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At least 1,171 records · Page 65Linked to original sources
USGS2013

Eco-evolutionary responses of Bromus tectorum to climate change: implications for biological invasions

How plant populations, communities, and ecosystems respond to climate change is a critical focus in ecology today. The responses of introduced species may be especially rapid. Current models that incorporate temperature and precipitation suggest that future Bromus tectorum invasion risk is low for the Colorado Plateau. With a field warming experiment at two sites in southeastern Utah, we tested this prediction over 4 years, measuring B. tectorum phenology, biomass, and reproduction. In a complimentary greenhouse study, we assessed whether changes in field B. tectorum biomass and reproductive output influence offspring performance. We found that following a wet winter and early spring, the timing of spring growth initiation, flowering, and summer senescence all advanced in warmed plots at both field sites and the shift in phenology was progressively larger with greater warming. Earlier green-up and development was associated with increases in B. tectorum biomass and reproductive output, likely due early spring growth, when soil moisture was not limiting, and a lengthened growing season. Seeds collected from plants grown in warmed plots had higher biomass and germination rates and lower mortality than seeds from ambient plots. However, in the following two dry years, we observed no differences in phenology between warmed and ambient plots. In addition, warming had a generally negative effect on B. tectorum biomass and reproduction in dry years and this negative effect was significant in the plots that received the highest warming treatment. In contrast to models that predict negative responses of B. tectorum to warmer climate on the Colorado Plateau, the effects of warming were more nuanced, relied on background climate, and differed between the two field sites. Our results highlight the importance of considering the interacting effects of temperature, precipitation, and site-specific characteristics such as soil texture, on plant demography and have direct implications for B. tectorum invasion dynamics on the Colorado Plateau.

Utah↗
USGS2024

White-tailed deer detection rates increase when coyotes are present

Predator species can indirectly affect prey species through the cost of anti-predator behavior responses, which may involve shifts in occupancy, space use, or movement. Quantifying the various strategies implemented by prey species to avoid adverse interactions with predators can lead to a better understanding of potential population-level repercussions. Therefore, the purpose of this study was to examine predator–prey interactions by quantifying the effect of predator species presence on detection rates of prey species, using coyotes ( Canis latrans ) and white-tailed deer ( Odocoileus virginianus ) in Central Appalachian forests of the eastern United States as a model predator–prey system. To test two competing hypotheses related to interspecific interactions, we modeled species detections from 319 camera traps with a two-species occupancy model that incorporated a continuous-time detection process. We found that white-tailed deer occupancy was independent of coyote occupancy, but white-tailed deer were more frequently detectable and had greater detection intensity at sites where coyotes were present, regardless of vegetation-related covariates. In addition, white-tailed deer detection rates at sites with coyotes were highest when presumed forage availability was relatively low. These findings suggest that white-tailed deer may be exhibiting an active avoidance behavioral response to predators by increasing movement rates when coyotes are present in an area, perhaps due to reactive evasive maneuvers and/or proactive attempts to reduce adverse encounters with them. Concurrently, coyotes could be occupying sites with higher white-tailed deer densities. Because white-tailed deer did not exhibit significant shifts in daily activity patterns based on coyote occupancy, we further suggest that white-tailed deer in our study system generally do not use temporal partitioning as their primary strategy for avoiding encounters with coyotes. Overall, our study implements a recently developed analytical approach for modeling multi-species occupancy from camera traps and provides novel ecological insight into the complex relationships between predator and prey species.

