Ecosphere

The U.S. Fish and Wildlife Service monitors species-specific waterfowl (ducks, seaducks, geese, and brant) harvest through two hunter surveys, one that estimates the total harvest for each waterfowl group, and a second that estimates the species composition of each waterfowl group. Point estimates for species-specific harvest can be computed by multiplying the estimated total harvest by the estimated proportion of the total harvest of each species. However, to date, no uncertainty estimates have been available. Here, we combine these two data sources to provide species-specific harvest estimates at the state and flyway level while characterizing the uncertainty via Bayesian estimation. We take a similar approach to Smith et al. (2022), providing both estimates that treat yearly data as independent and estimates that share information across years via a random walk process. We then discuss the advantages and disadvantages of each approach.

We present gridded 8 km-resolution data products of the estimated biomass, basal area, and stem density of tree taxa at the time of Euro-American settlement of the midwestern United States for the states of Minnesota, Wisconsin, Michigan, Illinois, and Indiana. The data come from settlement-era Public Land Survey (PLS) data (ca. 0.8-km resolution) of trees recorded by land surveyors. The surveyor notes have been transcribed, cleaned, and processed to estimate biomass, basal area, and stem density at individual points on the landscape. The point-level data are then aggregated within grid cells and statistically smoothed using a statistical model that accounts for zero-inflated continuous data with smoothing based on generalized additive modeling techniques and approximate Bayesian uncertainty estimates. We expect this data product to be useful for understanding the state of vegetation in the midwestern United States prior to large-scale Euro-American settlement. In addition to specific regional questions, the data product can serve as a baseline against which to investigate how forests and ecosystems change after intensive settlement. The data products (including both raw and statistically smoothed estimates at the 8-km scale) are being made available at the LTER network data portal as version 1.0.

Plant invasions often lead to homogenization of the plant community, but the potential for plant invasions to cause homogenization of other trophic levels is under-studied in many systems. We tested whether the bird community in Phragmites australis-invaded marsh would exhibit spatial and temporal taxonomic homogenization compared to remnant cattail and meadow marsh. We compared the bird community using marsh invaded by P. australis and remnant, uninvaded marsh vegetation in a year with average water depths and a year with above-average water depths in the coastal marshes of a World Biosphere Reserve. Our results demonstrate strong evidence for spatial and temporal homogenization of the wetland bird community following P. australis invasion. The birds present in P. australis-invaded marsh were a nested subset of those present in remnant marsh, and total beta diversity decreased when water depths were above average. In contrast, total beta diversity was high in remnant marsh vegetation and stable between the two years. The distinctively structured vegetation zones in remnant (uninvaded) marsh yields structural complexity and habitat heterogeneity that supports greater taxonomic turnover in the bird community. Our study provides evidence that invasion by a plant has resulted in biological homogenization of the wetland bird community.

Continuous indices of functional diversity are popular in studies examining community structure and ecosystem function across a wide range of subfields from paleontology to range management. These indices were designed to replace the use of more arbitrary, discrete functional groups or guilds; however, the effect of typical methodological decisions on these new continuous measures has not been fully investigated. To test the effect of using ordinal traits in functional diversity analysis, I first calculated relative functional diversity index values in real plant communities with real continuous trait data and Euclidean distances. I then compared these original values to "treatment" functional diversity index values obtained by discretizing the trait data and using Gowers distance. Agreement between original and treatment values was highly unpredictable and often abysmal. Small methodological choices, such as whether to treat a functional trait as continuous (mm) or ordinal ("small", "medium", "large"), could completely change a perceived functional diversity relationship along an environmental gradient. Some parameter combinations returned results that were no better than random noise. Because simple methodological choices can have such a large impact on continuous functional diversity indices, it is ambiguous whether analyses using ordinal traits are actually measuring an underlying functional diversity relationship between communities or just reflecting the arbitrary parameter choices of researchers.

