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On July 29th, 2020 federal agents of the Department of Homeland Security deployed unknown chemical agents via a thermal fogger on racial justice protesters outside the Hatfield Federal Courthouse in Portland, Oregon. In awakening a dormant Foucault’s Boomerang, DHS instantly made a large swath of the populace aware of an insidious weapon that was birthed in the American occupation of Vietnam and perfected for use against domestic protesters in the 1960s and '70s. Despite repeated use of thermal foggers to deploy chemical weapons over the last half century, the device appears to have slipped from the zeitgeist, only to reemerge in the city which experienced the most visible novel federal deployment of chemical weapons, the most chemical weapons-based incidents of police brutality at racial justice protests across American cities (regardless of population size), and a notable density of photographers and videographers (both professional and amateur). Not all of the weapon's history is documented, but enough is that we can dispel the myth that this deployment was new in any notable sense other than being recent., Files provided here are [1] the pdf version of the booklet and a [2] zipped file containing the markdown and image components of the booklet for rendering [a] the pdf and [b] the html subpage on the general chemical weapons research consortium website.

This document outlines the analysis and results of the multi-year Crescent Dunes Mortality Estimation. The methods deviate significantly from those of Simonis (2017, 2018, 2019, 2020a, 2020b), by using an updated estimator (GenEst; Dalthorp et al. 2018, Simonis et al. 2018), encompassing all carcass data across years, and include trial data from all years (Simonis 2020b). This generalization builds on previous models; provides more robust estimates for efficiency, persistence, and mortality; and facilitates comparisons of estimates with those generated from other methods or sites (Bernardino et al. 2013, Stevens and Dennis 2013, Dalthorp et al. 2018).

Law enforcement’s use of chemical weapons is a threat to human and environmental health, exemplified during 2020 Black Lives Matter (BLM) protests in Portland, OR, where city, county, state, and federal agencies have deployed various chemicals for over 100 days. In the second half of July, Department of Homeland Security (DHS) agents used an exceptionally toxic and unknown weapon to quell free speech in support of Black lives and against federal presence. With significant support from the community, I combined first-hand accounts, media reports, videos and photos of munitions, primary literature and analytical chemistry to identify the weapon as gaseous Zinc Chloride (ZnCl2) from so-called Hexachloroethane (HC) “smoke” grenades. I then used hierarchical Bayesian methods to estimate that DHS deployed 24 (23 - 27) HC grenades in July. The gas released is so toxic that the grenades deployed produced enough ZnCl2 to kill the author (~100 kg) 137 (131 - 154) times over and its release has led to persistent major health issues in the exposed population. Given prior case histories of ZnCl2 exposure and novel symptoms associated with HC grenade use by DHS, ZnCl2 is certainly the causal agent and has created an ongoing human and environmental health crisis extending well beyond the protests’ footprint. DHS’s wanton use of ZnCl2 against protesters will have lasting impacts for decades and was identified through a community of civilians standing up to say Black Lives Matter.

Law enforcement’s use of chemical weapons is a threat to human and environmental health, exemplified during 2020 racial justice protests in Portland, Oregon, USA. In July, US Department of Homeland Security (DHS) agents used an exceptionally toxic and unknown weapon to quell free speech in support of Black lives and against federal presence. With significant help from the community, I combined first-hand accounts, videos and photos of munitions, primary literature, and analytical chemistry to identify the weapon as gaseous ZnCl2 from Hexachloroethane (HC) “smoke” grenades. Using hierarchical Bayesian methods, I estimated that DHS deployed 26 (25 – 30; 95% CI) HC grenades. Given the toxicity of ZnCl2, that many canister could have killed hundreds of people. Although no fatalities were reported, the exposed population experienced acute, delayed, and persistent health issues. DHS’s wanton use of ZnCl2 will have lasting impacts and was identified through a community standing up for racial justice . Significance Statement The US Army Chemical Warfare Service invented Hexachloroethane (HC) smoke screens after World War I, but by the mid-1950s armed services around the world were well-aware of HC’s acute and chronic toxicity. The “smoke” produced by HC devices is gaseous Zinc Chloride (ZnCl2), a known lethal compound that induces heavy metal poisoning (“metal fume fever”); chemically burns dermal, bronchial, and gastrointestinal epithelia; and bioaccumulates. Despite well-established impacts, US Department of Homeland Security (DHS) agents deployed HC in Portland Oregon to quell Black Lives Matter protests in July 2020. I use a hierarchical Bayesian model that combined multiple observation streams to compile community-collected video and photographic evidence and estimate that DHS deployed enough grenades to kill or maim hundreds of people.

