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At least 919 records · Page 51Linked to original sources

USGS2017

Multi-level multi-task learning for modeling cross-scale interactions in nested geospatial data

Predictive modeling of nested geospatial data is a challenging problem as the models must take into account potential interactions among variables defined at different spatial scales. These cross-scale interactions, as they are commonly known, are particularly important to understand relationships among ecological properties at macroscales. In this paper, we present a novel, multi-level multi-task learning framework for modeling nested geospatial data in the lake ecology domain. Specifically, we consider region-specific models to predict lake water quality from multi-scaled factors. Our framework enables distinct models to be developed for each region using both its local and regional information. The framework also allows information to be shared among the region-specific models through their common set of latent factors. Such information sharing helps to create more robust models especially for regions with limited or no training data. In addition, the framework can automatically determine cross-scale interactions between the regional variables and the local variables that are nested within them. Our experimental results show that the proposed framework outperforms all the baseline methods in at least 64% of the regions for 3 out of 4 lake water quality datasets evaluated in this study. Furthermore, the latent factors can be clustered to obtain a new set of regions that is more aligned with the response variables than the original regions that were defined a priori from the ecology domain.

Conference Paper↗

USGS2019

Back to the future: Rebuilding the Everglades

Society values landscapes that are engrained in cultural tradition and have a rich connection with human history. As such, there has been a concerted effort to look at the pristine past and develop plans to move the past into the future. However, bringing the past back is constrained by hysteretic changes, irrevocable damages, and anthropogenic trends that do not reflect past conditions. The scale of the Everglades and its importance to water supply and flood control is such that a full recovery, to past, pre-drainage conditions, is not possible. What is possible? The answer is the federally authorized Comprehensive Everglades Restoration Plan (CERP) and the first, most significant implementation of the $12 Billion CERP is the $2 Billion Central Everglades Planning Project (CEPP). CEPP is our “flux-capacitor” in the DeLorean sports car that generates the ability to go back and forth in time, in the movie series “Back to the Future.” The primary hydrological modeling outputs of CEPP came from a version of the Regional Simulation Model (RSM), developed by the South Florida Water Management District. The RSM is the DeLorean vehicle, designed to carry the bags of ecological restoration. Unfortunately, the capacity of this vehicle (i.e., CEPP) is limited, but is it large enough? Will CEPP make a difference? The 20-year RSM simulations (1965 – 1985) without restoration showed nine dry periods when there was no water in the sloughs of Everglades National Park (ENP). When the model was run with CEPP conditions, all of these extreme dry conditions were eliminated. The impact of this was most apparent for fish, especially the size classes that wading birds eat. With our DeLorean (i.e. the RSM) we saw a 60-90% increase in fish density. As one might expect, the birds in our alternative future responded to the fish. The increased volume, flow, and connectivity in the CEPP simulations significantly improved the foraging response of all wading bird species, especially in Water Conservation Area 3 (WCA-3) and ENP. Foraging conditions for an average CEPP year improved by 25-100%. Further downstream, the CEPP simulations showed increased delivery of low nutrient fresh water to the Coastal Everglades and Florida Bay that displaced the relatively P-rich marine water, increased water transparency, and thus decreased algal blooms. However, in a future with accelerating sea levels and estuarine lakes with legacy phosphorus (P), how much more fresh water will be needed to maintain submerged aquatic vegetated habitats? The quest for Everglades Restoration will reach a resource management milestone with the implementation of CEPP. CEPP successfully used a broad suite of hydrological, ecological and societal models to build an acceptable and feasible adaptive management vision of the future. It has been a long and difficult journey, but what we have learned in the process will guide future travelers back in time.

