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Wildlife and natural resource institutions play key roles in invasive species monitoring and management. Paradoxically, the extensive fieldwork undertaken by these institutions and their partners may result in the inadvertent movement and spread of invasive species within and between sensitive ecosystems. In this work, we consider the potential effects of internal guidelines and policies designed to prevent the spread of invasive species by the field activities of management institutions and their partners. Such policies could be high-leverage tools for invasive species prevention, however, as large, complex organizations seek to implement policies to limit the spread of invasive species, it may be challenging to accommodate the wide diversity of potential invasion pathways and mitigation efforts facilitated via the programs, activities, and ecosystems they manage. Prevention policies may also be met with resistance due to the costs of implementation unless concrete benefits can be demonstrated. Assessing and communicating the effects of prevention policies could motivate improved implementation and adherence by institutional units and partners and could help inform adaptive policy changes. However, assessing the effectiveness of invasive species prevention presents a unique set of challenges, including incomplete data on invasive species distribution and pathways, that make it difficult to measure the effects of prevention efforts. In this work, we present a conceptual framework for evaluating institutional policies for invasive species prevention. We describe a flexible, multifaceted approach that considers policy implementation and adherence as well as ecological outcomes. We discuss potential application of this framework using a policy recently implemented by the Pacific Region of the U.S. Fish and Wildlife Service to prevent the introduction and spread of invasive species by service personnel and partners during field activities as a case study. [ABSTRACT FROM AUTHOR]

Occupancy models are often used to analyze long‐term monitoring data to better understand how and why species redistribute across dynamic landscapes while accounting for incomplete capture. However, this approach requires replicate detection/non‐detection data at a sample unit and many long‐term monitoring programs lack temporal replicate surveys. In such cases, it has been suggested that surveying subunits within a larger sample unit may be an efficient substitution (i.e., space‐for‐time substitution). Still, the efficacy of fitting occupancy models using a space‐for‐time substitution has not been fully explored and is likely context dependent. Herein, we fit occupancy models to Delta Smelt (Hypomesus transpacificus) and Longfin Smelt (Spirinchus thaleichthys) catch data collected by two different monitoring programs that use the same sampling gear in the San Francisco Bay‐Delta, USA. We demonstrate how our inferences concerning the distribution of these species changes when using a space‐for‐time substitution. Specifically, we found the probability that a sample unit was occupied was much greater when using a space‐for‐time substitution, presumably due to the change in the spatial scale of our inferences. Furthermore, we observed that as the spatial scale of our inferences increased, our ability to detect environmental effects on system dynamics was obscured, which we suspect is related to the tradeoffs associated with spatial grain and extent. Overall, our findings highlight the importance of considering how the unique characteristics of monitoring programs influences inferences, which has broad implications for how to appropriately leverage existing long‐term monitoring data to understand the distribution of species. [ABSTRACT FROM AUTHOR]

Bull trout (Salvelinus confluentus) are challenging to detect as a result of the species cryptic behavior and coloration, relatively low densities in complex habitats, and affinity for cold, high clarity, low conductivity waters. Bull trout are also closely associated with the stream bed, frequently conceal in substrate, and this concealment behavior is poorly understood. Consequently, population assessments are problematic and biologists and managers often lack quantitative information to accurately describe bull trout distributions, estimate abundance, and assess status and trends; particularly for stream-dwelling populations. During controlled laboratory trials, we recorded concealment, resting, and swimming behavior of juvenile wild bull trout in response to: (1) constant and fluctuating water temperature, (2) presence or absence of light, and (3) substrate size. Light level had the strongest influence on wild fish concealment and more fish concealed as light levels increased from darkness to daylight. Wild fish were 14.5 times less likely to conceal in constant darkness and 4.1 times more likely to conceal in 12 h light x 12 h darkness compared to constant light. Wild fish were 6.2 times less likely to conceal in small (26–51 mm) substrate compared to larger (52–102 mm) substrate. As water temperature increased, fewer wild fish concealed. Knowledge of wild bull trout concealment will improve field sampling protocols and increase detection efficiencies. These data also enhance knowledge of bull trout niche requirements which illuminates ecological differences among species and informs conservation and restoration efforts. [ABSTRACT FROM AUTHOR]