Law enforcement’s use of chemical weapons is a threat to human and environmental health, exemplified during 2020 Black Lives Matter (BLM) protests in Portland, OR, where city, county, state, and federal agencies have deployed various chemicals for over 100 days. In July, US Department of Homeland Security (DHS) agents used an exceptionally toxic and unknown weapon to quell free speech in support of Black lives and against federal presence. With significant support from the community, I combined first-hand accounts, print media, videos and photos of munitions, primary literature, and analytical chemistry to identify the weapon as gaseous Zinc Chloride (ZnCl2) from Hexachloroethane (HC) “smoke” grenades. I used hierarchical Bayesian methods to estimate that DHS deployed 26 (25 – 30; 95% CI) HC grenades in July, which, given the toxicity of ZnCl2, was enough to kill 235 (156 – 306) people. Although no fatalities have been associated with this ZnCl2 release thus far, it has led to acute, delayed, and persistent major health issues in the exposed population, including bystanders. Further, symptoms overlap with influenza/COVID-19 and Zn is not regularly assayed in patients, hampering medical diagnoses. Given alignment of novel mass symptoms with prior case histories, however, ZnCl2 is certainly the causal agent and has created an ongoing human and environmental health crisis extending well beyond the protests’ footprint in downtown Portland. DHS’s wanton use of ZnCl2 against protesters will have lasting impacts for decades and was identified through a community of civilians standing up to say Black Lives Matter. Significance Statement The US Army Chemical Warfare Service invented Hexachloroethane (HC) smoke screens after World War I, but by the mid-1950s armed services around the world were well-aware of HC’s acute and chronic toxicity. The “smoke” produced by HC devices is gaseous Zinc Chloride (ZnCl2), a known lethal compound that induces heavy metal poisoning (“metal fume fever”); chemically burns dermal, bronchial, and gastrointestinal epithelia; and bioaccumulates. Despite well-established impacts, US Department of Homeland Security (DHS) agents deployed HC in Portland Oregon to quell Black Lives Matter protests in July 2020. I use a hierarchical Bayesian model that combined multiple observation streams to compile community-collected video and photographic evidence and estimate that DHS deployed enough grenades to kill or maim hundreds of people.

We, the undersigned memebers of the scientific community, request that you leverage any and all possible avenues to ***halt current and future use of chemical weapons*** against all civilians.

This report integrates the full set of stream surveys and data analysis by Cramer Fish Sciences (CFS) to describe how the production potential for Chinook, Steelhead and Rainbow trout will be affected by flows within the South Fork Battle Creek bypass reach of the Lassen Lodge Hydro Power Project (LLHP). The surveyed reach (bypass reach) extends from Angel Falls (RM 22.2) downstream to RM 20.6, where water flowing through the powerhouse will rejoin flow in the stream channel through the tailrace return; the project will have no effect on stream flows below that point.&nbsp