Florida↗

USGS2017

Species distributions models in wildlife planning: agricultural policy and wildlife management in the great plains

We know economic and social policy has implications for ecosystems at large, but the consequences for a given geographic area or specific wildlife population are more difficult to conceptualize and communicate. Species distribution models, which extrapolate species-habitat relationships across ecological scales, are capable of predicting population changes in distribution and abundance in response to management and policy, and thus, are an ideal means for facilitating proactive management within a larger policy framework. To illustrate the capabilities of species distribution modeling in scenario planning for wildlife populations, we projected an existing distribution model for ring-necked pheasants ( Phasianus colchicus ) onto a series of alternative future landscape scenarios for Nebraska, USA. Based on our scenarios, we qualitatively and quantitatively estimated the effects of agricultural policy decisions on pheasant populations across Nebraska, in specific management regions, and at wildlife management areas.

Nebraska↗

USGS2018

Waterfowl spring migratory behavior and avian influenza transmission risk in the changing landscape of the East Asian-Australasian Flyway

Avian influenza has advanced from a regional concern to a global health issue with significant economic, trade, and public health implications. Wild birds, particularly waterfowl (Anseriformes), are known reservoirs for low-pathogenic avian influenza viruses (AIV) and recent studies have shown their potential in the spread of highly pathogenic forms of virus. East Asia remains an epicenter for the emergence of novel strains of AIV, however, information on movement ecology of waterfowl, and subsequently a clearer understanding of disease transmission risks in this region has been greatly lacking. To address this, we marked two species of wild waterfowl, northern pintail (Anas acuta) and Eurasian wigeon (Anas penelope) , with satellite transmitters on their wintering grounds in Hong Kong, China to study the northward spring migration in the East Asian-Australasian Flyway in relation to disease transmission factors. Northern pintail were found to initiate migration 42 days earlier, travel 2,150 km farther, and perform 4.4 more stopovers than Eurasian wigeon. We found both species used similar stopover locations including areas along the Yangtze River near Shanghai, Bohai Bay and Korea Bay in rapidly developing regions of the Yellow Sea, and the Sea of Okhotsk where the species appeared to funnel through a migratory bottleneck. Both species appeared to exhibit strong habitat selection for rice paddies during migration stopovers, a habitat preference which has the potential to influence risks of AIV outbreaks as rapid land use and land cover changes occur throughout China. Both species had greatest association with H5N1 outbreaks during the early stages of migration when they were at lower latitudes. While Eurasian wigeon were not associated with outbreaks after the mean date of wintering ground departures, northern pintail were associated with outbreaks until the majority of individuals departed from the Yellow Sea, a migratory stopover location. Our results show species-level differences in migration timing and behavior for these common and widespread species, demonstrating the need to consider their unique temporal and spatial movement ecology when incorporating wild birds into AIV risk modeling and management.

Frontiers in Ecology and Evolution↗

USGS2021

Instrumental variable methods in structural equation models

Instrumental variable regression (RegIV) provides a means for detecting and correcting parameter bias in causal models. Widely used in economics, recently several papers have highlighted its potential utility for ecological applications. Little attention has thus far been paid to the fact that IV methods can also be implemented within structural equation models (SEMIV). In this paper I present the motivations, requirements and basic procedures for using SEMIV. I first consider causal inference and IVs from the perspective of a randomized experiment with partial control of the cause of interest. I consider common sources of bias, the role of randomization and limits to its capacity to exclude bias. Sources of bias include omitted confounders, reciprocal causation, reverse causation and measurement error, all of which can all be seen as a single problem—endogeneity. The approach to estimating IV models most commonly used in econometric practice, two-stage least squares regression (2SLS), is explained, followed by a brief exposition of the covariance modelling approach used in SEM. Using data from an ecological field experiment, I illustrate the use of the treatment variable as an IV and then illustrate procedures for evaluating candidate variables that might serve as additional IVs. IV methods are shown to be useful for both detecting endogeneity and removing its influences. I illustrate some of the ways that bias can be generated, as well as diagnostic capabilities and means for remedy embedded within SEM. Procedures for screening and evaluating additional IVs reveal valuable lessons regarding the theoretical requirements and empirical standards for IVs. SEMIV provides a useful way to detect and control for bias. I suggest that the use of IVs within the SEM framework can support the simultaneous pursuit of causal inference and explanatory modelling, a common pair of aspirations for ecologists. Moving forward, there is a need for a better understanding of the capabilities of SEMIV and requirements for successful application.