Species are shifting their ranges in response to climate change. There remain many unknowns about relative impacts of range-expanding ecosystem engineers between historical and expanded habitats, however. The mud fiddler crab Minuca pugnax (=Uca pugnax) is shifting its range northward likely due to increased warming in the Gulf of Maine. A burrowing crab, M. pugnax affects ecosystem functioning in salt marshes south of Cape Cod, Massachusetts with unknown effects in expanded marsh habitats over 150km to the north. We therefore studied the M. pugnax range expansion to determine the extent that range expanding ecosystem engineers are influencing ecosystem functioning expanded ranges relative to historical habitats. We installed in 2017 and 2018 a series of crab-inclusion cages at both the UMass Boston Nantucket Field Station (historical range) and the Plum Island Estuary Long Term Ecological Research site (PIE-LTER, expanded range). For each site, year, and block, we measured in the beginning and end of the three-month experiment metrics of sediment strength, primary production, and decomposition. We developed and tested causal models using structural equation modeling (SEM) to determine direct and indirect effects of fiddler crabs on ecosystem functions. Despite site, year, and block variability, local environment influenced burrow density, which directly affected sediment strength and indirectly affected primary production in both ranges. Overall, understanding range-expanding ecosystem engineers in historical ranges was predictive for how they influence expanded habitats, despite inter-site heterogeneity. Therefore, it is critical to study relative impacts of range-expanding ecosystem engineers to understand total impacts of global range shifts.

Invasion of nonindigenous species is considered one of the most urgent problems affecting native ecosystems and agricultural systems. Mechanistic models that account for short-term population dynamics can improve prediction because they incorporate differing demographic processes that link the environmental conditions of a spatial location explicitly with the invasion process. Yet short-term population dynamics are rarely accounted for in spatial models of invasive species spread. Accounting for transient population dynamics, we predict the population growth rate and establishment probability of wild pigs following introduction into any location in North America. We compared predicted population growth rate with observed geographic rates of spread and found significant relationships between the annual rate of spread and population growth rates. We used geospatial data on the distribution of mast producing tree species (a principle forage resource of wild pigs) and agricultural crops that can replace mast in their diets to predict population dynamics using transient population simulations. We simulated populations under different initial population sizes (i.e. number of introduced individuals, often termed propagule size) and for different amounts of time following introduction. By varying the initial population size and simulation time, we were able to identify areas in North America with high probability for establishment of wild pigs if introduced. Our findings can be used to inform surveillance and removal efforts to reduce the potential for establishment and spread of wild pigs.

Expanding human enterprise leading to resource subsidies for generalist species has resulted in widespread increases in common raven (Corvus corax) populations across the Western U.S. Ravens are an efficient predator and increased population abundance has led to adverse effects to multiple sensitive prey species. In regions where problematic interactions between ravens and their prey exist, managers seek efficient and effective tools for monitoring and controlling expanding raven populations. We previously developed a Rapid Assessment Function (RAF) for managers to quickly estimate raven population density and assess the need for management actions. We developed the RAF for the Great Basin (GB RAF) by first estimating raven density using robust distance sampling protocols with >30,000 raven point count surveys from sagebrush ecosystems in California, Nevada, Idaho, and Oregon across 131 field sites and years. We then used the relationship between raven density estimates from distance sampling and n ravens observedsite-year/ n surveyssite-year (that is, raven index) at each site-year combination to develop a function that accounts for detection probability and adjusts simple counts to provide a prediction of true density. Our function produced reliable density estimates given approximately 50-100 surveys, thereby reducing the field-based and analytical efforts typically needed to estimate raven density, facilitating more efficient raven management in open sagebrush habitats. In this study, we sought to test our original GB RAF using data from sagebrush ecosystems outside of the Great Basin. Using raven point count data from two field site units in Washington state collected from 2016 to 2023, we calculated density estimates from distance sampling methods, comparable to what was done for previous analyses. We then used the GB RAF to generate predictions of density and compared those values to the more robust estimates from distance sampling. Additionally, we developed modified RAFs specifically for Washington data (WA RAFs) to assess how well they predicted raven density compared to the GB RAF. We found the detection curves estimated for the Washington sites largely aligned with those used to generate the original GB RAF. Furthermore, the estimates from the GB RAF exhibited similar or higher correlation with densities calculated from distance models (Pearsons r = 0.73) than the modified WA RAFs with 1.33 km and 1.25 km truncation distances (Pearsons r = 0.63 and 0.73, respectively). Producing an equivalently performing modified WA RAF would likely necessitate more data to reduce estimation error and produce more reliable estimates. These results provide evidence for the applicability of our GB RAF for more widespread use within sagebrush ecosystems, possibly negating the need for locally developed RAFs. Continued assessments of the GB RAF outside of the Great Basin would further verify its applicability across the sagebrush biome.

