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At least 1,063 records · Page 59Linked to original sources

USGS2021

Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization

Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A., that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas, GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hot spots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N -mixture models to estimate habitat–abundance relationships, develop spatially explicit predictions of abundance, and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated a high representation of this bird assemblage within the entire conservation network, but with a bias toward birds associated with moderately tallgrasses relative to those associated with shortgrasses or tallgrasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serves as a complementary tool for conservation planning efforts globally.

Wisconsin↗

USGS2016

Assessing models of speciation under different biogeographic scenarios; An empirical study using multi-locus and RNA-seq analyses

Evolutionary biology often seeks to decipher the drivers of speciation, and much debate persists over the relative importance of isolation and gene flow in the formation of new species. Genetic studies of closely related species can assess if gene flow was present during speciation, because signatures of past introgression often persist in the genome. We test hypotheses on which mechanisms of speciation drove diversity among three distinct lineages of desert tortoise in the genus Gopherus . These lineages offer a powerful system to study speciation, because different biogeographic patterns (physical vs. ecological segregation) are observed at opposing ends of their distributions. We use 82 samples collected from 38 sites, representing the entire species' distribution and generate sequence data for mtDNA and four nuclear loci. A multilocus phylogenetic analysis in *BEAST estimates the species tree. RNA‐seq data yield 20,126 synonymous variants from 7665 contigs from two individuals of each of the three lineages. Analyses of these data using the demographic inference package ∂a∂i serve to test the null hypothesis of no gene flow during divergence. The best‐fit demographic model for the three taxa is concordant with the *BEAST species tree, and the ∂a∂i analysis does not indicate gene flow among any of the three lineages during their divergence. These analyses suggest that divergence among the lineages occurred in the absence of gene flow and in this scenario the genetic signature of ecological isolation (parapatric model) cannot be differentiated from geographic isolation (allopatric model).

Ecology and Evolution↗

USGS2024

Multi-decadal trophic shifts in Lake Erie yellow perch Perca flavescens

In Lake Erie, yellow perch Perca flavescens support vast commercial and recreational fisheries, yet populations have recently declined. Using N = 5889 yellow perch stomachs collected from 1997 to 2021, we explored trends in the feeding ecology and trophic level of yellow perch with generalized additive models. Models revealed a significant decrease in yellow perch trophic level (−0.15 trophic levels in the last decade), and significant dietary shifts. Yellow perch have shifted away from feeding on piscine prey and round goby Neogobius melanostomus over the 25-year period, and now feed on invertebrates more frequently—including invasive waterfleas ( Bythotrephes longimanus and Cercopagis pengoi ) and chironomids. Dietary patterns appear to reflect broad ecological changes—invasive waterfleas have proliferated while populations of forage fish and round goby have declined. Furthermore, hypoxia events have increased in duration and severity, which may explain observed increases in chironomid consumption, which are hypoxia tolerant. This study demonstrates trophic adaptability in yellow perch, which have changed feeding behavior and trophic position in response to novel invaders and changing environmental conditions.

Lake Erie↗

USGS2021

Historical changes in plant water use and need in the continental United States

A robust method for characterizing the biophysical environment of terrestrial vegetation uses the relationship between Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD). These variables are usually estimated from a water balance model rather than measured directly and are often more representative of ecologically-significant changes than temperature or precipitation. We evaluate trends and spatial patterns in AET and CWD in the Continental United States (CONUS) during 1980–2019 using a gridded water balance model. The western US had linear regression slopes indicating increasing CWD and decreasing AET (drying), while the eastern US had generally opposite trends. When limits to plant performance characterized by AET and CWD are exceeded, vegetation assemblages change. Widespread increases in aridity throughout the west portends shifts in the distribution of plants limited by available moisture. A detailed look at Sequoia National Park illustrates the high degree of fine-scale spatial variability that exists across elevation and topographical gradients. Where such topographical and climatic diversity exists, appropriate use of our gridded data will require sub-setting to an appropriate area and analyzing according to categories of interest such as vegetation communities or across obvious physical gradients. Recent studies have successfully applied similar water balance models to fire risk and forest structure in both western and eastern U.S. forests, arid-land spring discharge, amphibian colonization and persistence in wetlands, whitebark pine mortality and establishment, and the distribution of arid-land grass species and landscape scale vegetation condition. Our gridded dataset is available free for public use. Our findings illustrate how a simple water balance model can identify important trends and patterns at site to regional scales. However, at finer scales, environmental heterogeneity is driving a range of responses that may not be simply characterized by a single trend.