Stream ecosystems provide multiple, valued services to society, including water supply, waste assimilation, recreation, and habitat for diverse and productive biological communities. Managers striving to sustain these services in the face of changing climate, land uses, and water demands need tools to assess the potential effectiveness of alternative management actions, and often, the resulting tradeoffs between competing objectives. Integrating predictive modeling with monitoring data in an adaptive management framework provides a process by which managers can reduce model uncertainties and thus improve the scientific bases for subsequent decisions. We demonstrate an integration of monitoring data with a dynamic, metapopulation model developed to assess effects of streamflow alteration on fish occupancy in a southeastern US stream system. Although not extensive (collected over three years at nine sites), the monitoring data allowed us to assess and update support for alternative population dynamic models using model probabilities and Bayes rule. We then use the updated model weights to estimate the effects of water withdrawal on stream fish communities and demonstrate how feedback in the form of monitoring data can be used to improve water resource decision making. We conclude that investment in more strategic monitoring, guided by a priori model predictions under alternative hypotheses and an adaptive sampling design, could substantially improve the information available to guide decision-making and management for ecosystem services from lotic systems. [ABSTRACT FROM AUTHOR]

Most pathogen detection tests are imperfect, with a sensitivity < 100%, thereby resulting in the potential for a false negative, where a pathogen is present but not detected. False negatives in a sample inflate the number of non-detections, negatively biasing estimates of pathogen prevalence. Histological examination of tissues as a diagnostic test can be advantageous as multiple pathogens can be examined and providing important information on associated pathological changes to the host. However, it is usually less sensitive than molecular or microbiological tests for specific pathogens. Our study objectives were to 1) develop a hierarchical occupancy model to examine pathogen prevalence in spring Chinook salmon Oncorhynchus tshawytscha and their distribution among host tissues 2) use the model to estimate pathogen-specific test sensitivities and infection rates, and 3) illustrate the effect of using replicate within host sampling on sample sizes required to detect a pathogen. We examined histological sections of replicate tissue samples from spring Chinook salmon O. tshawytscha collected after spawning for common pathogens seen in this population: Apophallus/echinostome metacercariae, Parvicapsula minibicornis, Nanophyetus salmincola/ metacercariae, and Renibacterium salmoninarum. A hierarchical occupancy model was developed to estimate pathogen and tissue-specific test sensitivities and unbiased estimation of host- and organ-level infection rates. Model estimated sensitivities and host- and organ-level infections rates varied among pathogens and model estimated infection rate was higher than prevalence unadjusted for test sensitivity, confirming that prevalence unadjusted for test sensitivity was negatively biased. The modeling approach provided an analytical approach for using hierarchically structured pathogen detection data from lower sensitivity diagnostic tests, such as histology, to obtain unbiased pathogen prevalence estimates with associated uncertainties. Accounting for test sensitivity using within host replicate samples also required fewer individual fish to be sampled. This approach is useful for evaluating pathogen or microbe community dynamics when test sensitivity is <100%. [ABSTRACT FROM AUTHOR]

The life history of Cambarus halli, a crayfish endemic to the Tallapoosa River Basin, GA, was studied at four sites within the Tallapoosa River. Two sites had allopatric populations of C. halli, and two sites had populations of C. halli sympatric with C. englishi. Three age classes existed across sites. For Cambarus halli, total number of pleopodal eggs was positively related to carapace length, but egg size was only weakly positively related to carapace length. Cambarus halli were smaller across age classes at sympatric sites, hut had greater growth rates than at allopatric sites. Cambarus halli density estimates were lower at sympatric sites, while proportions of reproductively active age-1 and age-2 individuals were higher at allopatric sites (63% vs. 33%). [ABSTRACT FROM AUTHOR]

The article focuses on a study about the sources of variation in detection among wading birds in the Florida Everglades. The study used dual-observer trials for fixed wing aerial surveys of the birds. It explains that the probability of detection depended on the group size and species of the birds, as well as the observer's position in aircraft. It concludes that detection bias should be considered a major factor in the validity of data, and suggests that observers should use dual-observer method for less bias and unexpected factors.