Pennsylvania, West Virginia↗
USGS2001

A probabilistic model for silver bioaccumulation in aquatic systems and assessment of human health risks

Silver (Ag) is discharged in wastewater effluents and is also a component in a proposed secondary water disinfectant. A steady-state model was developed to simulate bioaccumulation in aquatic biota and assess ecological and human health risks. Trophic levels included phytoplankton, invertebrates, brown trout, and common carp. Uptake routes included water, food, or sediment. Based on an extensive review of the literature, distributions were derived for most inputs for use in Monte Carlo simulations. Three scenarios represented ranges of dilution and turbidity. Compared with the limited field data available, median estimates of Ag in carp (0.07-2.1 Iμg/g dry weight) were 0.5 to 9 times measured values, and all measurements were within the predicted interquartile range. Median Ag concentrations in biota were ranked invertebrates > phytoplankton > trout > carp. Biotic concentrations were highest for conditions of low dilution and low turbidity. Critical variables included Ag assimilation eficiency, specific feeding rate, and the phytoplankton bioconcentration factor. Bioaccumulation of Ag seems unlikely to result in txicity to aquatic biota and humans consuming fish. Although the highest predicted Ag concentrations in water (>200 ng/L) may pose chronic risks to early survival and development of salmonids and risks of argyria to subsistence fishers, these results occur under highly conservative conditions.

Environmental Toxicology and Chemistry↗
USGS2012

Predicting the likelihood of altered streamflows at ungauged rivers across the conterminous United States

An approach is presented in this study to aid water-resource managers in characterizing streamflow alteration at ungauged rivers. Such approaches can be used to take advantage of the substantial amounts of biological data collected at ungauged rivers to evaluate the potential ecological consequences of altered streamflows. National-scale random forest statistical models are developed to predict the likelihood that ungauged rivers have altered streamflows (relative to expected natural condition) for five hydrologic metrics (HMs) representing different aspects of the streamflow regime. The models use human disturbance variables, such as number of dams and road density, to predict the likelihood of streamflow alteration. For each HM, separate models are derived to predict the likelihood that the observed metric is greater than (‘inflated’) or less than (‘diminished’) natural conditions. The utility of these models is demonstrated by applying them to all river segments in the South Platte River in Colorado, USA, and for all 10-digit hydrologic units in the conterminous United States. In general, the models successfully predicted the likelihood of alteration to the five HMs at the national scale as well as in the South Platte River basin. However, the models predicting the likelihood of diminished HMs consistently outperformed models predicting inflated HMs, possibly because of fewer sites across the conterminous United States where HMs are inflated. The results of these analyses suggest that the primary predictors of altered streamflow regimes across the Nation are (i) the residence time of annual runoff held in storage in reservoirs, (ii) the degree of urbanization measured by road density and (iii) the extent of agricultural land cover in the river basin.

River Research and Applications↗
USGS2014

Bioavailability and bioaccumulation of metal-based engineered nanomaterials in aquatic environments: Concepts and processes

Bioavailability of Me-ENMs to aquatic organisms links their release into the environment to ecological implications. Close examination shows some important differences in the conceptual models that define bioavailability for metals and Me-ENMs. Metals are delivered to aquatic animals from Me-ENMs via water, ingestion, and incidental surface exposure. Both metal released from the Me-ENM and uptake of the nanoparticle itself contribute to bioaccumulation. Some mechanisms of toxicity and some of the metrics describing exposure may differ from metals alone. Bioavailability is driven by complex interaction of particle attributes, environmental transformations, and biological traits. Characterization of Me-ENMs is an essential part of understanding bioavailability and requires novel methodologies. The relative importance of the array of processes that could affect Me-ENM bioavailability remains poorly known, but new approaches and models are developing rapidly. Enough is known, however, to conclude that traditional approaches to exposure assessment for metals would not be adequate to assess risks from Me-ENMs.