PLoS ONE↗

USGS2017

Moving forward in circles: Challenges and opportunities in modeling population cycles

Population cycling is a widespread phenomenon, observed across a multitude of taxa in both laboratory and natural conditions. Historically, the theory associated with population cycles was tightly linked to pairwise consumer–resource interactions and studied via deterministic models, but current empirical and theoretical research reveals a much richer basis for ecological cycles. Stochasticity and seasonality can modulate or create cyclic behaviour in non-intuitive ways, the high-dimensionality in ecological systems can profoundly influence cycling, and so can demographic structure and eco-evolutionary dynamics. An inclusive theory for population cycles, ranging from ecosystem-level to demographic modelling, grounded in observational or experimental data, is therefore necessary to better understand observed cyclical patterns. In turn, by gaining better insight into the drivers of population cycles, we can begin to understand the causes of cycle gain and loss, how biodiversity interacts with population cycling, and how to effectively manage wildly fluctuating populations, all of which are growing domains of ecological research.

Ecology Letters↗

USGS2003

Quantile regression models of animal habitat relationships

Typically, all factors that limit an organism are not measured and included in statistical models used to investigate relationships with their environment. If important unmeasured variables interact multiplicatively with the measured variables, the statistical models often will have heterogeneous response distributions with unequal variances. Quantile regression is an approach for estimating the conditional quantiles of a response variable distribution in the linear model, providing a more complete view of possible causal relationships between variables in ecological processes. Chapter 1 introduces quantile regression and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of estimates for homogeneous and heterogeneous regression models. Chapter 2 evaluates performance of quantile rankscore tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). A permutation F test maintained better Type I errors than the Chi-square T test for models with smaller n , greater number of parameters p , and more extreme quantiles τ. Both versions of the test required weighting to maintain correct Type I errors when there was heterogeneity under the alternative model. An example application related trout densities to stream channel width:depth. Chapter 3 evaluates a drop in dispersion, F -ratio like permutation test for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). Chapter 4 simulates from a large ( N = 10,000) finite population representing grid areas on a landscape to demonstrate various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable (spatially and not spatially structured). Depending on whether interactions of the measured habitat and unmeasured variable were negative (interference interactions) or positive (facilitation interactions), either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Sampling ( n = 20 - 300) simulations demonstrated that confidence intervals constructed by inverting rankscore tests provided valid coverage of these biased parameters. Quantile regression was used to estimate effects of physical habitat resources on a bivalve mussel ( Macomona liliana ) in a New Zealand harbor by modeling the spatial trend surface as a cubic polynomial of location coordinates.

Thesis↗

USGS2025

Environmental drivers of productivity explain population patterns of an Arctic-nesting goose across a half-century

Joint estimation of demographic rates and population size has become an essential tool in ecology because it enables evaluating mechanisms for population change and testing hypotheses about drivers of demography in a single modeling framework. This approach provides a comprehensive perspective on population dynamics and how animal populations will respond to global pressures in future years. However, long-term data for such analyses are often limited in quantity and quality. We developed an integrated population model combining data on demography and population size from nine different sources to understand the population ecology of the lesser snow goose ( Anser caerulescens caerulescens ) in the Pacific Flyway in North America from 1970 to 2022. We divided the flyway population into Wrangel Island and Western Arctic subpopulations and assessed demographic mechanisms for population change and environmental and anthropogenic drivers that influenced demography. During 1970–2022, the estimated spring population of snow geese in the Pacific Flyway increased from ~300,000 to ~2,300,000. Short-term changes in population growth rate were primarily driven by changes in productivity in the Western Arctic and productivity and immigration in Wrangel Island. Changes in hunting and natural mortality had less influence on short-term but likely contributed to the pronounced long-term population growth. Early snowmelt positively influenced per capita productivity in both regions, and warm, rainy weather during the non-breeding season was associated with high per capita productivity in the Western Arctic. In the Western Arctic, per capita productivity was negatively associated with population size, and adult natural mortality was positively associated with population size, indicating density-dependent regulation in this subpopulation. In Wrangel Island, warm weather in early fall decreased juvenile natural mortality. Our results demonstrate that per capita productivity and immigration, rather than adult survival, were the primary mechanisms of short-term population change in this long-lived species. Our results also indicate that environmental conditions and density-dependent effects can impact population dynamics more than harvest, even for a long-lived, commonly harvested species. We demonstrate that a warming climate can have multiple effects on demography, emphasizing the importance of assessing a variety of spatial and temporal factors when predicting how populations might respond to large-scale environmental changes. This emphasizes the importance of conservation plans that consider these environmental drivers, although this may complicate direct management of such populations.

