Search USGS⌕ Search

USGS · 70223771

Pedigree accumulation analysis: Combining methods from community ecology and population genetics for breeding adult estimation

Abstract

Estimates of the number of successfully breeding adults ( N S ) in a population can predict levels of recruitment. However, assessments of N S are often difficult to obtain because encounters with adults are limited due to life-history characteristics, low abundance or other constraints associated with access to critical habitats. Alternatively, efforts to sample individuals at earlier ontogenetic stages can be more tractable, resulting in more comprehensive samples. To estimate N S , we describe, evaluate and apply two nonparametric species richness estimators to information associated with genetic pedigree reconstruction. Simulations compared bias and precision associated with Chao and Jackknife methods when estimating N S . We also evaluated N S estimation sensitivity to two sources of variation associated with species reproductive ecology (variance in reproductive success and sex ratio skew) and genetic pedigree assignment error. Finally, the application of our novel method was demonstrated in two different species and systems (Chinook Salmon in Oregon, USA and Lake Sturgeon in Michigan, USA). We found unbiased N S estimates were generated across a broad range of offspring sample sizes using the Chao method. Empirical results corroborated simulation-based expectations and highlighted applications where parents and offspring are sampled, and when only offspring are sampled. When offspring sample sizes are adequate and pedigree reconstruction errors are low, the combination of established methods from community ecology and genetic pedigree reconstruction provides an accurate alternative method to estimate N S that can facilitate population assessments.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nicholas M. Sard, Robert D. Hunter, Edward F. Roseman, Daniel B. Hayes, Robin L . DeBruyne, Kim T Scribner. 2021-09-02. Pedigree accumulation analysis: Combining methods from community ecology and population genetics for breeding adult estimation. https://doi.org/10.1111/2041-210x.13704

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

A systematic review of avian influenza virus transmission models: Gaps and opportunities

Highly pathogenic avian influenza virus (HPAIV) increasingly threatens wild birds, poultry, non-human mammals and humans, yet gaps and opportunities in avian influenza virus (AIV) transmission modelling have not been comprehensively synthesized. We reviewed 182 articles published through 2025, categorized dimensions, including modelling approaches, data, host types, biological levels, spatial scales, temporal scales and model objectives, examined associations among these dimensions, and synthesized author-stated limitations and recommendations. Statistical and mechanistic models dominated. Model data centred on population dynamics and demography, disease surveillance, host epidemiology and competence, meteorology and land use and land cover. Most studies focused on poultry or wild birds at regional spatial scales, seasonal/annual and multi-year temporal scales, population or multi-population levels, and were retrospective. Machine learning and decision- making models were less common. Immunity, behaviour and life history, community composition, morphology, viral genetics and viral persistence received less attention. Non-human mammals, individual and community biological levels, local and broader spatial scales, daily temporal scales and prospective modelling were also less frequently modelled. The authors frequently reported limitations related to data availability, surveillance sampling and reporting bias, and model simplification, while commonly recommending improved data quality and greater model complexity and realism.

Methods in Ecology and Evolution↗

Spatially realistic dynamic N-mixture models

Dispersal is a complex ecological process that can be driven by the abiotic environment, density-dependent processes and species mobility. Traditionally, dispersal has been studied with capture-based data that are often limited in space and time. More recently, spatial dynamic N-mixture models (i.e. N-mixture models accounting for dispersal among local populations and changes in abundance over time) allow the study of dispersal with count data. However, current spatial dynamic N-mixture models focus mainly on distance-based dispersal, limiting our ability to evaluate realistic patterns and drivers of dispersal at relevant spatial and temporal scales. We developed a spatially realistic dynamic N-mixture model that allows dispersal to be driven by environmental conditions and population density. Using simulations, we evaluated the inferential performance of this model when large proportions of sites are unsurveyed and the survey period is short, situations that are typical of real-world studies. We then used two case studies to illustrate the applications of this model, one representing a species with limited mobility in fragmented habitat (Chiricahua Leopard Frogs, Lithobates chiricahuensis , in isolated ponds) and the other representing a species with great mobility in continuous heterogeneous habitat (Common Nighthawks, Chordeiles minor , in grasslands). Our simulation study showed that the model provides valid inference even when the number of surveyed sites is small (𝑛 =10) or when the survey period is short for dynamic models (5 years), indicating that the model could have great practical value in real-world studies. The case study of Chiricahua Leopard Frogs showed that the model is capable of revealing pond-type-specific dispersal and predicting dispersal among ponds. The case study of Common Nighthawks showed the ability of the model to reveal the effect of primary productivity on dispersal and to map dispersal across the study area. Our effort demonstrates the potential of using count data to gain insights about complex yet realistic dispersal processes. The model that we introduce is flexible and adaptable for a broad range of situations in population and conservation ecology

Arizona, Kansas↗

Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity

1. The biodiversity crisis necessitates spatially extensive methods to monitor multiple taxonomic groups for evidence of change in response to evolving environmental conditions. Programs that combine passive acoustic monitoring and machine learning are increasingly used to meet this need. These methods require large, annotated datasets, which are time-consuming and expensive to produce, creating potential barriers to adoption in data- and funding-poor regions. Recently released pre-trained avian acoustic classification models provide opportunities to reduce the need for manual labelling and accelerate the development of new acoustic classification algorithms through transfer learning. Transfer learning is a strategy for developing algorithms under data scarcity that uses pre-trained models from related tasks to adapt to new tasks. 2. Our primary objective was to develop a transfer learning strategy using the feature embeddings of a pre-trained avian classification model to train custom acoustic classification models in data-scarce contexts. We used three annotated avian acoustic datasets to test whether transfer learning and soundscape simulation-based data augmentation could substantially reduce the annotated training data necessary to develop performant custom acoustic classifiers. We also conducted a sensitivity analysis for hyperparameter choice and model architecture. We then assessed the generalizability of our strategy to increasingly novel non-avian classification tasks. 3. With as few as two training examples per class, our soundscape simulation data augmentation approach consistently yielded new classifiers with improved performance relative to the pre-trained classification model and transfer learning classifiers trained with other augmentation approaches. Performance increases were evident for three avian test datasets, including single-class and multi-label contexts. We observed that the relative performance among our data augmentation approaches varied for the avian datasets and nearly converged for one dataset when we included more training examples. 4. We demonstrate an efficient approach to developing new acoustic classifiers leveraging open-source sound repositories and pre-trained networks to reduce manual labelling. With very few examples, our soundscape simulation approach to data augmentation yielded classifiers with performance equivalent to those trained with many more examples, showing it is possible to reduce manual label-ling while still achieving high-performance classifiers and, in turn, expanding the potential for passive acoustic monitoring to address rising biodiversity monitoring needs.

Methods in Ecology and Evolution↗