Efforts to understand activity patterns of bees, our most important pollinators, often rely on opportunistically collected museum records to model temporal shifts or declines. This type of data, however, may not be suitable for this purpose given high spatiotemporal variability of native bee activity. By comparing phenological metrics calculated from intensive systematic inventory data with those from opportunistic museum records for bee species spanning a range of functional traits, we explored biases and limitations of data types to determine best practices for bee monitoring and assessment. We compiled half a million records of wild bee occurrence from opportunistic museum collections and six systematic inventory efforts, focusing analyses on 45 well-represented species that spanned five functional traits: sociality, nesting habits, floral specialization, voltinism, and body size. We then used permutation tests to evaluate differences between data types in estimating three phenology metrics: flight duration, number of annual abundance peaks, and date of the highest peak. We used GLMs to test for patterns of data type significance across traits. All 45 species differed significantly in the value of at least one phenology metric depending on the data type used. The date of the highest abundance peak differed for 40 species, flight duration for 34 species, and the number of peaks for 15 species. The number of peaks was more likely to differ between data types for larger bees, and flight duration was more likely to differ for larger bees and specialist bees. Our results reveal a strong influence of data type on phenology metrics that necessitates consideration of data source when evaluating changes in phenological activity, possibly applicable to many taxa. Accurately assessing phenological change may require expanding wild bee monitoring and data sharing.

Chapter Four: Juvenile Steelhead & Chinook Production and Smolt Survival The Umatilla Basin Natural Production Monitoring and Evaluation Project 2019 Annual Progress Report

Chapter Four: Juvenile Steelhead & Chinook Production and Smolt Survival The Umatilla Basin Natural Production Monitoring and Evaluation Project

Review of the quantitative vegetation monitoring data collected at the Snake River RM 439.5 riparian forest restoration project (also known as the Bayha Island Research Project, or “Bayha”), as well as a review of the procedure guiding its collection.

This document outlines the analysis and results of the Pinyon Jay Nest Site Selection Study.

This document provides a comparison of the Hull and Muir (2010) bat fatality distribution with the observed Kawailoa Wind Farm fatality distribution data. The goal was to answer the following questions: [1] What is the probability that the Hull and Muir (2010) model predicts the actual distribution of bat carcasses at Kawailoa? [2] What is the probability that the observed bats accurately represent the carcass distribution at Kawailoa? [3] Given the information available, is it more advisable to use the Hull and Muir (2010) distribution or the empirical distribution?

This document outlines the methods and results associated with the project “Estimating Project-Specific Mortality Estimates from Post-Construction Survey Data and Refinement of Eagle Bayesian Risk Model”.

This document provides a description of the analytical approach used to estimate fatalities at the Gunsight Wind Energy Project, including methods to estimate searcher efficiency, carcass persistence, overall detection probability, and total fatalities.

Estimation of the impacts of removing predators from Clifton Court Forebay

Chapter Four: Juvenile Steelhead & Chinook Production and Smolt Survival The Umitilla Basin Natural Production Monitoring and Evaluation Project 2017 Annual Progress Report

The goals of the current project included the following: Determine spatiotemporal dynamics of water temperature within Dry Creek, California, immediately downstream of Warm Springs Dam (WSD). Contextualize these temperature analyses in terms of coho salmon bioenergetics and potential climate scenarios. Develop recommendations for habitat restoration actions targeting coho salmon, based on these analyses. Our approach involved analyzing data from water temperature loggers deployed in Dry Creek, and subsequently determining drivers of water temperature within this reach. Key results included the observations that water temperature within Dry Creek exhibited the following patterns: Significant increases in water temperature with increasing distance downstream from cold water outflow from WSD. Differences in water temperature among habitat types, including the following: Warmer and less temporally variable water temperatures in disconnected pool habitats Cooler and more temporally variable water temperatures in mainstem habitats Intermediate temperature regimes in backwater and side channel habitats Temperatures within backwater and disconnected pool habitats, but not mainstem or side channel habitats, occasionally exceeded temperature thresholds for rearing juvenile coho These exceedances only occurred >15 km downstream from WSD Additionally, we contextualize the effect size associated with these trends by applying a simplified bioenergetics model, parameterized for coho salmon. This effort revealed that Dry Creek temperatures are currently near or slightly exceeding optimal temperatures for coho salmon physiological processes underlying bioenergetics. Finally, we explored regionally relevant climate scenario projections for the 21st century, in order to appraise future effects of currently considered restoration actions and provide recommendations that ensure alignment of such actions with long-term goals of population recovery.