Book chapter↗
USGS2019

Local niche differences predict genotype associations in sister taxa of desert tortoise

Aims To investigate spatial congruence between ecological niches and genotype in two allopatric species of desert tortoise that are species of conservation concern. Location Mojave and Sonoran Desert ecoregions; California, Nevada, Arizona, Utah, USA. Methods We compare ecological niches of Gopherus agassizii and Gopherus morafkai using species distribution modelling (SDM) and then calibrate a pooled‐taxa distribution model to explore local differences in species–environment relationships based on the spatial residuals of the pooled‐taxa model. We use multiscale geographically weighted regression (MGWR) applied to those residuals to estimate local species–environment relationships that can vary across the landscape. We identify multivariate clusters in these local species–environment relationships and compare them against models of (a) a geographically based taxonomic designation for two sister species and (b) an environmental ecoregion designation, with respect to their ability to predict a genotype association index for these two species. Results We find non‐identical niches for these species, with differences that span physiographic and vegetation niche dimensions. We find evidence for two distinct clusters of local species–environment relationships that when mapped, predict an index of genotype association for the two sister taxa better than did either the geographically based taxonomic designation or an environmental ecoregion designation. Main conclusions Exploring local species–environment relationships by coupling SDM and MGWR can benefit studies of biogeography and conservation. We find that niche separation in habitat selection conforms to genotypic differences between sister taxa of tortoise in a recent secondary contact zone. This result may inform decision making by agencies with regulatory or land management authority for the two sister taxa addressed here.

Arizona, California, Nevada, Utah↗
USGS2025

Host responses and viral traits interact to shape the impacts of climate warming on highly pathogenic avian influenza in migratory waterfowl

Emerging infectious diseases pose threats to wildlife populations, as exemplified by recent outbreaks of avian influenza viruses in wild birds. Climate change can affect infection dynamics in wildlife through direct effects on pathogens (e.g., environmental decay rates) and changes to host ecology, including shifting migration patterns. Here, we adapt an existing mechanistic model that couples migration and infection to study how traits of highly pathogenic avian influenza (HPAI) viruses contribute to HPAI outcomes in migratory waterfowl, then apply this model to explore potential impacts of climate change on HPAI dynamics. We find that the simulated impacts of HPAI on the host population under baseline climate conditions varied from no impact to 100% mortality, depending on viral traits. In most cases, traits related to transmission (i.e., contact rates, shedding rates) were more important for HPAI establishment probability, infection prevalence, and mortality than were other viral traits (e.g., environmental temperature sensitivity, cross-protective immunity). We then simulated the effects of climate change (i.e., altered temperature regimes) on HPAI dynamics both via viral environmental decay and via changes in bird migration phenology. In these simulations, we found that a 9-day advancement in spring migration timing increased the duration of HPAI outbreaks by increasing time birds spent at their breeding grounds, leading to higher mortality and fewer infections. In contrast, increased viral decay in warmer years had a smaller, but opposite impact. These patterns depended on the primary transmission mode of HPAI (i.e., direct vs. environmental) and its sensitivity to environmental temperatures. Together, these results suggest that climate change is likely to increase the impacts of HPAI on waterfowl populations if HPAI relies strongly on direct transmission and birds advance their spring migration. Further integrating host-viral co-evolution and other climatic changes (e.g., salinity, humidity) could provide more precise predictions of how HPAI dynamics could change in the future.

Alaska, California, Oregon↗
USGS2013

Evaluating methodological assumptions of a catch-curve survival estimation of unmarked precocial shorebird chickes

Estimating productivity for precocial species can be difficult because young birds leave their nest within hours or days of hatching and detectability thereafter can be very low. Recently, a method for using a modified catch-curve to estimate precocial chick daily survival for age based count data was presented using Piping Plover ( Charadrius melodus ) data from the Missouri River. However, many of the assumptions of the catch-curve approach were not fully evaluated for precocial chicks. We developed a simulation model to mimic Piping Plovers, a fairly representative shorebird, and age-based count-data collection. Using the simulated data, we calculated daily survival estimates and compared them with the known daily survival rates from the simulation model. We conducted these comparisons under different sampling scenarios where the ecological and statistical assumptions had been violated. Overall, the daily survival estimates calculated from the simulated data corresponded well with true survival rates of the simulation. Violating the accurate aging and the independence assumptions did not result in biased daily survival estimates, whereas unequal detection for younger or older birds and violating the birth death equilibrium did result in estimator bias. Assuring that all ages are equally detectable and timing data collection to approximately meet the birth death equilibrium are key to the successful use of this method for precocial shorebirds.

