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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]

• 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]

• 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]