Previous studies which have showed pre-screen losses (PSL) of federal and State Endangered Species Act (ESA) listed salmonids within the Clifton Court Forebay range from 63% to 99%. Consequently, the National Marine Fisheries Service has required that the Department of Water Resources implement the Reasonable and Prudent Alternative (RPA) action (IV 4.2(2)) of the Biological Opinion (BiOp) and Conference Opinion on the Long-term Operations of the Central Valley Project and State Water Project (NMFS 2009) to reduce predation of ESA protected Salmon and Steelhead within the Clifton Court Forebay. Predation is thought to account for a significant, but unknown percentage of the PSL at the Forebay. Numerous studies have estimated PSL using recaptures of tagged fish released into the Forebay; however, there is no direct evidence of piscivorous species predating upon listed species in the Forebay. This research, part of the Clifton Court Forebay Predator Study, was developed to investigate the impact on predation-related PSL of listed species, and includes the analysis of predatory fish diet. Cramer Fish Sciences (CFS) and Genidaqs (as part of the AECOM Team) have been tasked with identifying fish species in the digestive tracts of Striped Bass (Morone saxatilis) using genetic methods that target species-specific DNA sequences and modeling the daily meal of each Striped Bass age class in order to estimate how much total mass of each target prey species was consumed through time using bioenergetic modeling. In Chapter 1 of this technical memo, we summarize the results from two years of bioenergetics modeling, during which field collection protocols and sample integrity were assessed. In Chapter 2, we quantify uncertainty in the predictions of the bioenergetics model, and thereby provide greater context to the analysis in the first chapter. Bioenergetics models are comprised of a set of physiological equations, each with multiple parameters, combined via a basic mass-balance approach. While all of the underlying parameters and equations have uncertainty, they are treated as fixed values and equations when using the standard Fish Bioenergetics 3.0 software package, and as a result the model produces a fixed-point prediction of daily consumption (Chapter 1). In addition, there is uncertainty and inherent variability in the state variables (predator weights, diet compositions) used to construct the model, yet this variation is also ignored, as single values are used for all state variables. Therefore, the sensitivity analysis (Chapter 2) quantifies the variability present and evaluates how parameter and state variable uncertainties contribute to it. The general approach we take for the sensitivity analysis is one of variance analyses. If we assume that the entire model is devoid of uncertainty, each component has a fixed value and the prediction is a fixed value with no variance. Indeed, this is the assumption of the standard bioenergetics model as used in Chapter 1. When variation is included in a parameter value or state variable, it will propagate through to the prediction, causing it to be variable as well. The process then is to include variation in all model components and quantify the impact of this variance on the uncertainty in the predictions of the model. The results of the feasibility modeling (Chapter 1) suggest that the Striped Bass consume Chinook, Delta Smelt, Rainbow Trout / Steelhead, and Green Sturgeon. For example, in WY2013 Chinook and Delta Smelt median consumption rate varied respectively from 0.015 – 0.116 g/d and 0.008 – 0.075 g/d, depending on age (Chapter 1, Table 1.4, Figure 1). In WY2014 the median Chinook and Delta Smelt consumption rates varied respectively from 0.021 – 0.310 g/d, and 0.010 – 0.492 g/d. Rainbow Trout (Steelhead) consumption varied between WY2013 and WY2014, with median consumption rates of 0.007 – 0.051 g/d and 0.021 – 0.299 g/d, respectively. Our evaluation of the sensitivity of the model (Chapter 2) illustrates that the widespread uncertainty in the model inputs translates to substantial variance in the predicted rates of consumption for all cohorts of predators consuming all prey items (including listed prey). Furthermore, our analysis indicates that treating the inputs as fixed values produces a downward bias in prey-specific consumption rates (under fixed inputs, predicted mean consumption rates are lower than under variable inputs). Thus, the predictions in Chapter 1 should be treated as underestimates. For example, an Age-1 Striped Bass in WY2013 is predicted to have a daily consumption rate of Chinook salmon of 0.015 (0.010-0.051) g/d in the absence of variation, compared to a range of 0.003-0.076 g/d (mean 0.028, 95% CI of 0.008-0.052) when all model inputs were variable. This range increases substantially with predator age, such that the fixed model predicts an Age-7 Striped Bass in WY2013 would consume a total of 80.41 g of Chinook, whereas the variable model predicts 99.79 g Chinook (mean), with a range of 2.44-219.00 g. The combination of bias and false precision in the estimates indicates that input uncertainty is an important factor that needs to be addressed so that consumption estimates can be used robustly to manage predators. Indeed, all sources that we evaluated contributed variance to the predicted values, but not equally. Of the function and parameter inputs, the most variation was introduced by the functions used to estimate initial and final weights (growth), followed by the bioenergetics functions, and the functions used to estimate diet composition (historic and predation genetics data). In general, uncertainty in the energy density functions did not contribute much to the prediction uncertainty. In addition, the variation among individuals in size and date of sampling (due to inherent ecological variation among individuals) contributed substantial variance to predicted consumption values. Taken together, this analysis reveals that while the bioenergetics model provides a useful approach to estimate consumption by Striped Bass individuals, the variance currently present in model inputs will generate large uncertainty in model predictions. The sensitivity analysis was helpful in determining the sources of variance within the model framework. Future experiments that focus on parameter and input variable refinement would aid considerably in reducing model variance and tightening the consumption prediction range. For example, the data that are collected in the field which contribute the most variance should be evaluated for potential enhancements to the modeling design. If such datasets exist already, leveraging them in the future will help reduce the predicted uncertainty. A few improvements that could reduce uncertainty in this framework would be to: 1) collect weights rather than lengths for predators (e.g. don’t assume a weight-length relationship); 2) perform a mark-recapture experiment where individuals are caught, weighed, released with a tag, potentially caught and weighed again multiple times to allow for a more robust estimate of their growth; and 3) conduct additional diet sampling to refine numerical and weight percent composition estimates.