Silver carp Hypophthalmichthys molitrix, are a highly mobile aquatic invasive species in the United States. The James River, South Dakota, USA, is a tributary of the Missouri River and is considered the northwestern leading edge of their invasion front. Understanding silver carp movement patterns on the cusp of this invasion is key to combatting the northwestward expansion of the species. We used passive telemetry to observe large-scale movement patterns of silver carp in the James River, South Dakota. Fifty silver carp were implanted with acoustic transmitter tags in June 2021, and movement data was recorded over a 1.5-year period. Most individuals exhibited site fidelity and stayed within the James River throughout the duration of the study. We used environmental data (i.e., temperature, dissolved oxygen, daily mean discharge, the change in discharge over 24 h and 48 h) and movement data collected from passive telemetry receivers to understand and predict silver carp movement cues in the James River. Daily mean discharge ("flow") was the most important predictor of silver carp movement in the James River. As flow increased, the probability of movement increased from 59% (95% CrI: 34% to 81%) at 1.5 m3/s to 94% (95% CrI: 80 to 99%) at 100 m3/s. In this study, silver carp exhibited a high propensity for movement within the James River, especially during periods of high flow. To prevent further northwestward expansion of these fish, silver carp movements must continue to be monitored and removal and prevention of further range expansion must be prioritized.

Due to climate change and past logging and fire suppression, the western US are experiencing increasingly large and frequent wildfires. Understanding how wildlife respond to these mega-fires is becoming increasingly relevant to protect and manage these populations. However, the lack of predictability inherent in such events makes studies difficult to plan. We took advantage of a large high-severity wildfire that burned adjacent to an ongoing study of mule deer (Odocoileus hemionus) on their summer range upslope of the fire to investigate their displacement onto our study area both immediately and upon their return to summer range the following year. We used spatial capture-recapture models in conjunction with noninvasive fecal DNA sampling to estimate density and non-spatial Pradel robust-design models to estimate apparent survival and recruitment rates. Compared to density before the fire, we observed an increase in deer density and an increase in per-capita recruitment rates one month after the fire. These findings suggest that the immediate response of at least some deer was to flee the fire upslope onto the study area rather than to downslope toward their winter range. These changes did not carry over into the following year, however, suggesting that deer formerly using the burned area as summer range may have returned there despite the high severity of the fire, or may have chosen new areas for their summer range. This suggests that, at least in the short term, the fire did not negatively affect the deer population.

There are increasing concerns about the decreasing population trends of small mammalian carnivores around the world. With limited knowledge about their ecology and natural history, small mammal conservation and management remains difficult. To address one of these deficiencies for western spotted skunks (Spilogale gracilis), we investigated their diet in the Oregon Cascades of the Pacific Northwest during 2017 - 2019. We collected 130 spotted skunk scats opportunistically and with detection dog teams and identified prey items using DNA metabarcoding and mechanical sorting. Western spotted skunk diet consisted of invertebrates such as wasps, millipedes, and gastropods, vertebrates such as small mammals, amphibians, and birds, and plants such as Gaultheria, Rubus, and Vaccinium. Diet also consisted of items such as black-tailed deer that were likely scavenged. Comparison in diet by season revealed that spotted skunks consumed more insects during the dry season (June - August) and marginally more mammals during the wet season (September - May). We observed similar diet in areas with no record of human disturbance and areas with a history of logging. Western spotted skunks provide important food web linkages between aquatic, terrestrial, and arboreal systems by facilitating energy and nutrient transfer, and serve important functional roles of seed dispersal and scavenging. Through prey-switching, western spotted skunks may dampen the effects of irruptions of prey, such as wasps during dry springs and summers, which could then provide ecosystem resilience to environmental change.