Methods in Ecology and Evolution↗

USGS2013

A spatial mark–resight model augmented with telemetry data

Abundance and population density are fundamental pieces of information for population ecology and species conservation, but they are difficult to estimate for rare and elusive species. Mark-resight models are popular for estimating population abundance because they are less invasive and expensive than traditional mark-recapture. However, density estimation using mark-resight is difficult because the area sampled must be explicitly defined, historically using ad-hoc approaches. We develop a spatial mark-resight model for estimating population density that combines spatial resighting data and telemetry data. Incorporating telemetry data allows us to inform model parameters related to movement and individual location. Our model also allows <100% individual identification of marked individuals. We implemented the model in a Bayesian framework, using a custom-made Metropolis-within-Gibbs Markov chain Monte Carlo algorithm. As an example, we applied this model to a mark-resight study of raccoons on South Core Banks, a barrier island in Cape Lookout National Seashore, North Carolina. We estimated a population of 186.71 ± 14.81 individuals, which translated to a density of 8.29 ± 0.66 individuals per km 2 . The model presented here will have widespread utility in future applications, especially for species that are not naturally marked.

Ecology↗

USGS2021

Predicting non-native insect impact: Focusing on the trees to see the forest

Non-native organisms have invaded novel ecosystems for centuries, yet we have only a limited understanding of why their impacts vary widely from minor to severe. Predicting the impact of non-established or newly detected species could help focus biosecurity measures on species with the highest potential to cause widespread damage. However, predictive models require an understanding of potential drivers of impact and the appropriate level at which these drivers should be evaluated. Here, we used non-native, specialist herbivorous insects of forest ecosystems to test which factors drive impact and if there were differences based on whether they used woody angiosperms or conifers as hosts. We identified convergent and divergent patterns between the two host types indicating fundamental similarities and differences in their interactions with non-native insects. Evolutionary divergence time between native and novel hosts was a significant driver of insect impact for both host types but was modulated by different factors in the two systems. Beetles in the subfamily Scolytinae posed the highest risk to woody angiosperms, and different host traits influenced impact of specialists on conifers and woody angiosperms. Tree wood density was a significant predictor of host impact for woody angiosperms with intermediate densities (0.5–0.6 mg/mm 3 ) associated with highest risk, whereas risk of impact was highest for conifers that coupled shade tolerance with drought intolerance. These results underscore the importance of identifying the relevant levels of biological organization and ecological interactions needed to develop accurate risk models for species that may arrive in novel ecosystems.

Biological Invasions↗

USGS2006

Multistate survival models and their extensions in Program MARK

Program MARK provides .100 models for the estimation of population parameters from mark?encounter data. The multistate model of Brownie et al. (1993) and Hestbeck et al. (1991) allows animals to move between states with a probability of transition. The simplest multistate model is an extension of the Cormack?Jolly?Seber (CJS) live recapture model. arameters estimated are state-specific survival rates and encounter probabilities and transition probabilities between states. The multistate model provides a valuable framework to evaluate important ecological questions. For example, estimation of state-specific survival and transition probabilities between the biological states of breeders and nonbreeders allows estimation of the cost of reproduction. Transitions between physical states, such as spatial areas, provide estimates needed for meta-population models. The basic multistate model uses only live recaptures, but 3 extensions are included in MARK. A multistate model with live and dead encounters is available, although the dead encounters are not state specific. Robust-design multistate models are also included in MARK, with both open and closed robust designs. These models assume that animals move between states only between primary sessions of the robust design. For the closed robust design, we can specify 12 different data types for the modeling of encounter probabilities during the primary session, including 6 versions of the closed model likelihood incorporating population size (N) directly in the likelihood, and 6 versions of the Huggins model in which N is estimated as a derived parameter outside the likelihood. One assumption that is generally necessary to estimate state-specific survival rates in the multistate model is that transitions take place immediately before encounter occasions. Otherwise, survival rates over the interval between encounter occasions are a mix of survival rates over multiple states. Advantages of using MARK to estimate the parameters of the various multistate models include flexibility of model specification to include group, time, and individual covariates, estimation of variance components, model averaging of parameter estimates, and Bayesian parameter estimation using Markov chain Monte Carlo procedures on the logit scale.

