Multistage time-to-event models improve survival inference by partitioning mortality processes of tracked organisms.

Advances in tagging technologies are expanding opportunities to estimate survival of fish and wildlife populations. Yet, capture and handling effects could impact survival outcomes and bias inference about natural mortality processes. We developed a multistage time-to-event model that can partition the survival process into sequential phases that reflect the tagged animal experience, including handling and release mortality, post-release recovery mortality, and subsequently, natural mortality. We demonstrate performance of multistage survival models through simulation testing and through fish and bird telemetry case studies. Models are implemented in a Bayesian framework and can accommodate left, right, and interval censorship events. Our results indicate that accurate survival estimates can be achieved with reasonable sample sizes ( n ≈ 100 + ) and that multimodel inference can inform hypotheses about the configuration and length of survival stages needed to adequately describe mortality processes for tracked specimens. While we focus on survival estimation for tagged fish and wildlife populations, multistage time-to-event models could be used to understand other phenomena of interest such as migration, reproduction, or disease events across a range of taxa including plants and insects.

Whole-lake acoustic telemetry to evaluate survival of stocked juvenile fish.

Estimates of juvenile survival are critical for informing population dynamics and the ecology of fish, yet these demographic parameters are difficult to measure. Here, we demonstrate that advances in animal tracking technology provide opportunities to evaluate survival of juvenile tagged fish. We implemented a whole-lake telemetry array in conjunction with small acoustic tags (including tags < 1.0 g) to track the fate of stocked juvenile cisco (Coregonus artedi) as part of a native species restoration effort in the Finger Lakes region of New York, USA. We used time-to-event modeling to characterize the survival function of stocked fish, where we infer mortality as the cessation of tag detections. Survival estimates revealed distinct stages of juvenile cisco mortality including high immediate post-release mortality, followed by a period of elevated mortality during an acclimation period. By characterizing mortality over time, the whole-lake biotelemetry effort provided information useful for adapting stocking practices that may improve survival of stocked fish, and ultimately the success of the species reintroduction effort. The combination of acoustic technology and time-to-event modeling to inform fish survival may have wide applicability across waterbodies where receiver arrays can be deployed at scale and where basic assumptions about population closure can be satisfied.

Detecting and analysing intraspecific genetic variation with eDNA: From population genetics to species abundance.

Advancements in environmental DNA (eDNA) approaches have allowed for rapid and efficient species detections in diverse environments. Although most eDNA research is focused on leveraging genetic diversity to identify taxa, some recent studies have explored the potential for these approaches to detect within-species genetic variation, allowing for population genetic assessments and abundance estimates from environmental samples. However, we currently lack a framework outlining the key considerations specific to generating, analysing and applying eDNA data for these two purposes. Here, we discuss how various genetic markers differ with regard to genetic information and detectability in environmental samples and how analysis of eDNA samples differs from common tissue-based analyses. We then outline how it may be possible to obtain species absolute abundance estimates from eDNA by detecting intraspecific genetic variation in mixtures of DNA under multiple scenarios. We also identify the major causes contributing to allele detection and frequency errors in eDNA data, discuss their consequences for population-level analyses and outline bioinformatic approaches to detect and remove erroneous sequences. This review summarizes the key advances required to harness the full potential of eDNA-based intraspecific genetic variation to inform population-level questions in ecology, evolutionary biology and conservation management.

Toward absolute abundance for conservation applications: Estimating the number of contributors via microhaplotype genotyping of mixed-DNA samples.