Waterbirds↗
USGS2005

Estimating site occupancy and abundance using indirect detection indices

Knowledge of factors influencing animal distribution and abundance is essential in many areas of ecological research, management, and policy-making. Because common methods for modeling and estimating abundance (e.g., capture–recapture, distance sampling) are sometimes not practical for large areas or elusive species, indices are sometimes used as surrogate measures of abundance. We present an extension of the Royle and Nichols (2003) generalization of the MacKenzie et al. (2002) site-occupancy model that incorporates length of the sampling interval into the model for detection probability. As a result, we obtain a modeling framework that shows how useful information can be extracted from a class of index methods we call indirect detection indices (IDIs). Examples of IDIs include scent station, tracking tube, snow track, tracking plate, and hair snare surveys. Our model is maximum likelihood, and it can be used to estimate site occupancy and model factors influencing patterns of occupancy and abundance in space. Under certain circumstances, it can also be used to estimate abundance. We evaluated model properties using Monte Carlo simulations and illustrate the method with tracking tube and scent station data. We believe this model will be a useful tool for determining factors that influence animal distribution and abundance.

Journal of Wildlife Management↗
USGS2011

Combined multibeam and LIDAR bathymetry data from eastern Long Island Sound and westernmost Block Island Sound-A regional perspective

Detailed bathymetric maps of the sea floor in Long Island Sound are of great interest to the Connecticut and New York research and management communities because of this estuary's ecological, recreational, and commercial importance. The completed, geologically interpreted digital terrain models (DTMs), ranging in area from 12 to 293 square kilometers, provide important benthic environmental information, yet many applications require a geographically broader perspective. For example, individual surveys are of limited use for the planning and construction of cross-sound infrastructure, such as cables and pipelines, or for the testing of regional circulation models. To address this need, we integrated 12 multibeam and 2 LIDAR (Light Detection and Ranging) contiguous bathymetric DTMs, produced by the National Oceanic and Atmospheric Administration during charting operations, into one dataset that covers much of eastern Long Island Sound and extends into westernmost Block Island Sound. The new dataset is adjusted to mean lower low water, is gridded to 4-meter resolution, and is provided in UTM Zone 18 NAD83 and geographic WGS84 projections. This resolution is adequate for sea floor-feature and process interpretation but is small enough to be queried and manipulated with standard Geographic Information System programs and to allow for future growth. Natural features visible in the grid include exposed bedrock outcrops, boulder lag deposits of submerged moraines, sand-wave fields, and scour depressions that reflect the strength of the oscillating and asymmetric tidal currents. Bedform asymmetry allows interpretations of net sediment transport. Anthropogenic artifacts visible in the bathymetric data include a dredged channel, shipwrecks, dredge spoils, mooring anchors, prop-scour depressions, buried cables, and bridge footings. Together the merged data reveal a larger, more continuous perspective of bathymetric topography than previously available, providing a fundamental framework for research and resource management activities in this major east-coast estuary.

Open-File Report↗
USGS1986

Analysis of utilization of desert habitats with dynamic simulation

The effects of climate and herbivores on cool desert shrubs in north-western Utah were investigated with a dynamic simulation model. Cool desert shrublands are extensively managed as grazing lands, and are defoliated annually by domestic livestock. A primary production model was used to simulate harvest yields and shrub responses under a variety of climatic regimes and defoliation patterns. The model consists of six plant components, and it is based on equations of growth analysis. Plant responses were simulated under various combinations of 20 annual weather patterns and 14 defoliation strategies. Results of the simulations exhibit some unexpected linearities in model behavior, and emphasize the importance of both the pattern of climate and the level of plant vigor in determining optimal harvest strategies. Model behaviors are interpreted in terms of shrub morphology, physiology and ecology.