A number of "shortcuts" for assessing biodiversity and prioritizing conservation action have been proposed, one of which is to focus on a subset of the most common species. I critically evaluated the claim that common species better explain patterns of species richness than rare species, using bird data from the same spatial extent at two markedly different spatial resolutions and a third dataset for a larger spatial extent. I did find situations where common species convey more information about species richness patterns than rare species, such as in sequential correlations of species sets with total species richness. However, "hotspots" of species richness tended to be most associated with species occurring with intermediate frequency, rather than the most common (or most rare) species. Furthermore, differences in the degree and sequence of rarity across the three datasets meant that conclusions drawn from species in one dataset did not necessarily hold in the others. Overall, therefore, I found little evidence that common species alone could provide a satisfactory shortcut to understanding biodiversity, particularly given that rare species are often facing the greatest risk of extirpation or extinction. Drawing upon citizen scientists to aid in monitoring and ensuring that unusual or unique ecosystem types and configurations are surveyed may be invaluable in obtaining the thorough understanding of biodiversity needed for successful conservation outcomes.

Quantifying and estimating trends in wildlife abundance is critical for their management and conservation. Harvest-based indices are often used as a surrogate index for wildlife population. Sex-Age-Kill (SAK) models generally use the age-at-harvest of males and females, combined with annual mortality and reproduction rates to calculate a preharvest population estimate. We used and SAK model to estimate abundance for bobcats from 1981-2015. Pre-hunt population size ranged from approximately 1630-2148 during 1981-1995 after which the population increased to a maximum of 4439 in 2005 before declining to 2598 in 2013. Pre-hunt population size was highly correlated an index of abundance from winter track counts (r = 0.93). We found that the model, as currently implemented by WNDR, appears to provide an accurate trend of statewide bobcat abundance. SAK models more logistically feasible for long-term evaluations of population trends overbroad spatial extents than more intensive methods. While SAK models may be the only technique available to wildlife managers for estimating the abundance of harvested species, we encourage additional research to evaluate the effects of potential biases on estimates of abundance.

While reports of mass mortality events (MMEs) are increasing in the literature, comparing the incidence of MMEs through time, among locations, or taxa is problematic without accounting for detection probabilities. MMEs involving small, cryptic species can be difficult to detect even during the event, and degradation and scavenging of carcasses can make the window for detection very short. As such, the number or occurrence rate of MMEs may often be severely underestimated, especially with infrequent observations. We develop a simple modeling framework to quantify the probability of detecting an MME as a function of the observation frequency relative to the rate at which MMEs become undetectable. This framework facilitates the design of surveillance programs and may be extended to correct estimates of the incidence of MMEs from actual surveillance data for more appropriate analyses of trends through time and among taxa.

Hypogeous fungi ("truffles") are challenging to study because they produce underground sporocarps that may not be found during traditional fungal surveys. Commercially valuable truffles are located using scent-detection dogs trained on truffles. However, the dogs are not necessarily limited to commercial truffle species when trained on other truffle taxa of interest. For example, Genea (Pyronemataceae, Ascomycota) is a genus of common but difficult to find truffles that develops small and often soil-colored hypogeous ascomata (truffles).. We used a truffle dog trained to locate Genea truffles in the southern Willamette Valley and associated mountains in western Oregon, U.S.A, recording when the sporocarps were present at a wide range of elevations (113 to 1879 m). We found Genea was present in half of our surveys, and noted that it rarely fruited in areas that had experienced wildfire. This study demonstrates the value of using truffle dogs in documenting truffle diversity, particularly those that are difficult to locate visually, and provides further evidence for the abundance of Genea.