Molecular methods including metabarcoding and quantitative polymerase chain reaction have shown promise for estimating species abundance by quantifying the concentration of genetic material in field samples. However, the relationship between specimen abundance and detectable concentrations of genetic material is often variable in practice. DNA mixture analysis represents an alternative approach to quantify specimen abundance based on the presence of unique alleles in a sample. The DNA mixture approach provides novel opportunities to inform ecology and conservation by estimating the absolute abundance of target taxa through molecular methods; yet, the challenges associated with genotyping many highly variable markers in mixed-DNA samples have prevented its widespread use. To advance molecular approaches for abundance estimation, we explored the utility of microhaplotypes for DNA mixture analysis by applying a 125-marker panel to 1179 Chinook salmon (Oncorhynchus tshawytscha) smolts from the Sacramento-San Joaquin Delta, California, USA. We assessed the accuracy of DNA mixture analysis through a combination of mock mixtures containing DNA from up to 20 smolts and a trophic ecological application enumerating smolts in predator diets. Mock DNA mixtures of up to 10 smolts could reliably be resolved using microhaplotypes, and increasing the panel size would likely facilitate the identification of more individuals. However, while analysis of predator gastrointestinal tract contents indicated DNA mixture analysis could discern the presence of multiple prey items, poor and variable DNA quality prevented accurate genotyping and abundance estimation. Our results indicate that DNA mixture analysis can perform well with high-quality DNA, but methodological improvements in genotyping degraded DNA are necessary before this approach can be used on marginal-quality samples.

Rapid population growth and high management costs have created a narrow window for control of introduced hippos in Colombia.

The introduction of hippos into the wild in Colombia has been marked by their rapid population growth and widespread dispersal on the landscape, high financial costs of management, and conflicting social perspectives on their management and fate. Here we use population projection models to investigate the effectiveness and cost of management options under consideration for controlling introduced hippos. We estimate there are 91 hippos in the middle Magdalena River basin, Colombia, and the hippo population is growing at an estimated rate of 9.6% per year. At this rate, there will be 230 hippos by 2032 and over 1,000 by 2050. Applying the population control methods currently under consideration will cost at least 1-2 million USD to sufficiently decrease hippo population growth to achieve long-term removal, and depending on the management strategy selected, there may still be hippos on the landscape for 50-100 years. Delaying management actions for a single decade will increase minimum costs by a factor of 2.5, and some methods may become infeasible. Our approach illustrates the trade-offs inherent between cost and effort in managing introduced species, as well as the importance of acting quickly, especially when dealing with species with rapid population growth rates and potential for significant ecological and social impacts.

Biodiversity underpins fisheries resilience to exploitation in the Amazon river basin.

Inland fisheries feed greater than 150 million people globally, yet their status is rarely assessed due to their socio-ecological complexity and pervasive lack of data. Here, we leverage an unprecedented landings time series from the Amazon, Earth's largest river basin, together with theoretical food web models to examine (i) taxonomic and trait-based signatures of exploitation in inland fish landings and (ii) implications of changing biodiversity for fisheries resilience. In both landings time series and theory, we find that multi-species exploitation of diverse inland fisheries results in a hump-shaped landings evenness curve. Along this trajectory, abundant and large species are sequentially replaced with faster growing and smaller species. Further theoretical analysis indicates that harvests can be maintained for a period of time but that continued biodiversity depletion reduces the pool of compensating species and consequently diminishes fisheries resilience. Critically, higher fisheries biodiversity can delay fishery collapse. Although existing landings data provide an incomplete snapshot of long-term dynamics, our results suggest that multi-species exploitation is affecting freshwater biodiversity and eroding fisheries resilience in the Amazon. More broadly, we conclude that trends in landings evenness could characterize multi-species fisheries development and aid in assessing their sustainability.

Reducing adverse impacts of Amazon hydropower expansion.

Proposed hydropower dams at more than 350 sites throughout the Amazon require strategic evaluation of trade-offs between the numerous ecosystem services provided by Earth's largest and most biodiverse river basin. These services are spatially variable, hence collective impacts of newly built dams depend strongly on their configuration. We use multiobjective optimization to identify portfolios of sites that simultaneously minimize impacts on river flow, river connectivity, sediment transport, fish diversity, and greenhouse gas emissions while achieving energy production goals. We find that uncoordinated, dam-by-dam hydropower expansion has resulted in forgone ecosystem service benefits. Minimizing further damage from hydropower development requires considering diverse environmental impacts across the entire basin, as well as cooperation among Amazonian nations. Our findings offer a transferable model for the evaluation of hydropower expansion in transboundary basins.

A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams.