Journal of Wildlife Management↗

USGS2012

The resilience and functional role of moss in boreal and arctic ecosystems

Mosses in northern ecosystems are ubiquitous components of plant communities, and strongly influence nutrient, carbon and water cycling. We use literature review, synthesis and model simulations to explore the role of mosses in ecological stability and resilience. Moss community responses to disturbance showed all possible responses (increases, decreases, no change) within most disturbance categories. Simulations from two process-based models suggest that northern ecosystems would need to experience extreme perturbation before mosses were eliminated. But simulations with two other models suggest that loss of moss will reduce soil carbon accumulation primarily by influencing decomposition rates and soil nitrogen availability. It seems clear that mosses need to be incorporated into models as one or more plant functional types, but more empirical work is needed to determine how to best aggregate species. We highlight several issues that have not been adequately explored in moss communities, such as functional redundancy and singularity, relationships between response and effect traits, and parameter vs conceptual uncertainty in models. Mosses play an important role in several ecosystem processes that play out over centuries – permafrost formation and thaw, peat accumulation, development of microtopography – and there is a need for studies that increase our understanding of slow, long-term dynamical processes.

New Phytologist↗

USGS2010

Linking process to pattern: estimating spatiotemporal dynamics of a wildlife epidemic from cross‐sectional data

Underlying dynamic event processes unfolding in continuous time give rise to spatiotemporal patterns that are sometimes observable at only a few discrete times. Such event processes may be modulated simultaneously over several spatial (e.g., latitude and longitude) and temporal (e.g., age, calendar time, and cohort) dimensions. The ecological challenge is to understand the dynamic latent processes that were integrated over several dimensions (space and time) to produce the observed pattern: a so‐called inverse problem. An example of such a problem is characterizing epidemiological rate processes from spatially referenced age‐specific prevalence data for a wildlife disease such as chronic wasting disease (CWD). With age‐specific prevalence data, the exact infection times are not observed, which complicates the direct estimation of rates. However, the relationship between the observed data and the unobserved rate variables can be described with likelihood equations. Typically, for problems with multiple timescales, the likelihoods are integral equations without closed forms. The complexity of the likelihoods often makes traditional maximum‐likelihood approaches untenable. Here, using seven years of hunter‐harvest prevalence data from the CWD epidemic in white‐tailed deer (Odocoileus virginianus) in Wisconsin, USA, we develop and explore a Bayesian approach that allows for a detailed examination of factors modulating the infection rates over space, age, and time, and their interactions. Our approach relies on the Bayesian ability to borrow strength from neighbors in both space and time. Synthesizing a number of areas of event time analysis (current‐status data, age/period/cohort models, Bayesian spatial shared frailty models), our general framework has very broad ecological applicability beyond disease prevalence data to a number of important ecological event time analyses, including general survival studies with multiple time dimensions for which existing methodology is limited. We observed strong associations of infection rates with age, gender, and location. The infection rate appears to be increasing with time. We could not detect growth hotspots, or location by time interactions, which suggests that spatial variation in infection rates is determined primarily by when the disease arrives locally, rather than how fast it grows. We emphasize assumptions and the potential consequences of their violations.

Wisconsin↗

USGS1996

Potential role of vegetation feedback in the climate sensitivity of high-latitude regions: A case study at 6000 years B.P.