Journal of Environmental Management↗
USGS2018

Estimating distemper virus dynamics among wolves and grizzly bears using serology and Bayesian state‐space models

Many parasites infect multiple hosts, but estimating the transmission across host species remains a key challenge in disease ecology. We investigated the within and across host species dynamics of canine distemper virus (CDV) in grizzly bears ( Ursus arctos ) and wolves ( Canis lupus ) of the Greater Yellowstone Ecosystem (GYE). We hypothesized that grizzly bears may be more likely to be exposed to CDV during outbreaks in the wolf population because grizzly bears often displace wolves while scavenging carcasses. We used serological data collected from 1984 to 2014 in conjunction with Bayesian state‐space models to infer the temporal dynamics of CDV. These models accounted for the unknown timing of pathogen exposure, and we assessed how different testing thresholds and the potential for testing errors affected our conclusions. We identified three main CDV outbreaks (1999, 2005, and 2008) in wolves, which were more obvious when we used higher diagnostic thresholds to qualify as seropositive. There was some evidence for increased exposure rates in grizzly bears in 2005, but the magnitude of the wolf effect on bear exposures was poorly estimated and depended upon our prior distributions. Grizzly bears were exposed to CDV prior to wolf reintroduction and during time periods outside of known wolf outbreaks, thus wolves are only one of several potential routes for grizzly bear exposures. Our modeling approach accounts for several of the shortcomings of serological data and is applicable to many wildlife disease systems, but is most informative when testing intervals are short. CDV circulates in a wide range of carnivore species, but it remains unclear whether the disease persists locally within the GYE carnivore community or is periodically reintroduced from distant regions with larger host populations.

Ecology and Evolution↗
USGS2021

A seasonally dynamic model of light at the stream surface

Light is a primary constraint on primary production and drives many ecological processes in stream ecosystems, yet light regimes have received considerably less attention than other factors of the stream environment, such as hydrology or nutrient cycling. Light received by streams can be highly heterogeneous in both space and time resulting from changes in topography, channel characteristics, and riparian vegetation. Both the structure and phenology of riparian vegetation can be important determinants of the seasonality and magnitude of light reaching the stream surface, particularly in smaller forested streams. Despite the importance of riparian phenology on temporal patterns of stream light availability, existing models do not account for the seasonal dynamics of canopies. We developed a dynamic, biophysically based model ( StreamLight ) that incorporates canopy structure and phenology to predict light reaching the stream surface. We compared StreamLight to an existing model at 21 sites across the USA and found that, across sites, our biophysically based model produced light estimates that were more strongly correlated to observations and reduced the magnitude of errors in comparison to the existing model, particularly for streams that were relatively narrow compared to the height of riparian vegetation. Because smaller streams represent most global stream length, we expect that, in many smaller forested streams, the inclusion of canopy structure and phenology will enhance our ability to predict light regimes. We also used model simulations to examine the importance of controls on stream light environments and found that channel width was the strongest control on light environments. StreamLight represents an important incremental step forward in developing mechanistic models of river network productivity and in linking shifts in terrestrial vegetation structure and phenology to aquatic ecosystem productivity and thermal regimes.

Freshwater Science↗
USGS2011

Monitoring carnivore populations at the landscape scale: occupancy modelling of tigers from sign surveys