Restoring stream ecosystem integrity by removing unused or derelict dams has become a priority for watershed conservation globally. However, efforts to restore connectivity are constrained by the availability of accurate dam inventories which often overlook smaller unmapped riverine dams. Here we develop and test a machine learning approach to identify unmapped dams using a combination of publicly available topographic and geospatial habitat data. Specifically, we trained a random forest classification algorithm to identify unmapped dams using digitally engineered predictor variables and known dam sites for validation. We applied our algorithm to two subbasins in the Hudson River watershed, USA, and quantified connectivity impacts, as well as evaluated a range of predictor sets to examine tradeoffs between classification accuracy and model parameterization effort. The random forest classifier achieved high accuracy in predicting dam sites (true positive rate = 89%, false positive rate = 1.2%) using a subset of variables related to stream slope and presence of upstream lentic habitats. Unmapped dams were prevalent throughout the two test watersheds. In fact, existing dam inventories underestimated the true number of dams by ∼80-94%. Accounting for previously unmapped dams resulted in a 62-90% decrease in dendritic connectivity indices for migratory fishes. Unmapped dams may be pervasive and can dramatically bias stream connectivity information. However, we find that machine learning approaches can provide an accurate and scalable means of identifying unmapped dams that can guide efforts to develop accurate dam inventories, thereby informing and empowering efforts to better manage them.

Declining diversity of wild-caught species puts dietary nutrient supplies at risk.

Although biodiversity loss adversely influences a variety of ecosystem functions, how declining wild food diversity affects nutrient supplies for people is poorly understood. Here, we analyze the impact of declining biodiversity on nutrients supplied by fish using detailed information from the Peruvian Amazon, where inland fisheries provide a critical source of nutrition for many of the region's 800,000 people. We found that the impacts of biodiversity loss on nutrient supplies depended on compensation, trophic dynamics, and functional diversity. When small sedentary species compensated for declines in large migratory species, fatty acid supplies increased, while zinc and iron supplies decreased. In contrast, the probability of failing to maintain supplies or nutrient supply risk increased when species were nutritionally unique. Our results show that trait-based regulations and public health polices need to consider biodiversity's vital role in sustaining nutritional benefits for over 2 billion people dependent on wild foods across the globe.

Nuclear eDNA estimates population allele frequencies and abundance in experimental mesocosms and field samples.

Advances in environmental DNA (eDNA) methodologies have led to improvements in the ability to detect species and communities in aquatic environments, yet the majority of studies emphasize biological diversity at the species level by targeting variable sites within the mitochondrial genome. Here, we demonstrate that eDNA approaches also have the capacity to detect intraspecific diversity in the nuclear genome, allowing for assessments of population-level allele frequencies and estimates of the number of genetic contributors in an eDNA sample. Using a panel of microsatellite loci developed for the round goby (Neogobius melanostomus), we tested the similarity between eDNA-based and individual tissue-based estimates of allele frequencies from experimental mesocosms and in a field-based trial. Subsequently, we used a likelihood-based DNA mixture framework to estimate the number of unique genetic contributors in eDNA samples and in simulated mixtures of alleles. In both mesocosm and field samples, allele frequencies from eDNA were highly correlated with allele frequencies from genotyped round goby tissue samples, indicating nuclear markers can be reliably amplified from water samples. DNA mixture analyses were able to estimate the number of genetic contributors from mesocosm eDNA samples and simulated mixtures of DNA from up to 58 individuals, with the degree of positive or negative bias dependent on the filtering scheme of low-frequency alleles. With this study we document the application of eDNA and multiple amplicon-based methods to obtain intraspecific nuclear genetic information and estimate the absolute abundance of a species in eDNA samples. With proper validation, this approach has the potential to advance noninvasive survey methods to characterize populations and detect population-level genetic diversity.

Substitution of inland fisheries with aquaculture and chicken undermines human nutrition in the Peruvian Amazon.