Previous climate model simulations have shown that the configuration of the Earth's orbit during the early to mid-Holocene (approximately 10–5 kyr) can account for the generally warmer-than-present conditions experienced by the high latitudes of the northern hemisphere. New simulations for 6 kyr with two atmospheric/mixed-layer ocean models (Community Climate Model, version 1, CCMl, and Global ENvironmental and Ecological Simulation of Interactive Systems, version 2, GENESIS 2) are presented here and compared with results from two previous simulations with GENESIS 1 that were obtained with and without the albedo feedback due to climate-induced poleward expansion of the boreal forest. The climate model results are summarized in the form of potential vegetation maps obtained with the global BIOME model, which facilitates visual comparisons both among models and with pollen and plant macrofossil data recording shifts of the forest-tundra boundary. A preliminary synthesis shows that the forest limit was shifted 100–200 km north in most sectors. Both CCMl and GENESIS 2 produced a shift of this magnitude. GENESIS 1 however produced too small a shift, except when the boreal forest albedo feedback was included. The feedback in this case was estimated to have amplified forest expansion by approximately 50%. The forest limit changes also show meridional patterns (greatest expansion in central Siberia and little or none in Alaska and Labrador) which have yet to be reproduced by models. Further progress in understanding of the processes involved in the response of climate and vegetation to orbital forcing will require both the deployment of coupled atmosphere-biosphere-ocean models and the development of more comprehensive observational data sets.

Global Biogeochemical Cycles↗

USGS2025

Heterogeneity of locked‐pasture snow conditions modulate habitat and movement choices of a facultative migrant

Habitat selection and movement are key mechanisms by which animals can respond to and potentially cope with highly variable environmental conditions. Optimal responses likely vary, however, depending on the severity and scope of conditions. We tested this hypothesis using a facultative migrant species, the Great Gray Owl ( Strix nebulosa ), which exhibits high inter- and intra-individual variation in the timing, direction, and distance of winter movements. Specifically, we evaluated whether episodic, spatiotemporally variable “locked-pasture” snow conditions, which restrict access to subnivean food, prompted shifts in habitat selection or long-distance movements by owls. We quantified the movement of 42 owls using global positioning system (GPS) data within the Greater Yellowstone Ecosystem, USA, during 2017–2022. We used a novel ecological application of SnowModel, a snow evolution modeling system, to estimate fine-scale, physical snow properties likely to influence access to prey. Variables included snow depth, snow crusts produced by wind, and ice crusts produced by melt-freeze and rain-on-snow events. Owls avoided heterogeneously distributed wind crusts via local shifts in habitat selection. More homogenous ice crusts elicited long-distance movements away from affected home ranges. Finally, owls employed both proximate shifts in habitat selection and long-distance movements to avoid deeper snow. Ultimately, owls exhibited behavioral flexibility in response to limiting snow conditions that can vary in terms of severity, spatial extent, and duration. Such behavioral responses determine species distribution, with implications for population and community dynamics in spatiotemporally variable systems. Understanding the effects of, and responses to, environmental controls is increasingly important given the scope of on-going global change.

Idaho, Wyoming↗

USGS2005

Effective discharge analysis of ecological processes in streams

Discharge is a master variable that controls many processes in stream ecosystems. However, there is uncertainty of which discharges are most important for driving particular ecological processes and thus how flow regime may influence entire stream ecosystems. Here the analytical method of effective discharge from fluvial geomorphology is used to analyze the interaction between frequency and magnitude of discharge events that drive organic matter transport, algal growth, nutrient retention, macroinvertebrate disturbance, and habitat availability. We quantify the ecological effective discharge using a synthesis of previously published studies and modeling from a range of study sites. An analytical expression is then developed for a particular case of ecological effective discharge and is used to explore how effective discharge varies within variable hydrologic regimes. Our results suggest that a range of discharges is important for different ecological processes in an individual stream. Discharges are not equally important; instead, effective discharge values exist that correspond to near modal flows and moderate floods for the variable sets examined. We suggest four types of ecological response to discharge variability: discharge as a transport mechanism, regulator of habitat, process modulator, and disturbance. Effective discharge analysis will perform well when there is a unique, essentially instantaneous relationship between discharge and an ecological process and poorly when effects of discharge are delayed or confounded by legacy effects. Despite some limitations the conceptual and analytical utility of the effective discharge analysis allows exploring general questions about how hydrologic variability influences various ecological processes in streams.