The growth of biodiversity data sets generated by citizen scientists continues to accelerate. The availability of such data has greatly expanded the scale of questions researchers can address. Yet, error, bias, and noise continue to be serious concerns for analysts, particularly when data being contributed to these giant online data sets are difficult to verify. Counts of birds contributed to eBird, the worlds largest biodiversity online database, present a potentially useful resource for tracking trends over time and space in species abundances. We quantified counting errors in a sample of 1406 eBird checklists by comparing numbers contributed by birders (N=246) who visited a popular birding location in Oregon, USA, with numbers generated by a professional ornithologist engaged in a long-term study creating benchmark (reference) measurements of daily waterbird counts. We focused on waterbirds, which are easily visible at this site. We evaluated potential predictors of count differences, including characteristics of contributed checklists, of each species, and of time of day and year. Count differences were biased toward undercounts, with more than 75% of counts being below the daily benchmark value. When only checklists that actually reported a species known to be present were included, median count errors were -29.1% (range: 0 to -42.8 %; N=20 species). Model sets revealed an important influence of each species reference count, which varied seasonally as waterbird numbers fluctuated, and of percent of species known to be present each day that were included on each checklist. That is, checklists indicating a more thorough survey of the species richness at the site also had, on average, lower counting errors. However, even on checklists with the most thorough species lists, counts were biased low and exceptionally variable in their accuracy. To improve utility of such bird count data, we suggest three strategies to pursue in the future. One is to assess additional options for analytically determining how to select checklists that have the highest probability of including less biased count data, as well as exploring options for correcting bias during the analysis stage. Another is to add options for users to provide additional information that helps analysts choose checklists, such as an option for users to tag checklists where they focused on obtaining accurate counts. We also recommend exploration of opportunities to effectively calibrate citizen-science bird count data by establishing a formalized network of marquis sites where dedicated observers regularly contribute carefully collected benchmark data.

Ecologists use a variety of systematically and opportunistically sampled count data to estimate bird abundance, and integrating or fusing different datasets has emerged as a critical challenge in recent years. While previous work provides data integration methodology for occupancy (presence/absence) estimation, methods for abundance estimation that account for imperfect detection and disparate survey protocols remains an active area of research. Here we show how to integrate systematically collected removal-in-time distance sampling data from the Integrated Monitoring in Bird Conservation Regions (IMBCR) program with North American Breeding Bird Survey (BBS) point counts and eBird community science observations. Using the Grasshopper Sparrow (Ammodramus savannarum) in the Great Plains of the United States as a focal species, we demonstrate that BBS and eBird data improve predictive performance for IMBCR count data, providing more spatially refined and precise estimates of abundance at regional scales. Data fusion increased predictive performance even despite relatively weak spatial correlations among data sets. The methodology developed here provides a principled way to fuse data when estimating abundance with distance sampling, that accounts for imperfect detection and variable effort.

Lahontan Cutthroat Trout Oncorhynchus clarkii henshawi have experienced massive declines in their native range and are now a threatened species under the US Endangered Species Act. A key management goal for this species is re-establishing extirpated populations using translocations and conservation hatcheries. In California USA, two broodstocks (Pilot Peak and Independence Lake) are available for translocation, in addition to potential wild sources. Yet suitability of these sources for re-introduction in different ecosystem types and regions remains an open and important topic. We conducted growth experiments using Lahontan Cutthroat Trout stocked into Sagehen Creek, CA USA. Experiments evaluated both available broodstocks and a smaller sample of wild fish translocated from a nearby creek. Fish from the Independence Lake source had significantly higher growth in weight and length compared to the other sources. Further, Independence Lake fish were the only stock that gained weight on average over the duration of the experiment. Our experiments suggest fish from the Independence Lake brood stock may be useful for re-introduction efforts into small montane headwater streams in California.