Ecological Applications↗

USGS2020

Identifying candidate reference reaches to assess the physical and biological integrity of wadeable streams in different ecoregions and among stream sizes

Efforts to quantify disturbances to aquatic systems often use landscape-level metrics, presumably linked to ecological integrity, but fewer studies have directly linked ecological integrity to instream habitat, and applied these results to unsampled stream reaches throughout a landscape. We developed a flexible, quantitative approach that characterizes stream impairment across a landscape and identifies least-disturbed stream reaches to serve as benchmarks for high quality physical habitat and ecological integrity. Fish and macroinvertebrate community characteristics, reach-level physical habitat and water quality metrics were summarized in 891 wadeable stream reaches across two ecoregions in Missouri, USA. The influence of reach and water-quality characteristics as well as landscape-level variables on 7 fish and 3 macroinvertebrate community biological indicator metrics was then modeled using boosted regression trees (BRTs). On average, reach-level models explained more variance (25 and 27% in the two ecoregions examined) in biotic metrics than landscape-level models (18% and 20%). Abiotic and biotic associations differed among ecoregions and stream sizes, however, reach-level habitat (e.g., bankfull width/depth ratio, channel incision height) and water quality (e.g., dissolved oxygen, total chlorophyll) were consistently top predictors. At the landscape scale, fish richness in the agriculturally dominated ecoregion increased with decreased fragmentation/flow modification, while invertebrate metrics responded to agricultural disturbances. Invertebrate metrics in the forested ecoregion showed community degradation apparent with crop coverage as low as 8-10% of the riparian zone, while urban impairment was best detected using invertebrate indicators of biotic integrity and measures of fish trophic ecology. Relationships among landscape-scale variables and reach characteristics identified as top predictors in BRTs also highlighted potential mechanistic relationships among landscape, habitat, and measures of ecological integrity. Using the results of the landscape-level models, estimates for overall ecological integrity were predicted for over 28,000 stream reaches throughout Missouri, and a total of 1,423 candidate reference reaches were identified. The objective approach to characterizing stream impairment developed in this study offers specific advantages, including a reach and landscape-level evaluation of human disturbance as well as an inductive, multi-metric determination of ecological integrity.

Missouri↗

USGS2012

Exploring similarities among many species distributions

Collecting species presence data and then building models to predict species distribution has been long practiced in the field of ecology for the purpose of improving our understanding of species relationships with each other and with the environment. Due to limitations of computing power as well as limited means of using modeling software on HPC facilities, past species distribution studies have been unable to fully explore diverse data sets. We build a system that can, for the first time to our knowledge, leverage HPC to support effective exploration of species similarities in distribution as well as their dependencies on common environmental conditions. Our system can also compute and reveal uncertainties in the modeling results enabling domain experts to make informed judgments about the data. Our work was motivated by and centered around data collection efforts within the Great Smoky Mountains National Park that date back to the 1940s. Our findings present new research opportunities in ecology and produce actionable field-work items for biodiversity management personnel to include in their planning of daily management activities.