Water Resources Research↗

USGS2023

Towards a better future for biodiversity and people: Modelling Nature Futures

The Nature Futures Framework (NFF) is a heuristic tool for co-creating positive futures for nature and people. It seeks to open up a diversity of futures through mainly three value perspectives on nature – Nature for Nature, Nature for Society, and Nature as Culture. This paper describes how the NFF can be applied in modelling to support decision-making. First, we describe key considerations for the NFF in developing qualitative and quantitative scenarios: i) multiple value perspectives on nature as a state space where pathways improving nature toward a frontier can be represented, ii) mutually reinforcing key feedbacks of social-ecological systems that are important for nature conservation and human wellbeing, iii) indicators of multiple knowledge systems describing the evolution of complex social-ecological dynamics. We then present three approaches to modelling Nature Futures scenarios in the review, screening, and design phases of policy processes. This paper seeks to facilitate the integration of relational values of nature in models and strengthen modelled linkages across biodiversity, nature’s contributions to people, and quality of life.

Global Environmental Change↗

USGS2024

A hierarchical model for eDNA fate and transport dynamics accommodating low concentration samples

Environmental DNA (eDNA) sampling is an increasingly important tool for answering ecological questions and informing aquatic species management; however, several factors currently limit the reliability of ecological inference from eDNA sampling. Two particular challenges are (1) determining species source location(s) and (2) accurately and precisely measuring low concentration eDNA samples in the presence of multiple sources of ecological and measurement variability. The recently introduced eDNA Integrating Transport and Hydrology (eDITH) model provides a framework for relating eDNA measurements to source locations in riverine networks, but little empirical work has been done to test and refine model assumptions or accommodate low concentration samples, that can be systematically undermeasured. To better understand eDNA fate and transport dynamics and our ability to reliably quantify low concentration samples, we developed a hierarchical model and used it to evaluate a fate and transport experiment. Our model addresses several low concentration challenges by modeling the number of copies in each PCR replicate as a latent variable with a count distribution and conditioning detection and quantification on replicate copy number. We provide evidence that the eDNA removal rate declined through time, estimating that over 80% of eDNA was removed over the first 10 m, traversed in 41 s. After this initial period of rapid decay, eDNA decayed slowly with consistent detection through our farthest site 1 km from the release location, traversed in 67.8 min. Our model further allowed us to detect extra-Poisson variation in the allocation of copies to replicates. We extended our hierarchical model to accommodate a continuous effect of inhibitors and used our model to provide evidence for the inhibitor hypothesis and explore the potential implications. While our model is not a panacea for all challenges faced when quantifying low-concentration eDNA samples, it provides a framework for a more complete accounting of uncertainty.

Environmental and Ecological Statistics↗

USGS2007

Invasive plants and their ecological strategies: Prediction and explanation of woody plant invasion in New England

Effective management of introduced species requires the early identification of species that pose a significant threat of becoming invasive. To better understand the invasive ecology of species in New England, USA, we compiled a character data set with which to compare non-native species that are known invaders to non-native species that are not currently known to be invasive. In contrast to previous biological trait-based models, we employed a Bayesian hierarchical analysis to identify sets of plant traits associated with invasiveness for each of three growth forms (vines, shrubs, and trees). The resulting models identify a suite of 'invasive traits' highlighting the ecology associated with invasiveness for each of three growth forms. The most effective predictors of invasiveness that emerged from our model were 'invasive elsewhere', 'fast growth rate', 'native latitudinal range', and 'growth form'. The contrast among growth forms was pronounced. For example, 'wind dispersal' was positively correlated with invasiveness in trees, but negatively correlated in shrubs and vines. The predictive model was able to correctly classify invasive plants 67% of the time (22/33), and non-invasive plants 95% of the time (204/215). A number of potential future invasive species in New England that deserve management consideration were identified. ?? 2007 The Authors.

