Geology topics

D. Todd Jones-Farrand

Publications and source records attributed to D. Todd Jones-Farrand.

6 recordsLinked to original sources

USGS2025

Toward a shared vision for climate-informed resource stewardship

Climate-change adaptation planning processes and tools are increasing in number and evolving rapidly. During times of innovation and proliferation, a potential danger is incoherence, when well-intended contributions can overwhelm, create confusion, or mask complementarities. A shared vision is needed to avoid duplication, reduce misunderstandings, and facilitate work across jurisdictions to steward resources undergoing profound changes. Such a vision would document fundamental tools and approaches while allowing flexibility to match highly varied management contexts and organizational missions. Fortunately, the preconditions for coherence exist. Here, we illustrate how climate-change adaptation tools—including scenario planning, conceptual frameworks, structured decision making, and impact-evaluation methods—can be (and are) used in tandem to support adaptation planning that accounts for uncertainty, considers a broad range of strategies and actions, makes transparent and robust choices, evaluates outcomes, and is flexible and responsive to the decision context.

Frontiers in Ecology and the Environment↗

USGS2024

Sea turtle density surface models along the United States Atlantic coast

Spatially explicit estimates of marine species distribution and abundance are required to quantify potential impacts from human activities such as military training and testing, fisheries interactions, and offshore energy development. There are 4 protected species of sea turtle (loggerhead, green, Kemp’s ridley, and leatherback) commonly found along the east coast of the USA, our study area, and which require impact assessments. Data from 7 different survey organizations were used to create density surface models for the 4 sea turtle species utilizing 1.2 million km of line-transect surveys. A substantial portion (29.7%) of available sightings were not identified to the species level. Not including these sightings would underestimate density, so a conditional random forest model was used to assign unidentified sightings to species. Higher densities of loggerhead, green, and Kemp’s ridley sea turtles were predicted south of the Outer Banks in cool months, transitioning northwards in late spring to occupy seasonal neritic habitats. The highest leatherback densities were predicted off the coasts of Georgia and Florida. Leatherbacks were also predicted throughout offshore areas. The predicted distribution patterns generally matched satellite tracking and strandings data, indicating the models reproduced established seasonal movements. Surveys rarely detect sea turtles smaller than 40 cm, so these age classes are not represented. The models are the first for the study area to apply availability bias estimates developed in or near the study area and attempt to classify unidentified sightings to the species level, providing an updated, critical tool for conservation management along the eastern seaboard.

Connecticut, Delaware, Florida, Georgia, Maine, Ma↗

USGS2024

Using resiliency, redundancy, and representation in a Bayesian belief network to assess imperilment of riverine fishes

Conservation prioritization frameworks are used worldwide to identify species at greatest risk of extinction and to allocate limited resources across regions, species, and populations. Conservation prioritization can be impeded by ecological knowledge gaps and data deficiency, especially in freshwater species inhabiting highly complex aquatic ecosystems. Therefore, we developed a flexible approach that calculates a species' imperilment risk based on the conservation principles of resiliency, redundancy, and representation (i.e., the “three R's”). Our approach organizes data on species traits, distributions, population connectivity, and threats within a Bayesian belief network capable of predicting resiliency and redundancy within representative ecological settings. Empirical data and expert judgment inform the model to provide robust and repeatable risk assessments for rare and data-deficient species. The model calculates resiliency at hierarchical spatial scales from distributional trends and population strength. Redundancy is estimated from the connectivity and quantities of extant populations. Resiliency, redundancy, and species' inherent vulnerability based on species traits collectively estimate extirpation risk within each unique ecological setting. Extirpation risks across ecological settings characterize representation and are aggregated to estimate global imperilment risk. We demonstrate the model's utility with Piebald Madtom ( Noturus gladiator ), a species petitioned for listing under the U.S. Endangered Species Act. Our results revealed that resiliency, redundancy, and extirpation risks can vary spatially across the species' range while identifying populations where additional sampling could disproportionally reduce uncertainty in estimated global imperilment risk. Our approach could standardize and expedite conservation status assessments, identify opportunities for early management intervention of at-risk species and populations, and strategically reduce uncertainty by focusing monitoring and research on priority information gaps.