Conference Paper↗

USGS2015

Characterization of shrubland ecosystem components as continuous fields in the northwest United States

Accurate and consistent estimates of shrubland ecosystem components are crucial to a better understanding of ecosystem conditions in arid and semiarid lands. An innovative approach was developed by integrating multiple sources of information to quantify shrubland components as continuous field products within the National Land Cover Database (NLCD). The approach consists of several procedures including field sample collections, high-resolution mapping of shrubland components using WorldView-2 imagery and regression tree models, Landsat 8 radiometric balancing and phenological mosaicking, medium resolution estimates of shrubland components following different climate zones using Landsat 8 phenological mosaics and regression tree models, and product validation. Fractional covers of nine shrubland components were estimated: annual herbaceous, bare ground, big sagebrush, herbaceous, litter, sagebrush, shrub, sagebrush height, and shrub height. Our study area included the footprint of six Landsat 8 scenes in the northwestern United States. Results show that most components have relatively significant correlations with validation data, have small normalized root mean square errors, and correspond well with expected ecological gradients. While some uncertainties remain with height estimates, the model formulated in this study provides a cross-validated, unbiased, and cost effective approach to quantify shrubland components at a regional scale and advances knowledge of horizontal and vertical variability of these components.

California, Idaho, Nevada, Oregon↗

USGS2016

Fire in the Earth System: Bridging data and modeling research

Significant changes in wildfire occurrence, extent, and severity in areas such as western North America and Indonesia in 2015 have made the issue of fire increasingly salient in both the public and scientific spheres. Biomass combustion rapidly transforms land cover, smoke pours into the atmosphere, radiative heat from fires initiates dramatic pyrocumulus clouds, and the repeated ecological and atmospheric effects of fire can even impact regional and global climate. Furthermore, fires have a significant impact on human health, livelihoods, and social and economic systems. Modeling and databased methods to understand fire have rapidly coevolved over the past decade. Satellite and ground-based data about present-day fire are widely available for applications in research and fire management. Fire modeling has developed in part because of the evolution in vegetation and Earth system modeling efforts, but parameterizations and validation are largely focused on the present day because of the availability of satellite data. Charcoal deposits in sediment cores have emerged as a powerful method to evaluate trends in biomass burning extending back to the Last Glacial Maximum and beyond, and these records provide a context for present-day fire. The Global Charcoal Database version 3 compiled about 700 charcoal records and more than 1,000 records are expected for the future version 4. Together, these advances offer a pathway to explore how the strengths of fire data and fire modeling could address the weaknesses in the overall understanding of human-climate–fire linkages. A community of researchers studying fire in the Earth system with individual expertise that included paleoecology, paleoclimatology, modern ecology, archaeology, climate, and Earth system modeling, statistics, geography, biogeochemistry, and atmospheric science met at an intensive workshop in Massachusetts to explore new research directions and initiate new collaborations. Research themes, which emerged from the workshop participants via preworkshop surveys, focused on addressing the following questions: What are the climatic, ecological, and human drivers of fire regimes, both past and future? What is the role of humans in shaping historical fire regimes? How does fire ecology affect land cover changes, biodiversity, carbon storage, and human land uses? What are the historical fire trends and their impacts across biomes? Are their impacts local and/or regional? Are the fire trends in the last two decades unprecedented from a historical perspective? The workshop 1 aimed to develop testable hypotheses about fire, climate, vegetation, and human interactions by leveraging the confluence of proxy, observational, and model data related to decadal- to millennial-scale fire activity on our planet. New research directions focused on broad interdisciplinary approaches to highlight how knowledge about past fire activity could provide a more complete understanding of the predictive capacity of fire models and inform fire policy in the face of our changing climate.

Bulletin of the American Meteorological Society↗

USGS2009

Modeling individual animal histories with multistate capture–recapture models

Many fields of science begin with a phase of exploration and description, followed by investigations of the processes that account for observed patterns. The science of ecology is no exception, and recent decades have seen a focus on understanding key processes underlying the dynamics of ecological systems. In population ecology, emphasis has shifted from the state variable of population size to the demographic processes responsible for changes in this state variable: birth, death, immigration, and emigration. In evolutionary ecology, some of these same demographic processes, rates of birth and death, are also the determinants of fitness. In animal population ecology, the estimation of state variables and their associated vital rates is especially problematic because of the difficulties in sampling such populations and detecting individual animals. Indeed, early capture–recapture models were developed for the purpose of estimating population size, given the reality that all animals are not caught or detected at any sampling occasion. More recently, capture–recapture models for open populations were developed to draw inferences about survival in the face of these same sampling problems. The focus of this paper is on multi‐state mark–recapture models (MSMR), which first appeared in the 1970s but have undergone substantial development in the last 15 years. These models were developed to deal explicitly with biological variation, in that animals in different “states” (classes defined by location, physiology, behavior, reproductive status, etc.) may have different probabilities of survival and detection. Animal transitions between states are also stochastic and themselves of interest. These general models have proven to be extremely useful and provide a way of thinking about a remarkably wide range of important ecological processes. These methods are now at a stage of refinement and sophistication where they can readily be used by biologists to tackle a wide range of important issues in ecology. In this paper, we draw together information on the state of the art in multistate mark–recapture methods, explaining the models and illustrating their use. We provide a modeling philosophy and a series of general principles on how to conduct analyses. We cover key issues and features, and we anticipate the ways in which we expect the models to develop in the years ahead.

