Giselle Block | Geology Reports

USGS2024

Developing a range-wide sampling framework for endangered species: A case study with light-footed Ridgway’s rail

Monitoring provides the foundation for evaluating recovery of endangered species, yet many species lack monitoring programs designed to integrate a species’ unique attributes, specific monitoring objectives, and principles of statistical sampling theory. We developed a framework for monitoring and assessment of endangered light-footed Ridgway’s rails ( Rallus obsoletus levipes ) across their U.S. range, relative to multi-scale recovery goals. We created spatially explicit sample units and a sampling frame covering all potential habitat to facilitate range-wide probability sampling, and also built a model of the call-broadcast process commonly used to survey marsh birds that included heterogeneity in availability for detection and conditional detectability for each bird during each survey. We used the model to simulate 96 sampling strategies that included different levels of replication, multiple approaches for sample allocation amongst strata, and both simple random and weighted probability sampling (i.e., weights proportional to local rail abundance) of sample units within strata. Effective monitoring surveyed ≥20–30% of the sampling frame on ≥3 occasions, with weighted sample selection and more targeted sampling (50% of units) for strata that are key to species recovery. We also tested Bayesian N-mixture models for estimating abundance and show that multiple models provide reasonable estimates. This work lays the foundation for statistical sampling and multi-scale population estimation for an endangered bird, and for refinement of abundance estimation models. Moreover, this work provides a replicable process for building customized and statistically defensible sampling frameworks to assess recovery of endangered species that can used for other sensitive species.

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USGS2015

Collaborative decision-analytic framework to maximize resilience of tidal marshes to climate change

Decision makers that are responsible for stewardship of natural resources face many challenges, which are complicated by uncertainty about impacts from climate change, expanding human development, and intensifying land uses. A systematic process for evaluating the social and ecological risks, trade-offs, and cobenefits associated with future changes is critical to maximize resilience and conserve ecosystem services. This is particularly true in coastal areas where human populations and landscape conversion are increasing, and where intensifying storms and sea-level rise pose unprecedented threats to coastal ecosystems. We applied collaborative decision analysis with a diverse team of stakeholders who preserve, manage, or restore tidal marshes across the San Francisco Bay estuary, California, USA, as a case study. Specifically, we followed a structured decision-making approach, and we using expert judgment developed alternative management strategies to increase the capacity and adaptability to manage tidal marsh resilience while considering uncertainties through 2050. Because sea-level rise projections are relatively confident to 2050, we focused on uncertainties regarding intensity and frequency of storms and funding. Elicitation methods allowed us to make predictions in the absence of fully compatible models and to assess short- and long-term trade-offs. Specifically we addressed two questions. (1) Can collaborative decision analysis lead to consensus among a diverse set of decision makers responsible for environmental stewardship and faced with uncertainties about climate change, funding, and stakeholder values? (2) What is an optimal strategy for the conservation of tidal marshes, and what strategy is robust to the aforementioned uncertainties? We found that when taking this approach, consensus was reached among the stakeholders about the best management strategies to maintain tidal marsh integrity. A Bayesian decision network revealed that a strategy considering sea-level rise and storms explicitly in wetland restoration planning and designs was optimal, and it was robust to uncertainties about management effectiveness and budgets. We found that strategies that avoided explicitly accounting for future climate change had the lowest expected performance based on input from the team. Our decision-analytic framework is sufficiently general to offer an adaptable template, which can be modified for use in other areas that include a diverse and engaged stakeholder group.

California↗