1. Assessing spatial distributions of threatened large carnivores at landscape scales poses formidable challenges because of their rarity and elusiveness. As a consequence of logistical constraints, investigators typically rely on sign surveys. Most survey methods, however, do not explicitly address the central problem of imperfect detections of animal signs in the field, leading to underestimates of true habitat occupancy and distribution. 2. We assessed habitat occupancy for a tiger Panthera tigris metapopulation across a c. 38 000-km 2 landscape in India, employing a spatially replicated survey to explicitly address imperfect detections. Ecological predictions about tiger presence were confronted with sign detection data generated from occupancy sampling of 205 sites, each of 188 km 2 . 3. A recent occupancy model that considers Markovian dependency among sign detections on spatial replicates performed better than the standard occupancy model (ΔAIC = 184·9). A formulation of this model that fitted the data best showed that density of ungulate prey and levels of human disturbance were key determinants of local tiger presence. Model averaging resulted in a replicate-level detection probability [inline image] = 0·17 (0·17) for signs and a tiger habitat occupancy estimate of [inline image] = 0·665 (0·0857) or 14 076 (1814) km2 of potential habitat of 21 167 km 2 . In contrast, a traditional presence-versus-absence approach underestimated occupancy by 47%. Maps of probabilities of local site occupancy clearly identified tiger source populations at higher densities and matched observed tiger density variations, suggesting their potential utility for population assessments at landscape scales. 4. Synthesis and applications. Landscape-scale sign surveys can efficiently assess large carnivore spatial distributions and elucidate the factors governing their local presence, provided ecological and observation processes are both explicitly modelled. Occupancy sampling using spatial replicates can be used to reliably and efficiently identify tiger population sources and help monitor metapopulations. Our results reinforce earlier findings that prey depletion and human disturbance are key drivers of local tiger extinctions and tigers can persist even in human-dominated landscapes through effective protection of source populations. Our approach facilitates efficient targeting of tiger conservation interventions and, more generally, provides a basis for the reliable integration of large carnivore monitoring data between local and landscape scales.

Journal of Applied Ecology↗
USGS2012

Strategic directions for U.S. Geological Survey water science, 2012-2022 - Observing, understanding, predicting, and delivering water science to the Nation

Executive Summary This report expands the Water Science Strategy that was begun in the USGS Science Strategy, “Facing Tomorrow’s Challenges—U.S. Geological Survey Science in the Decade 2007–2017” (U.S. Geological Survey, 2007). The report looks at the relevant issues facing society and develops a strategy built around observing, understanding, predicting, and delivering water science for the next 5 to 10 years by building new capabilities, tools, and delivery systems to meet the Nation’s water-resource needs. This report begins by presenting the vision of water science for the USGS and the societal issues that are influenced by, and in turn influence, the water resources of our Nation. The essence of the Water Strategic Science Plan is built on the concept of “water availability,” defined as spatial and temporal distribution of water quantity and quality, as related to human and ecosystem needs, as affected by human and natural influences . The report also describes the core capabilities of the USGS in water science—the strengths, partnerships, and science integrity that the USGS has built over its 130-year history. Nine priority actions are presented in the report, which combine and elevate the numerous specific strategic actions listed throughout the report. Priority actions were developed as a means of providing the audience of this report with a list for focused attention, even if resources and time limit the ability of managers to address all of the strategic actions in the report. Priority actions focus on the following: Improve integrated science planning for water. Expand and enhance water-resource monitoring networks. Characterize the water cycle through development of state-of-the-art 3-D/4-D hydrogeologic framework models at multiple scales. Clarify the linkage between human water use (engineered hydrology) and the water cycle (natural hydrology). Advance ecological flow science. Provide flood-inundation science and information. Develop rapid deployment teams for water-related emergencies. Conduct integrated watershed assessment, research, and modeling. Deliver water data and analyses to the Nation. The body of the report is presented as a hierarchal set of 5 goals, 14 objectives, and 27 strategic actions that the USGS should undertake to advance water science through year 2022. The goals deal with: Providing society the information it needs regarding the amount and quality of water in all components of the water cycle at high temporal and spatial resolution, nationwide; Advancing our understanding of processes that determine water availability; Predicting changes in the quantity and quality of water resources in response to changing climate, population, land use, and management scenarios; Anticipating and responding to water-related emergencies and conflicts; and Delivering timely hydrologic data, analyses, and decision-support tools seamlessly across the Nation to support water-resource decisions. Scientific information produced on water resources would be without value if it were not communicated to society in a fashion that can inform decisions and actions. Therefore, the chapter following the goals describes how the USGS should inform, involve, and educate society about the science it produces. This includes discussions on local outreach and the use of social media for effective communication. This report concludes with a chapter devoted to the crosscutting science issues of the Water Mission Area with the other USGS Mission Areas: Climate and Land Use Change, Core Science Systems, Ecosystems, Energy and Minerals, Environmental Health Science, and Natural Hazards. Not one of these Mission Areas stands alone—all must work together and integrate their actions to fulfill the USGS science mission for the future. This final chapter identifies the important linkages that must be realized and maintained for this integration to occur.

