No abstract

Demographic rates are rarely estimated over an entire species range, limiting empirical tests of ecological patterns and theories, and raising questions about the representativeness of studies that use data from a small part of a range. The uncertainty that results from using demographic rates from just a few sites is especially pervasive in population projections, which are critical for a wide range of questions in ecology and conservation. We developed a simple simulation to quantify how this lack of geographic representativeness can affect inferences about the global mean and variance of growth rates, which has implications for the robust design of a wide range of population studies. Using a coastal songbird, saltmarsh sparrow Ammodramus caudacutus, as a case study, we first estimated survival, fecundity, and population growth rates at 21 sites distributed across much of their breeding range. We then subsampled this large, representative dataset according to five sampling scenarios in order to simulate a variety of geographic biases in study design. We found spatial variation in demographic rates, but no large systematic patterns. Estimating the global mean and variance of growth rates using subsets of the data suggested that at least 10–15 sites were required for reasonably unbiased estimates, highlighting how relying on demographic data from just a few sites can lead to biased results when extrapolating across a species range. Sampling at the full 21 sites, however, offered diminishing returns, raising the possibility that for some species accepting some geographical bias in sampling can still allow for robust range‐wide inferences. The subsampling approach presented here, while conceptually simple, could be used with both new and existing data to encourage efficiency in the design of long‐term or large‐scale ecological studies.

The management of wintering North American waterfowl is based on the premise that the amount of foraging habitat can limit populations. To estimate carrying capacity of winter habitats, managers use bioenergetic models to quantify energy (food) availability and energy demand, and use results as planning tools to meet regional conservation objectives. Regional models provide only coarse estimates of carrying capacity because habitat area, habitat energy values, and temporal trends in population-level demand are difficult to quantify precisely at large scales. We took advantage of detailed data previously collected on wintering waterfowl at Edwin B. Forsythe National Wildlife Refuge and surrounding marsh, New Jersey, and created a well-constrained local model of carrying capacity. We used 1,223 core samples collected between 2006 and 2015 to estimate food availability. We used species-specific 24-h time–activity data collected between 2011 and 2013 to estimate daily energy expenditure, morphometrically corrected for site- and day-specific thermoregulatory costs. To estimate population-level energy demand, we used standardized monthly ground surveys (2005–2014) to create a migration curve, and proportionally scaled that to fit aerial survey data (2005–2014). Crucially, we also explicitly incorporated estimates of variance in all of these parameters and conducted a sensitivity analysis to diagnose the most important sources of variation in the model. Our results from an outlier-removed, a strict depletion model indicated that at estimated mean levels of supply (923 million kcal) and cumulative demand (3.4 billion kcal), refuge food resources were depleted before November. However, a constant-supply model that represented tidal replenishment of resources indicated that just enough energy was present to sustain peak winter populations. Variation in model output appeared to be driven primarily by uncertainty in population abundance during peak periods of use, emphasizing a new management focus on studying migration chronologies of waterfowl. This model allows for relative assessment of biases and uncertainties in carrying-capacity modeling, and serves as a framework identifying critical science needs to improve local and regional waterfowl management planning.

Globally limited to 45,000 km2, salt marshes and their endemic species are threatened by numerous anthropogenic influences, including sea-level rise and predator pressure on survival and nesting success. Along the Atlantic coast of North America, Seaside (Ammospiza maritima) and Saltmarsh (A. caudacuta) sparrows are endemic to salt marshes, with Saltmarsh Sparrows declining by 9% annually. Because vital rates and factors affecting population persistence vary for both species, local estimates are necessary to best predict population persistence in response to management actions. We used a metapopulation model to estimate the population viability of the breeding Seaside and Saltmarsh sparrow populations in coastal New Jersey over a 42-yr period. We incorporated empirical data on the vital rates and abundances of these populations and simulated the effect of low (0.35 m) and high (0.75 m) levels of sea-level rise. We found that the Seaside Sparrow population persisted under both sea-level rise scenarios; however, the Saltmarsh Sparrow population reached a quasi-extinction threshold within 20 yr. Using the same framework, we modeled potential management scenarios that could increase the persistence probability of Saltmarsh Sparrows and found that fecundity and juvenile survival rates will require at least a 15% concurrent increase for the local population to persist beyond 2050. Future field research should evaluate the feasibility and effectiveness of management actions, such as predator control, for increasing Saltmarsh Sparrow vital rates in order to maintain the species in coastal New Jersey.