Report detailing updates to the NPFMC coded wire tag database, including QA/QC and filtering.

A Population Viability Analysis (PVA) is a quantitative computer model that can be used to project a population’s long-term demographic and genetic future. In 2013, USFWS and the Red Wolf Species Survival Plan® (SSP) captive breeding program approached experts at the Lincoln Park Zoo to create the Red Wolf PVA team. The goal of this collaboration is to model the viability of the zoo-managed (SSP) and wild, northeastern North Carolina (NENC) red wolf populations, to better understand the conditions under which each population can best persist into the future and how movement of individuals between the populations impacts viability in both. This report summarizes modeling results from a stochastic individual-based model built in Vortex 10.1 for use in USFWS’ Feasibility Review.

Operations of the Central Valley Project (CVP) and the State Water Project (SWP) are hypothesized to have significant impacts on special status species populations. Both projects have salvage facilities that remove fish from diverted water and return them alive to the west delta. However, mortality occurs between the time fish enter the pre-screen space and when they are released in the west delta. This mortality is referred to as “loss”. The National Marine Fisheries Service 2009 Biological Opinion (BiOp) on the long-term operation of the CVP and SWP contains a Reasonable and Prudent Alternative (RPA) that includes actions that modify project operations based on specific levels of loss per-acre-feet diverted. The BiOp also directed the Bureau of Reclamation to pursue an alternative calculation of loss that addresses issues with the original calculation. An alternative calculation was developed by Jahn (2011) and a sensitivity analysis of the calculation was performed by Teply and Ceder (2013). Both the alternative calculation and sensitivity analysis were reviewed by the Independent Review Panel (IRP) on the long-term operations BiOp and reported in Anderson et al. (2013). The IRP identified several issues with the alternative loss calculation of Jahn (2011) and recommended that the closed-form error propagation approach be replaced and improved by a random variable approach. Anderson et al. (2013) reasoned that a random variable approach would provide a better accounting of variability in model parameters and estimate loss on a time scale that is more relevant to the salvage and loss process. Additionally, the IRP recommended using an alternate method for estimating loss when salvage is zero. We developed a Partially Observed Markov Process (POMP) model for estimating loss that addresses all of these issues and provides a flexible framework for estimating loss at both facilities. POMP models (a.k.a. state-space models) are a general class of mathematical models that can include random parameters and are used to analyze time series of data that have been measured incompletely and with error (Ionides et al. 2006, King et al. 2015b). Such approaches are of fundamental use in fisheries biology and management, where time series of population sizes or harvest quantities are estimated via subsampling methods (Hilborn and Mangel 1997). The POMP model framework provides a flexible and powerful modeling approach (King et al. 2015a) that allows us to mechanistically describe the processes of fish passage and sample collection at both facilities. Recent mathematical and computational developments allow us to obtain robust estimates of model likelihood, despite systems being non-linear, non-normal, time-series, and only partially observed (Ionides et al. 2006, Andrieu et al. 2010, Ionides et al. 2015, King et al. 2015b). Given the utility of model likelihood for a wide range of Frequentist and Bayesian inferential approaches (Pawitan 2001, Burnham and Anderson 2002), the POMP approach forms the foundation of a robust yet evolvable model of fish salvage. This report details our derivation of a general POMP model of fish passage that can be used for any species or race of fish at either facility. In the model, each facility is treated mechanistically as a series of components (e.g., pre-screen, primary louvers), reflecting their physical structures. Fish passage through each facility is governed by rates at which an individual survives and passes through each component. These rates are key parameters that not only describe the system but also are fundamental to our estimation of loss from sub-sampled salvage. We treat fish as discrete entities that move through distinct compartments in the facility in continuous time by formulating passage