To predict the distributions of breeding birds in the state of Georgia, USA, we built hierarchical models consisting of 4 levels of nested mapping units of decreasing area: 90,000 ha, 3,600 ha, 144 ha, and 5.76 ha. We used the Partners in Flight database of point counts to generate presence and absence data at locations across the state of Georgia for 9 avian species: Acadian flycatcher (Empidonax virescens), brown-headed nuthatch (Sitta pusilla), Carolina wren (Thryothorus ludovicianus), indigo bunting (Passerina cyanea), northern cardinal (Cardinalis cardinalis), prairie warbler (Dendroica discolor), yellow-billed cuckoo (Coccyzus americanus), white-eyed vireo (Vireo griseus), and wood thrush (Hylocichla mustelina). At each location, we estimated hierarchical-level-specific habitat measurements using the Georgia GAP Analysis18 class land cover and other Geographic Information System sources. We created candidate, species-specific occupancy models based on previously reported relationships, and fit these using Markov chain Monte Carlo procedures implemented in OpenBugs. We then created a confidence model set for each species based on Akaike's Information Criterion. We found hierarchical habitat relationships for all species. Three-fold cross-validation estimates of model accuracy indicated an average overall correct classification rate of 60.5%. Comparisons with existing Georgia GAP Analysis models indicated that our models were more accurate overall. Our results provide guidance to wildlife scientists and managers seeking predict avian occurrence as a function of local and landscape-level habitat attributes. [ABSTRACT FROM AUTHOR]

Pteronotropis welaka (Bluenose Shiner) has a fragmented range throughout the Southeast, but its apparent rarity may reflect a low probability of detection during surveys. Our objectives were to obtain up-to-date status information for populations in southwest Georgia and to account for incomplete detection in our estimate of the proportion of sites occupied. We detected Bluenose Shiner at 5 of 39 sites (13%) sampled during 2004 and 2005 and estimated detection probability (p) and the proportion of sites occupied (psi) from seine-haul data. Models containing habitat covariates as predictors of p and psi provided a better description of the data than models without covariates for Bluenose Shiner and three other minnow species. Regardless of the model structure, the probability of detecting Bluenose Shiner during a single seine haul was substantially lower than for the other minnow species (3-8% vs. 13-33%). However, estimates of the proportion of sites occupied (corrected for incomplete detection) were similar to observed occupancy rates for all four species because of the large number of seine hauls we made at each site. The modeling approach we followed increased our confidence in survey results and provided information on where and how much to sample in future surveys. It has broad application to future surveys and monitoring programs for rare aquatic species in the southeastern United States. [ABSTRACT FROM AUTHOR]

Effective conservation efforts for at-risk species require knowledge of the locations of existing populations. Species presence can be estimated directly by conducting field-sampling surveys or alternatively by developing predictive models. Direct surveys can be expensive and inefficient, particularly for rare and difficult-to-sample species, and models of species presence may produce biased predictions. We present a Bayesian approach that combines sampling and model-based inferences for estimating species presence. The accuracy and cost-effectiveness of this approach were compared to those of sampling surveys and predictive models for estimating the presence of the threatened bull trout (Salvelinus confluentus) via simulation with existing models and empirical sampling data. Simulations indicated that a sampling-only approach would be the most effective and would result in the lowest presence and absence misclassification error rates for three thresholds of detection probability. When sampling effort was considered, however, the combined approach resulted in the lowest error rates per unit of sampling effort. Hence, lower probability-of-detection thresholds can be specified with the combined approach, resulting in lower misclassification error rates and improved cost-effectiveness. [ABSTRACT FROM AUTHOR]

Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic assumptions are violated. Moreover, diagnostics to identify when bias in parameter estimates from N-mixture models is likely is largely unexplored. We simulated count data sets using 837 combinations of detection probability, number of sample units, number of survey occasions, and type and extent of heterogeneity in abundance or detectability. We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates. We also explored if assumption violations can be diagnosed prior to fitting N-mixture models. In doing so, we propose a new model diagnostic, which we term the quasi-coefficient of variation (QCV). N-mixture models performed well when assumptions were met and detection probabilities were moderate (i.e., ≥0.3), and the performance of the estimator improved with increasing survey occasions and sample units. However, the magnitude of bias in estimated mean abundance with even slight amounts of unmodeled heterogeneity was substantial. The parametric bootstrap GOF test did not perform well as a diagnostic for bias in parameter estimates when detectability and sample sizes were low. The results indicate the QCV is useful to diagnose potential bias and that potential bias associated with unidirectional trends in abundance or detectability can be diagnosed using Poisson regression. This study represents the most thorough assessment to date of assumption violations and diagnostics when fitting N-mixture models using the most commonly implemented error distribution. Unbiased estimates of population state variables are needed to properly inform management decision making. Therefore, we also discuss alternative approaches to yield unbiased estimates of population state variables using similar data types, and we stress that there is no substitute for an effective sample design that is grounded upon well-defined management objectives. [ABSTRACT FROM AUTHOR]

• Multistate occupancy 'abundance' models can differentiate sites with relatively high and low abundance. • For detecting population trends, the same sites should be surveyed from year to year (fixed-sites). • Multistate occupancy 'abundance' models reliably detect population declines of at least 45% if a fixed-site design is used. • Multistate occupancy 'abundance' models offer advantages compared to assumption sensitive N-mixture models for modeling abundance. • Multistate occupancy 'abundance' models refine the basic occupancy model to improve abundance estimates. Detecting spatiotemporal changes in the abundances of organisms is key to effectively conserving species. While indices of abundance have long been used, there has been a shift toward model-based estimators that account for the detection process. Popular approaches including traditional occupancy models and N-mixture models entail tradeoffs. The traditional occupancy approach requires the researcher coarsen the characterization of abundance to the probability that a site is occupied or unoccupied. Conversely, N-mixture models make use of variation in counts, but perform poorly when individuals have low detectability or move into or out of sites between visits. Multistate occupancy models that differentiate relatively abundant from non-abundant states have the potential to fill this gap but have been underexplored. We conducted a simulation study to test whether multistate occupancy models could capture spatial abundance patterns and detect population declines in the face of low individual detection probability (p ≤ 0.3) and unmodeled heterogeneity (e.g., that arising from individual movement). We considered 10,773 scenarios to examine the effects of differing amounts of heterogeneity as well as alternative study designs, population parameters, and modeling choices. We tracked bias in the proportion of sites estimated to be in the abundant state for single-season models, and power to detect a declining trend across multiple years. We also evaluated data diagnostic metrics to provide guidance to users. Multistate occupancy models were able to differentiate sites with higher abundances from sites with lower abundances when there were at least medium levels of spatial heterogeneity in true abundances. If different sites were randomly selected each year, power to detect even large population declines (65%) was poor (power < 0.8). However, if the same sites were surveyed each year, and a dynamic multistate occupancy was used, multistate occupancy models could detect (power ≥ 0.8) relatively small declines (5-40%) in 20% of scenarios, and frequently detect large declines of 45-60% (mean power = 0.92). Conservation decisions rely on detecting change reliably, rarely needing absolute abundance information. Multistate occupancy models can improve our ability to detect changing abundance while accommodating low individual detection probability and heterogeneity in count monitoring data. [ABSTRACT FROM AUTHOR]