Advances in Ecological Research↗

USGS2006

Report of the USGS Coastal and Marine Geology Modeling Workshop, Pacific Marine Science Center, Santa Cruz, CA, March 22-23, 2005

A U.S. Geological Survey (USGS) Coastal and Marine Geology (CMG) Modeling Workshop was held to discuss the general topic of coastal modeling, defined broadly to include circulation, waves, sediment transport, water quality, ecology, sediment diagenesis, morphology change, and coastal evolution, on scales ranging from seconds and a few centimeters (individual ripples) to centuries (coastal evolution) and thousands of kilometers (tsunami propagation). The workshop was convened at the suggestion of CMG Program Management to improve communication among modelers and model users, assess modeling-related activities being conducted at the three centers (Florida Integrated Science Center, FISC; Pacific Marine Science Center; PMSC; and Woods Hole Science Center; WHSC), and develop goals, strategies, and plans for future modeling activities. The workshop represents a step toward developing a five-year strategic plan, and was timed to provide input for the FY06 prospectus. The workshop was held at the USGS Pacific Marine Science Center in Santa Cruz on March 22-23, 2005.

Open-File Report↗

USGS2007

Patch-occupancy models indicate human activity as major determinant of forest elephant Loxodonta cyclotis seasonal distribution in an industrial corridor in Gabon

The importance of human activity and ecological features in influencing African forest elephant ranging behaviour was investigated in the Rabi-Ndogo corridor of the Gamba Complex of Protected Areas in southwest Gabon. Locations in a wide geographical area with a range of environmental variables were selected for patch-occupancy surveys using elephant dung to assess seasonal presence and absence of elephants. Patch-occupancy procedures allowed for covariate modelling evaluating hypotheses for both occupancy in relation to human activity and ecological features, and detection probability in relation to vegetation density. The best fitting models for old and fresh dung data sets indicate that (1) detection probability for elephant dung is negatively related to the relative density of the vegetation, and (2) human activity, such as presence and infrastructure, are more closely associated with elephant distribution patterns than are ecological features, such as the presence of wetlands and preferred fresh fruit. Our findings emphasize the sensitivity of elephants to human disturbance, in this case infrastructure development associated with gas and oil production. Patch-occupancy methodology offers a viable alternative to current transect protocols for monitoring programs with multiple covariates.

Biological Conservation↗

USGS2026

Statewide agent-based model for management of chronic wasting disease in white-tailed deer: PAOvCWD

Chronic wasting disease (CWD) is an always-fatal disease infecting wild cervids globally. Ecologically and economically important, CWD presents a challenge for managing white-tailed deer ( Odocoileus virginianus ). We built an agent-based model to simulate CWD transmission and assess potential management actions that could slow disease spread: PAOvCWD . We developed PAOvCWD using contact rates and other behavioral and ecological metrics estimated from deer monitored in Pennsylvania, USA. We programmed potential management responses (e.g., culling, altered hunter harvest) for all 22 Pennsylvania wildlife management units and validated the efficacy of PAOvCWD using a deer population in south-central Pennsylvania infected with CWD for > 10 years. To support applications of our model, we developed a user-friendly R pipeline that allows implementation with relatively minor modifications. Our pipeline includes four steps: • Steps 1 and 2 generate regional percent forest cover rasters and curate population-level demographics. • Step 3 initializes landscapes and agents using our PAOvPOP model. • Step 4 assesses CWD transmission and management responses using our PAOvCWD model.

