to infer a catchment area describing the geographic origins of affected individuals, as the basis to estimate population size for that area. These examples reveal unexpectedly greater potential risks from stressors for the more common and widely distributed species. This work illustrates key strengths of the framework but also important areas for subsequent theoretical and technical development to make it still more broadly applicable.

Ecosphere↗

USGS2020

Is your ad hoc model selection strategy affecting your multimodel inference?

(Yackulic) 1. Ecologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages in which certain features of a model are compared while other features are held constant. Defining these multi-stage strategies requires making a series of decisions, which may potentially impact inferences, but have not been critically evaluated. 2. We begin by identifying key features of strategies, introducing descriptive terms when they did not already exist in the literature. Strategies differ in how they define and order model building stages. Sequential-by-sub-model strategies focus on one sub-model (parameter) at a time with modeling of subsequent sub-models dependent on the selected model structures from the previous stages. Secondary candidate set strategies model sub-models independently and combine the top set of models from each sub-model for selection in a final stage. Build-up approaches define stages across sub-models and increase in complexity at each stage. Strategies also differ in how the top set of models is selected in each stage and whether they use null or more complex model structures for non-target sub-models. 3. We tested the performance of different model selection strategies using four datasets and three model types. For each dataset, we determined the “true” distribution of AIC weights by fitting all plausible models. Then, we calculated the number of models that would have been fitted and the portion of “true” AIC weight we recovered under different model selection strategies. 4. Sequential-by-sub-model strategies often performed poorly. Build-up or secondary candidate sets were more reliable, provided all models within 5 AIC of the top model were carried forward to subsequent stages. The structure of non-target sub-models was less important. 5. Multi-stage approaches cannot compensate for a lack of critical thought in selecting covariates and building models to represent competing a priori hypotheses. However, even when competing hypotheses for different sub-models are limited, thousands or more models may be possible so strategies to explore candidate model space reliably and efficiently will be necessary.

Ecosphere↗