Pacific salmon experience prolonged elevation in corticosteroid hormones during important life history events including migration, reproduction, and senescence. These periods of elevated corticosteroids correspond with changes to immunity and energy metabolism; therefore, fish may be particularly vulnerable to mortality at these times. Recent studies found that stress-induced cortisol release associated with microbial community shifts in salmonids, raising the question of how longer-term corticosteroid dynamics that accompany life history transitions affect salmonid microbiomes. In this work, we experimentally evaluated the relationships between gut microbiome composition, chronically elevated corticosteroids, and mortality in juvenile Chinook salmon (Oncorhynchus tshawytscha). We found that treatment with slow-release implants of the corticosteroids cortisol or dexamethasone resulted in changes to the gut microbiome. Morbidity was also associated with microbiome composition, suggesting that the gut microbiome reflects individual differences in susceptibility to opportunistic pathogens. Additionally, we analyzed a small number of samples from adult fish at various stages of senescence. Results from these samples suggest that microbiome composition associated with gut integrity, and that the microbial communities of corticosteroid treated juveniles shift in composition toward those of senescent adults. Overall, findings from this work indicate that the gut microbiome correlates with mortality risk during periods of chronic corticosteroid elevation. [ABSTRACT FROM AUTHOR]

Human 4-hydroxyphenylpyruvate dioxygenase-like (HPDL) is a putative iron-containing non-heme oxygenase of unknown specificity and biological significance. We report 25 families containing 34 individuals with neurological disease associated with biallelic HPDL variants. Phenotypes ranged from juvenile-onset pure hereditary spastic paraplegia to infantile-onset spasticity and global developmental delays, sometimes complicated by episodes of neurological and respiratory decompensation. Variants included bona fide pathogenic truncating changes, although most were missense substitutions. Functionality of variants could not be determined directly as the enzymatic specificity of HPDL is unknown; however, when HPDL missense substitutions were introduced into 4-hydroxyphenylpyruvate dioxygenase (HPPD, an HPDL orthologue), they impaired the ability of HPPD to convert 4-hydroxyphenylpyruvate into homogentisate. Moreover, three additional sets of experiments provided evidence for a role of HPDL in the nervous system and further supported its link to neurological disease: (i) HPDL was expressed in the nervous system and expression increased during neural differentiation; (ii) knockdown of zebrafish hpdl led to abnormal motor behaviour, replicating aspects of the human disease; and (iii) HPDL localized to mitochondria, consistent with mitochondrial disease that is often associated with neurological manifestations. Our findings suggest that biallelic HPDL variants cause a syndrome varying from juvenile-onset pure hereditary spastic paraplegia to infantile-onset spastic tetraplegia associated with global developmental delays. [ABSTRACT FROM AUTHOR]

• Model for integrating pinniped survey data from aerial counts with sightings-at-sea • Allows for the estimation of abundance, distribution, attendance at sites • Applied to Steller sea lions in Glacier Bay, Alaska • Estimates were biologically reasonable and comparable to previous studies • The modelling framework is generalizable to other species and systems Pinnipeds are commonly monitored using aerial photographic surveys at land- or ice-based sites, where animals come ashore for resting, pupping, molting, and to avoid predators. Although these counts form the basis for monitoring population change over time, they do not provide information regarding where animals occur in the water, which is often of management and conservation interest. In this study, we developed a hierarchical model that links counts of pinnipeds at terrestrial sites to sightings-at-sea and estimates abundance, spatial distribution, and the proportion of time spent on land (attendance probability). The structure of the model also allows for the inclusion of predictors that may explain variation in ecological and observation processes. We applied the model to Steller sea lions (Eumetopias jubatus) in Glacier Bay, Alaska using counts of sea lions from aerial photographic surveys and opportunistic in-water sightings from vessel surveys. Glacier Bay provided an ideal test and application of the model because data are available on attendance probability based on long-term monitoring. We found that occurrence in the water was positively related to proximity to terrestrial sites, as would be expected for a species that engages in central-place foraging. The proportion of sea lions in attendance at terrestrial sites and overall abundance estimates were consistent with reports from the literature and monitoring programs. The model we describe has benefit and utility for park managers who wish to better understand the overlap between pinnipeds and visitors, and the framework that we present has potential for application across a variety of study systems and taxa. [ABSTRACT FROM AUTHOR]