Open-File Report↗
USGS2019

Disease‐structured N‐mixture models: A practical guide to model disease dynamics using count data

Obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. While multistate mark–recapture models can produce high‐quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark–recapture techniques. Recently developed N ‐mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. N ‐mixture models are a type of state‐space model in which observation error is attributed to failing to detect some individuals when they are present (i.e., false negatives). The analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. We review the challenges of modeling disease dynamics and describe how N ‐mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. We also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease‐structured N ‐mixture models, and the integration of other data types with count data to inform disease dynamics. Managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. At a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.

Ecology and Evolution↗
USGS2025

When do single-species occupancy models outperform multispecies models?

Occupancy models have become increasingly popular for species monitoring and assessment, in part, because detection/non-detection data are readily obtained using a variety of methods. Multispecies occupancy models (MSOMs) can yield more accurate parameter estimates than single-species models (SSOMs) with less data through their hierarchical structure, making MSOMs an attractive option when species are hard to detect or when data collection is constrained, leading to sparse datasets. Such constraints may arise from limited sampling resources, but also occur in rare species monitoring or where preliminary results are desired to inform adaptive management. Further, experimental habitat treatments often impose spatial constraints on sampling based on the scale of their implementation. Whether a MSOM outperforms SSOMs depends on the volume of data, characteristics of the ecological community, research goals of a study and how these factors align with modeling assumptions. We performed a simulation study of hypothetical pollinator communities under varying sampling intensities for scenarios in which experimental habitat treatments produced different community-level effects. We fit occupancy models to simulated datasets and assessed model performance. At lower sampling intensities (< 20 spatial replicates and < 4 temporal replicates), MSOM community-level treatment effect estimates were biased. Even at twice this sampling intensity, SSOMs yielded more accurate species-specific effect estimates in treatment effect scenarios with high variance. In some cases, MSOMs can pull species in the tails of distributions too far toward the community mean effect, which risks incorrect conclusions concerning whether treatments help or harm individual species. When quantifying species-specific effects is the main objective, particularly for rarely observed species, SSOMs are more robust to outliers across a range of community response scenarios. Researchers can use this information to inform study design, guide simulation studies and decide whether the higher precision of MSOMs outweighs risks of improperly estimated effects for some species.

Ecology and Evolution↗
USGS2012

Morphometric-based sexual determination of Bananaquits (Coereba flaveola)

The Bananaquit (Coereba flaveola) is a common passerine throughout the tropics and has been a convenient species for ecological studies. This species has sexually monomorphic plumage and cannot be reliably sexed unless in breeding condition. This is problematic for demographic and comparative studies, which are contingent upon accurately aging and sexing individuals. Although male Bananaquits are larger than females, there is overlap in both wing chord and mass. We used morphometric data collected over eight years to develop a predictive model based on logistic regression to assign adult Bananaquits to sex. Our model classified 96% of validation individuals to the correct sex. We suggest that this approach may enhance ecological studies of the species by facilitating correct sex determination independent of breeding status. We believe our modeling approach is applicable elsewhere but, because there may be geographical variation across the species distribution, models will need to be customized to local populations.

Guana Island↗