With declining capture fisheries production, maintaining nutrient supplies largely hinges on substituting wild fish with economically comparable farmed animals. Although such transitions are increasingly commonplace across global inland and coastal communities, their nutritional consequences are unknown. Here, using human demographic and health information, and fish nutrient composition data from the Peruvian Amazon, we show that substituting wild inland fisheries with chicken and aquaculture has the potential to exacerbate iron deficiencies and limit essential fatty acid supplies in a region already experiencing high prevalence of anaemia and malnutrition. Substituting wild fish with chicken, however, can increase zinc and protein supplies. Chicken and aquaculture production also increase greenhouse gas emissions, agricultural land use and eutrophication. Thus, policies that enable access to wild fisheries and their sustainable management while improving the quality, diversity and environmental impacts of farmed species will be instrumental in ensuring healthy and sustainable food systems.

Reducing greenhouse gas emissions of Amazon hydropower with strategic dam planning.

Hundreds of dams have been proposed throughout the Amazon basin, one of the world's largest untapped hydropower frontiers. While hydropower is a potentially clean source of renewable energy, some projects produce high greenhouse gas (GHG) emissions per unit electricity generated (carbon intensity). Here we show how carbon intensities of proposed Amazon upland dams (median = 39 kg CO2eq MWh-1, 100-year horizon) are often comparable with solar and wind energy, whereas some lowland dams (median = 133 kg CO2eq MWh-1) may exceed carbon intensities of fossil-fuel power plants. Based on 158 existing and 351 proposed dams, we present a multi-objective optimization framework showing that low-carbon expansion of Amazon hydropower relies on strategic planning, which is generally linked to placing dams in higher elevations and smaller streams. Ultimately, basin-scale dam planning that considers GHG emissions along with social and ecological externalities will be decisive for sustainable energy development where new hydropower is contemplated.

Accurate recapture identification for genetic mark-recapture studies with error-tolerant likelihood-based match calling and sample clustering.

Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark-recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark-recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark-recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus (Odobenus rosmarus divergens) and fishers (Pekania pennanti). A novel two-stage clustering approach is demonstrated for genetic mark-recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark-recapture studies. Moderately sized SNP (64+) and MSAT (10-15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.

Dose-Response Relationships for Meningioma Radiosurgery.

OBJECTIVE: Dose-response relationships for meningioma radiosurgery are poorly characterized. We evaluated determinants of local recurrence for meningiomas treated with Gamma Knife radiosurgery (GKRS), to guide future treatment approaches to optimize tumor control. MATERIALS AND METHODS: A total of 101 consecutive patients (108 tumors) who underwent GKRS for benign, atypical, or malignant meningiomas between 1998 and 2011 were studied. Local recurrence was assessed. Cox proportional hazards and logistic regression analyses were used to determine the association of patient-related, tumor-related, and treatment-related characteristics with local recurrence. Acute and late toxicity was evaluated. RESULTS: World Health Organization (2007 classification) tumor grade was I (82%), II (11%), or III (7%). Median dose was 14 Gy (range, 10 to 18 Gy) for grade I tumors and 16 Gy (range, 12 to 20 Gy) for grade II and III tumors. Median follow-up was 25 months (maximum, 17 y). Two- /5-year actuarial local control rates were 100%/98% for grade I tumors and 76%/56% for grade II/III tumors. Higher tumor grade and lower GKRS dose were associated with local failure. In this cohort, there was a 42% relative reduction in local recurrence for each 1 Gy of dose escalation. CONCLUSIONS: Treatment was well tolerated with no moderate or severe toxicity. Tumor control was excellent in benign tumors and suboptimal in higher grade tumors. Because the main determinant of local recurrence was GKRS dose, we recommend dose escalation for atypical or malignant tumors to doses between 16 and 20 Gy where critical structures allow.

Global fishery development patterns are driven by profit but not trophic level.

Successful ocean management needs to consider not only fishing impacts but drivers of harvest. Consolidating post-1950 global catch and economic data, we assess which attributes of fisheries are good indicators for fishery development. Surprisingly, year of development and economic value are not correlated with fishery trophic levels. Instead, patterns emerge of profit-driven fishing for attributes related to costs and revenues. Post-1950 fisheries initially developed on shallow ranging species with large catch, high price, and big body size, and then expanded to less desirable species. Revenues expected from developed fisheries declined 95% from 1951 to 1999, and few high catch or valuable fishing opportunities remain. These results highlight the importance of economic attributes of species as leading indicators for harvest-related impacts in ocean ecosystems.