• We illustrate how estimates from disjunct monitoring data can be used to inform adaptive water management. • Larger may flow pulses than are currently being allocated are projected to be optimal for increasing Chinook salmon smolt survival through the south delta. • Water temperature and smolt outmigration timing drive optimal water distribution but sensitivity analysis indicates water allocation changes little with temperature uncertainty indicating future monitoring should focus on outmigration timing. • We suggest using current data to start informing decisions for complex problems despite far from perfect knowledge about system responses. Monitoring is usually among the first actions taken to help inform recovery planning for declining species, but these data are rarely used formally to inform conservation decision making. For example, Central Valley Chinook salmon were once abundant, but anthropogenic activities have led to widespread habitat loss and degradation resulting in significant population declines. Monitoring data suggest survival through the southern Sacramento-San Joaquin River Delta, in particular, may be a limiting factor for juvenile Chinook salmon outmigrating from the San Joaquin River and its tributaries. However, survival and routing monitoring data have not been formally used to inform water management in a decision analytic framework. Here, we illustrate how estimates derived from disjunct monitoring data can be used to inform water management and as a basis for adaptively managing flows. We aggregated a meta-analysis of Chinook salmon smolt survival and routing estimates through the south Delta with other sources of data to develop a survival and routing simulation model to estimate optimal flows for the San Joaquin River during smolt outmigration from February–May. We found that large flow pulses at predictable times during the spring are projected to be optimal for increasing Chinook salmon smolt survival to the San Francisco Bay and that optimal scenarios differed somewhat with water year type. Sensitivity analysis revealed temperature and smolt outmigration timing are driving optimal pulse distribution and that water allocation changes little with parameter uncertainty. This case study highlights the utility of the decision-analytic framework for solving conservation problems. [ABSTRACT FROM AUTHOR]

We used strong inference with Akaike's Information Criterion (AIC) to assess the processes capable of explaining long-term (1984-1995) variation in the per capita rate of change of mottled sculpin (Cottus bairdi) populations in the Coweeta Creek drainage (USA). We sampled two fourth- and one fifth-order sites (BCA [uppermost], BCB, and CC [lowermost]) along a downstream gradient, and the study encompassed extensive flow variation. Physical habitat availability varied significantly both within and among the sites. Sculpin densities in all sites were highly stable (coefficients of variation = 0.23-0.41) and sampling variability was low (coefficients of variation = 0.11-0.15). Population stability was positively associated with habitat stability, and the only significant correlations of population parameters among sites involved juveniles. Sculpin densities were significantly higher in BCB than in CC. The data suggest that, despite their proximity, the dynamics of populations within the sites are being determined by small-scale (i.e., 30-50 m) rather than broad-scale spatial processes. Both AIC and Dennis and Taper analyses indicated that simple density dependence had the greatest ability to explain variation in r for all life-history classes in all sites (AIC, seven of nine cases; Dennis and Taper, nine of nine cases). Multiprocess models had little explanatory power. When adults were removed from two sites, juvenile sculpin shifted into microhabitats formerly occupied by adults. No shifts occurred in control sites. Consequently, it is likely that the patterns of density dependence observed in all three sites were a consequence of intraspecific competition for space. Our findings argue for a multitiered approach to the study of population variation, one that encompasses long-term monitoring, spatial variation, and experimental testing of potential mechanisms. [ABSTRACT FROM AUTHOR]