Ecosphere↗

USGS2019

Developing a decision-support process for landscape conservation design

Planning for sustainable landscapes is hampered by uncertainty in how species will respond to conservation actions amidst impacts from landscape and climate change. Planning decisions, including tradeoffs among competing species objectives, are complex. We developed a decision-support framework that integrates dynamic-landscape metapopulation models (DLMPs) and structured decision making (SDM) to help guide landscape conservation design. With this framework, we demonstrated that planning for viable populations across broad scales can be achieved under global change. Furthermore, the integration of DLMPs with SDM enabled decisions to be more objective and transparent, and thus, more defensible.

Report↗

USGS2011

Comparison of statistical and theoretical habitat models for conservation planning: The benefit of ensemble prediction

Selection of a modeling approach is an important step in the conservation planning process, but little guidance is available. We compared two statistical and three theoretical habitat modeling approaches representing those currently being used for avian conservation planning at landscape and regional scales: hierarchical spatial count (HSC), classification and regression tree (CRT), habitat suitability index (HSI), forest structure database (FS), and habitat association database (HA). We focused our comparison on models for five priority forest-breeding species in the Central Hardwoods Bird Conservation Region: Acadian Flycatcher, Cerulean Warbler, Prairie Warbler, Red-headed Woodpecker, and Worm-eating Warbler. Lacking complete knowledge on the distribution and abundance of each species with which we could illuminate differences between approaches and provide strong grounds for recommending one approach over another, we used two approaches to compare models: rank correlations among model outputs and comparison of spatial correspondence. In general, rank correlations were significantly positive among models for each species, indicating general agreement among the models. Worm-eating Warblers had the highest pairwise correlations, all of which were significant ( P , 0.05). Red-headed Woodpeckers had the lowest agreement among models, suggesting greater uncertainty in the relative conservation value of areas within the region. We assessed model uncertainty by mapping the spatial congruence in priorities (i.e., top ranks) resulting from each model for each species and calculating the coefficient of variation across model ranks for each location. This allowed identification of areas more likely to be good targets of conservation effort for a species, those areas that were least likely, and those in between where uncertainty is higher and thus conservation action incorporates more risk. Based on our results, models developed independently for the same purpose (conservation planning for a particular species in a particular geography) yield different answers and thus different conservation strategies. We assert that using only one habitat model (even if validated) as the foundation of a conservation plan is risky. Using multiple models (i.e., ensemble prediction) can reduce uncertainty and increase efficacy of conservation action when models corroborate one another and increase understanding of the system when they do not.

Ecological Applications↗

USGS2010

Change in avian abundance predicted from regional forest inventory data

An inability to predict population response to future habitat projections is a shortcoming in bird conservation planning. We sought to predict avian response to projections of future forest conditions that were developed from nationwide forest surveys within the Forest Inventory and Analysis (FIA) program. To accomplish this, we evaluated the historical relationship between silvicolous bird populations and FIA-derived forest conditions within 25 ecoregions that comprise the southeastern United States. We aggregated forest area by forest ownership, forest type, and tree size-class categories in county-based ecoregions for 5 time periods spanning 1963-2008. We assessed the relationship of forest data with contemporaneous indices of abundance for 24 silvicolous bird species that were obtained from Breeding Bird Surveys. Relationships between bird abundance and forest inventory data for 18 species were deemed sufficient as predictive models. We used these empirically derived relationships between regional forest conditions and bird populations to predict relative changes in abundance of these species within ecoregions that are anticipated to coincide with projected changes in forest variables through 2040. Predicted abundances of these 18 species are expected to remain relatively stable in over a quarter (27%) of the ecoregions. However, change in forest area and redistribution of forest types will likely result in changed abundance of some species within many ecosystems. For example, abundances of 11 species, including pine warbler (Dendroica pinus), brown-headed nuthatch (Sitta pusilla), and chuckwills- widow (Caprimulgus carolinensis), are projected to increase within more ecoregions than ecoregions where they will decrease. For 6 other species, such as blue-winged warbler (Vermivora pinus), Carolina wren (Thryothorus ludovicianus), and indigo bunting (Passerina cyanea), we projected abundances will decrease within more ecoregions than ecoregions where they will increase.

Forest Ecology and Management↗