Diversity and Distributions↗

USGS2021

Synthesis of data and studies relating to Delta Smelt biology in the San Francisco Estuary, emphasizing water year 2017

In the San Francisco Estuary (SFE), the effects of freshwater flow on the aquatic ecosystem have been studied extensively over the years and remains a contentious management issue. It is especially contentious with regards to the Delta Smelt (Hypomesus transpacificus), a species endemic to the SFE that has been listed as threatened under the Federal Endangered Species Act and endangered by the State of California. Early studies of Delta Smelt distribution within the SFE suggested that Delta Smelt habitat is determined largely by freshwater flow; however, the exact mechanisms and processes producing such benefits remained unclear. In the summer of 2017, the Flow Alteration Management, Analysis, and Synthesis Team (FLOAT-MAST) was established to analyze, synthesize, and summarize the data collected from the various flow-related monitoring and special studies occurring in 2017(see Table Intro 4). This report will focus on the 2017 summer-fall status of Delta Smelt and its habitat following a record wet year. There has been a long-term decline in the abundance of Delta Smelt associated with a decline in other pelagic fishes. Investigators concluded that the decline has likely been caused by the interactive effects of several causes, including changes in both physical and biotic habitats, many of which are tied to amount and timing of freshwater flow. For this report, we formulated a number of basic predictions about the likely effects of high flows in 2017 on Delta Smelt and their habitat (Table 3). We use a qualitative weight of evidence approach to evaluate whether these predictions were supported by available data. Data sources included a variety of long-term monitoring surveys conducted by Interagency Ecological Program (IEP) agencies, as well as model outputs. Delta Smelt population, health, and life history metrics rarely responded as predicted. Water temperature appears to have a stronger effect on Delta Smelt growth rate and some metrics of life history diversity than outflow or X2 position. Other life history diversity attributes varied but did not appear to be driven by outflow or temperature. Health status was difficult to interpret. Low prevalence of lesions and improved nutritional condition during the drought was contradicted by declining overall population levels. Because of the sparse catches of Delta Smelt in the post-POD years, we consider the data insufficient to reach firm conclusions about the predictions concerning range and distribution of Delta Smelt, especially in the fall. The prediction of high survival was not supported. The 2017 Delta Smelt year class began with poor recruitment in spring of 2017 and below average survival for spring to summer and summer to fall. Thus, low production and low survival led to low abundance of all life stages. During the fall to winter period survival improved, yet the resulting adults were low in number. Foraging success of the fish captured, as measured by stomach fullness, was high for juveniles and adults in 2017 relative to recent years associated with the higher densities of common zooplankton prey that occurred in 2017.

California↗

USGS2017

Linking spring phenology with mechanistic models of host movement to predict disease transmission risk

Disease models typically focus on temporal dynamics of infection, while often neglecting environmental processes that determine host movement. In many systems, however, temporal disease dynamics may be slow compared to the scale at which environmental conditions alter host space-use and accelerate disease transmission. Using a mechanistic movement modelling approach, we made space-use predictions of a mobile host (elk [ Cervus Canadensis ] carrying the bacterial disease brucellosis) under environmental conditions that change daily and annually (e.g., plant phenology, snow depth), and we used these predictions to infer how spring phenology influences the risk of brucellosis transmission from elk (through aborted foetuses) to livestock in the Greater Yellowstone Ecosystem. Using data from 288 female elk monitored with GPS collars, we fit step selection functions (SSFs) during the spring abortion season and then implemented a master equation approach to translate SSFs into predictions of daily elk distribution for five plausible winter weather scenarios (from a heavy snow, to an extreme winter drought year). We predicted abortion events by combining elk distributions with empirical estimates of daily abortion rates, spatially varying elk seroprevelance and elk population counts. Our results reveal strong spatial variation in disease transmission risk at daily and annual scales that is strongly governed by variation in host movement in response to spring phenology. For example, in comparison with an average snow year, years with early snowmelt are predicted to have 64% of the abortions occurring on feedgrounds shift to occurring on mainly public lands, and to a lesser extent on private lands. Synthesis and applications . Linking mechanistic models of host movement with disease dynamics leads to a novel bridge between movement and disease ecology. Our analysis framework offers new avenues for predicting disease spread, while providing managers tools to proactively mitigate risks posed by mobile disease hosts. More broadly, we demonstrate how mechanistic movement models can provide predictions of ecological conditions that are consistent with climate change but may be more extreme than has been observed historically.

Journal of Applied Ecology↗