O_LIUngulate browsing threatens forest tree regeneration and diversity across forests in North America and other temperate regions but is inadequately monitored. This reflects both inadequate commitment and the lack of efficient, reliable methods to accurately track these impacts across sites and over time. C_LIO_LIThe twig age method estimates time: how long twigs grow before being browsed, providing a direct measure of herbivory. The original method aged twigs on maple (Acer) seedlings, but it works well in other deciduous species. Given ungulate preferences for some species, it is important to adjust twig ages to correct for these. C_LIO_LIUsing multiple datasets from many species and sites across the upper Midwest, I confirm the power and sensitivity of the twig age method for assessing and comparing white-tailed deer impacts over time, among species, and across sites. Twig ages respond differently to seedling heights when deer are present, providing an additional indicator. I extend the original method to rank and adjust for differential browsing among species in two ways: using least square means from statistical models and using ratios to adjust values to an Acer saccharum standard. These adjustments match, supporting the use of either to accurately track shifts in browse pressure over time and across sites even when these differ in species composition. C_LIO_LIThe adjusted twig-age method is highly efficient, allowing a sole worker to score 100+ seedlings/saplings within an hour to accurately gauge real-time ungulate impacts at a site. The reliability, power, and versatility of adjusted twig age data set a standard for assessing browse pressure and provide an efficient vehicle to expand programs to monitor deer impacts across landscapes. C_LI

O_LIJolly Seber (JS) models are an appealing class of capture-recapture models for modeling open populations because they allow for inferences about an array of population parameters, including abundance, survival, and recruitment. Multiple formulations of JS models have been developed and include both maximum likelihood and Bayesian approaches. Bayesian approaches offer greater flexibility; however, they are often extremely slow when applied to moderate to large populations because the latent states of all observed individuals, as well as any potential unobserved individuals (parameter-expanded data augmentation), must be simulated at each time step. C_LIO_LIJS model implementations can be sped up dramatically through marginalization, whereby the conditional likelihoods of states are tracked for unique capture histories, rather than the simulated states of all individuals. Here we describe a marginalized implementation of a multistate JS model and compare its performance to that of an implementation using simulated discrete states. We fit models to data generated under two scenarios, one in which no data was missing and another in which 25% of data was randomly removed. To illustrate how marginalization can accommodate more complex models and datasets, we describe a modified version of the model for application to populations with transients and fit the model to simulated data and to data collected on Kentucky Warblers (Geothlypis formosa) as part of the Monitoring Avian Productivity and Survivorship (MAPS) program. C_LIO_LIBoth the discrete latent state and marginalized JS model implementations performed similarly with respect to bias and coverage; however, the marginalized implementation was roughly 1,000 times faster when no data were missing and 100 times faster when 25% of data was missing. The marginalized model accommodating transients also converged quickly and a more complex version of the model applied to the larger multi-site Kentucky Warbler data set yielded useful estimates of most parameters within a reasonable time frame (hours). C_LIO_LIGains in efficiency provided by marginalization shown here should stimulate additional study and development of this useful class of models and provide new opportunities for real-world applications to large complex data sets. C_LI

Understanding how detection probability varies over time, space, or in response to measurable covariates is important to inform the monitoring and assessment of many species. A standard model to understand detectability - the availability/perception model - admits that detection probability is the composite of two components: availability and perception. Availability is largely affected by environmental and behavioral factors, whereas perception is primarily affected by attributes of individual observers and survey protocols, and thus can potentially be partially controlled by survey design. We designed and implemented a field study to understand the perception component of detection for Eastern box turtles (Terrapene carolina carolina) using visual encounter surveys. We obtained and deployed museum specimens of Eastern box turtle shells and subjected them to visual search surveys by observers in realistic field situations. Overall, about 50% of the box turtle shells were detected by observers including, 41.5% in the partially visible state and 63% in the fully visible state. There were significant differences among observers, which may be due to observer-specific variation in search technique -- the observers varied in how well they achieved the protocol guidance.