specifically using a compartment model (Godfrey 1983) enacted as a Continuous Time Markov Process (CTMP; Allen 2011). Samples are “taken” in the model by tracking the fish that are passed into salvage tanks that are counted at specific, but flexible intervals. This generates model predictions that are distributed appropriately for our observations (discrete number of fish counted in a variable amount of time, using a variable fraction of the water), which facilitates robust statistical inference even when no fish are counted in a sample. To provide a mathematical background for interested readers, we derive the basic concepts of POMP, compartment, and CTMP models, with special reference to our statistical and inferential needs, before developing our specific model. By constructing our models from a general starting point, we demonstrate the flexibility in specific application, thereby highlighting the evolvability of our models. As additional data (from, e.g., mark-recapture studies) become available, they can easily be integrated into the presented models without needing to redevelop the model’s general structure or inference machinery. In addition, the model we develop here can be used as a roadmap to design future experimental research about fish passage. We show how, using the POMP model we derive here and based on sound statistical inference (Pawitan 2001, Burnham and Anderson 2002, Ionides et al. 2006, Ionides et al. 2015, King et al. 2015b), one can make estimates of entrainment, loss, and salvage at timescales relevant for management decisions (i.e., daily), including the probability that certain triggers have been reached. In addition, we provide starting-point parameterizations for the model for Chinook and Steelhead at CVP and SWP, based on previous reports (CDWR 1973, Skinner 1974, CDWR 1986, Karp et al. 1995, Gingras 1997, Clark et al. 2009, Jahn 2011, Teply and Ceder 2013). We use these parameters to show how the model can be used to estimate loss (when salvage is zero or non-zero), quantify uncertainty associated with parameter variability, and quantify the probability that a threshold loss value had been exceeded. Given the mathematical and computational complexities of the model derived here, we recognize that most end-users of the loss calculation may not have the time, interest, or skill necessary to write, edit, run, and evaluate the code. Therefore, in addition to the mathematical description of our model, we provide here (see Appendix A6) a draft of its coded application, using the language R (R Core Team 2015) and relying on the pomp package (King et al. 2015b). However, we also recognize that most end-users still may not have the time, interest, or skill necessary to execute code within the R console. To that end, we have developed a user-friendly web application with a graphic user interface (GUI) to facilitate application of the tool in real time by end-users. We include here a live version of our application for evaluation along with this report. The POMP model we develop and apply here provides a flexible framework for estimating loss at the CVP and SWP facilities. The model is able to address issues with the Jahn (2011) calculation and additional recommendations made in the IRP report (Anderson et al. 2013), including estimation of loss when zero salvage is observed, more realistic accounting of uncertainty in model parameters, and estimation of loss at the time scale over which it occurs. However, construction of our model highlighted several issues with accuracy and precision of estimation given the available data and the reporting of daily values from a continuous process. Estimation of loss from salvage requires robust estimates of the survival rates for each species through the compartments of the facilities. The available estimates from previous experiments encompass a considerable range of values (CDWR 1973, Skinner 1974, CDWR 1986, Karp et al. 1995, Gingras 1997, Clark et al. 2009, Jahn 2011, Teply and Ceder 2013). When the variability in all these parameters is integrated into a model, the confidence intervals become so wide that their utility for evaluating if an RPA trigger has been reached becomes compromised. This would be true for any model formulation that simultaneously accounts for variability in all model parameters while modeling passage in a statistically robust fashion. We highlight specific issues with particular parameters and in general across the delta and conclude with recommendations for future avenues of experimental and modeling research and development that will improve the accuracy and precision of loss estimates at the CVP and SWP facilities.