Pennsylvania↗

USGS2010

Determination of biologically significant hydrologic condition metrics in urbanizing watersheds: an empirical analysis over a range of environmental settings

We investigated the relations among 83 hydrologic condition metrics (HCMs) and changes in algal, invertebrate, and fish communities in five metropolitan areas across the continental United States. We used a statistical approach that employed Spearman correlation and regression tree analysis to identify five HCMs that are strongly associated with observed biological variation along a gradient of urbanization. The HCMs related to average flow magnitude, high-flow magnitude, high-flow event frequency, high-flow duration, and rate of change of stream cross-sectional area were most consistently associated with changes in aquatic communities. Although our investigation used an urban gradient design with short hydrologic periods of record (≤1 year) of hourly cross-sectional area time series, these five HCMs were consistent with previous investigations using long-term daily-flow records. The ecological sampling day often was included in the hydrologic period. Regression tree models explained up to 73, 92, and 79% of variance for specific algal, invertebrate, and fish community metrics, respectively. National models generally were not as statistically significant as models for individual metropolitan areas. High-flow event frequency, a hydrologic metric found to be transferable across stream type and useful for classifying habitat by previous research, was found to be the most ecologically relevant HCM; transformation by precipitation increased national-scale applicability. We also investigated the relation between measures of stream flashiness and land-cover indicators of urbanization and found that land-cover characteristic and pattern variables, such as road density, percent wetland, and proximity of developed land, were strongly related to HCMs at both a metropolitan and national scale and, therefore, may be effective land-use management options in addition to wholesale impervious-area reduction.

Milwaukee-Green Bay; Boston; Raleigh; Atlanta; Bir↗

USGS2015

Climate change: evaluating your local and regional water resources

The BCM is a fine-scale hydrologic model that uses detailed maps of soils, geology, topography, and transient monthly or daily maps of potential evapotranspiration, air temperature, and precipitation to generate maps of recharge, runoff, snow pack, actual evapotranspiration, and climatic water deficit. With these comprehensive environmental inputs and experienced scientific analysis, the BCM provides resource managers with important hydrologic and ecologic understanding of a landscape or basin at hillslope to regional scales. The model is calibrated using historical climate and streamflow data over the range of geologic materials specific to an area. Once calibrated, the model is used to translate climate-change data into hydrologic responses for a defined landscape, to provide managers an understanding of potential ecological risks and threats to water supplies and managed hydrologic systems. Although limited to estimates of unimpaired hydrologic conditions, estimates of impaired conditions, such as agricultural demand, diversions, or reservoir outflows can be incorporated into the calibration of the model to expand its utility. Additionally, the model can be linked to other models, such as groundwater-flow models (that is, MODFLOW) or the integrated hydrologic model (MF-FMP), to provide information about subsurface hydrologic processes. The model can be applied at a relatively small scale, but also can be applied to large-scale national and international river basins.

Fact Sheet↗

USGS2006

Nest survival in dusky Canada geese ( Branta canadensis occidentalis ): Use of discrete-time models

The Dusky Canada Goose ( Branta canadensis occidentalis ) population that breeds in the Copper River Delta, Alaska, has declined substantially since the late 1970s. Persistent low numbers have been attributed to low productivity in recent years. We examined patterns in survival rates of 1,852 nests to better understand ecological processes that influenced productivity during 1997-2000. We compared 10 nonparametric models of daily survival rate of nests (DSR) that included variation among years, calendar dates, nest initiation dates, and nest ages with equivalent models based on parametric functions. The unequivocal best model included patterns of DSR that varied among discrete periods of years, calendar dates, and nest ages. Generally, DSR was low early in the nesting season and higher midseason. Across years, patterns in DSR were most variable early and late in the nesting season. Daily survival rates of nests declined between the first and second week after initiation, increased until the fourth week, and then declined during the last week before hatch. Nest survival probability estimates ranged from 0.07 to 0.71 across years and nest initiation dates. Mean rates of nest survival ranged between 0.21 and 0.31 each year. We suggest (1) considering models that do not limit estimates of daily nest survival to parametric forms; (2) placing greater emphasis on sample size when nests are rare, to obtain accurate estimates of nest survival; and (3) developing new techniques to estimate the number of nests initiated.

The Auk↗