The purpose of this study was twofold: First, we aimed to assess baseline conditions for cutthroat trout rearing capacity in a reach of Drift Creek, below the proposed dam. Second, we aimed to assess potential changes in rearing capacity that would result from alternative reservoir management scenarios. Our approach included environmental sampling and a modeling effort that estimated cutthroat trout rearing capacity within the study reach. Our survey measurements including an assessment of in-stream habitat, including stream channel geometry, flow, and water temperature. We collected baseline condition measurements for seven habitat units on three dates, which represented a range of flow and habitat conditions that approximate those anticipated to result from the alternative reservoir management strategies. Next, we combined these environmental data with modeled stream temperatures from a previous effort to generate estimates of rearing capacity associated with the reservoir management strategies. Briefly, we found that substantial rearing habitat exists below the proposed dam, but this habitat is strongly limited by temperature. The impacts of reservoir management on downstream rearing habitat are generally favorable, although there are considerable differences among the alternative options. In general, it appears that rearing capacity associated with the management strategies involving piped delivery of water are less favorable than rearing capacity associated with the management strategies involving in-stream delivery of water.

Chapter 4: Juvenile Steelhead Production and Smolt Survival The Umatilla Basin Natural Production Monitoring and Evaluation Project 2015 Annual Progress Report

Population Viability Analyses (PVA) are being conducted by Lincoln Park Zoo and Population Management Center researchers through funding from the Institute of Museum and Library Services (IMLS). The project team uses ZooRisk 3.80 (Earnhardt et al. 2008), a PVA modeling software, to examine what would happen to AZA populations if current conditions remain the same (the baseline scenario), and then assess the impact of changes in reproductive rates, space availability, imports/exports, and other potential management actions (alternate scenarios). Model scenarios for this population were developed with members of the Association of Zoos and Aquarium (AZA) Guam Kingfisher Animal Program and the U.S. Fish and Wildlife Service (FWS) Guam Kingfisher Recovery Committee in 2015.

Population Viability Analyses (PVA) are being conducted by Lincoln Park Zoo and Population Management Center researchers through funding from the Institute of Museum and Library Services (IMLS). The project team uses ZooRisk 3.80 (Earnhardt et al. 2008), a PVA modeling software, to examine what would happen to animal populations managed by the Association of Zoos and Aquariums (AZA) if current conditions remain the same (the baseline scenario), and then assess the impact of changes in reproductive rates, space availability, imports/exports, and other potential management actions (alternate scenarios). Model scenarios for this population were developed with members of the AZA Red Wolf Species Survival Plan® and the United States Fish and Wildlife Service (USFWS) Red Wolf Recovery Program during 2014.