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60 records · Page 4Linked to original sources
USGS2017

Age and growth comparisons of Hovsgol grayling ( Thymallus nigrescens Dorogostaisky, 1923), Baikal grayling ( T. baicalensis Dybowski, 1874), and lenok ( Brachymystax lenok Pallas, 1773) in lentic and lotic habitats of Northern Mongolia

Despite concern over the conservation status of many Mongolian salmonids and the importance of their ecological role in Mongolia's aquatic ecosystems, little is known about their basic biology. Hovsgol grayling ( Thymallus nigrescens ) is endemic to Lake Hovsgol, Mongolia and listed as endangered on the Mongolian Red List. Baikal grayling ( T. baicalensis ) and lenok ( Brachymystax lenok ) are found in lakes and rivers throughout the Selenge drainage. A detailed study of the age and growth of these three salmonids was conducted based on 1,682 samples collected from July 2006 to July 2013 in Lake Hovsgol, its outlet the Eg River, and one of the Eg's largest tributaries, the Uur River. Our results suggest that Hovsgol grayling in particular can reach a much older maximum age (17 years in our samples) than previously believed based on aging from scales. Female Hovsgol grayling were heavier at a given length than their male counterparts. Lenok had a greater average length-at-age in Lake Hovsgol compared to the rivers and greater weight-at-length in the warmer Uur River than in the Eg; female lenok from the rivers had a greater average length-at-age than their male counterparts. This study provides critical new information for the management and conservation of these threatened salmonid species in Mongolia.

Journal of Applied Ichthyology↗
USGS2018

An epidemiological model of virus transmission in salmonid fishes of the Columbia River Basin

We have developed a dynamic epidemiological model informed by records of viral presence and genotypes to evaluate potential transmission routes maintaining a viral pathogen in economically and culturally important anadromous fish populations. In the Columbia River Basin, infectious hematopoietic necrosis virus (IHNV) causes severe disease, predominantly in juvenile steelhead trout ( Oncorhynchus mykiss ) and less frequently in Chinook salmon ( O. tshawytscha ). Mortality events following IHNV infection can be devastating for individual hatchery programs. Despite reports of high local mortality and extensive surveillance efforts, there are questions about how viral transmission is maintained. Modeling this system offers important insights into disease transmission in natural aquatic systems, as well as about the data requirements for generating accurate estimates about transmission routes and infection probabilities. We simulated six scenarios in which testing rates and the relative importance of different transmission routes varied. The simulations demonstrated that the model accurately identified routes of transmission and inferred infection probabilities accurately when there was testing of all cohort-sites. When testing records were incomplete, the model accurately inferred which transmission routes exposed particular cohort-sites but generated biased infection probabilities given exposure. After validating the model and generating guidelines for result interpretation, we applied the model to data from 14 annual cohorts (2000–2013) at 24 focal sites in a sub-region of the Columbia River Basin, the lower Columbia River (LCR), to quantify the relative importance of potential transmission routes in this focal sub-region. We demonstrate that exposure to IHNV via the return migration of adult fish is an important route for maintaining IHNV in the LCR sub-region, and the probability of infection following this exposure was relatively high at 0.16. Although only 1% of cohort-sites experienced self-exposure by infected juvenile fish, this transmission route had the greatest probability of infection (0.22). Increased testing and/or determining whether transmission can occur from cohort-sites without testing records (e.g., determining there was no testing record because there were no fish at the cohort-site) are expected to improve inference about infection probabilities. Increased use of secure water supplies and continued use of biosecurity protocols may reduce IHNV transmission from adult fish and juvenile fish within the site, respectively, to juvenile salmonids at hatcheries. Models and conclusions from this study are potentially relevant to understanding the relative importance of transmission routes for other important aquatic pathogens in salmonids, including the agents of bacterial kidney disease and coldwater disease, and the basic approach may be useful for other pathogens and hosts in other geographic regions.

Ecological Modelling↗
USGS2019

Occupancy models for citizen-science data

Large‐scale citizen‐science projects, such as atlases of species distribution, are an important source of data for macroecological research, for understanding the effects of climate change and other drivers on biodiversity, and for more applied conservation tasks, such as early‐warning systems for biodiversity loss. However, citizen‐science data are challenging to analyse because the observation process has to be taken into account. Typically, the observation process leads to heterogeneous and non‐random sampling, false absences, false detections, and spatial correlations in the data. Increasingly, occupancy models are being used to analyse atlas data. We advocate a dual approach to strengthen inference from citizen science data for the questions the programme is intended to address: (a) the survey design should be chosen with a particular set of questions and associated analysis strategy in mind and (b) the statistical methods should be tailored not only to those questions but also to the specific characteristics of the data. We review the consequences of particular survey design choices that typically need to be made in atlas‐style citizen‐science projects. These include spatial resolution of the sampling units, allocation of effort in space, and collection of information about the observation process. On the analysis side, we review extensions of the basic occupancy models that are frequently necessary with atlas data, including methods for dealing with heterogeneity, non‐independent detections, false detections, and violation of the closure assumption. New technologies, such as cell‐phone apps and fixed remote detection devices, are revolutionizing citizen‐science projects. There is an opportunity to maximize the usefulness of the resulting datasets if the protocols are rooted in robust statistical designs and data analysis issues are being considered. Our review provides guidelines for designing new projects and an overview of the current methods that can be used to analyse data from such projects.

Methods in Ecology and Evolution↗
USGS2016

Quantifying resilience

The biosphere is under unprecedented pressure, reflected in rapid changes in our global ecological, social, technological and economic systems. In many cases, ecological and social systems can adapt to these changes over time, but when a critical threshold is surpassed, a system under stress can undergo catastrophic change and reorganize into a different state. The concept of resilience, introduced more than 40 years ago in the ecological sciences, captures the behaviour of systems that can occur in alternative states. The original definition of resilience forwarded by Holling ( 1973 ) is still the most useful. It defines resilience as the amount of disturbance that a system can withstand before it shifts into an alternative stable state. The idea of alternative stable states has clear and profound implications for ecological management. Coral reefs, for example, are high-diversity systems that provide key ecosystem services such as fisheries and coastal protection. Human impacts are causing significant, ongoing reef degradation, and many reefs have shifted from coral- to algal-dominated states in response to anthropogenic pressures such as elevated water temperatures and overfishing. Understanding and differentiating between the factors that help maintain reefs in coral-dominated states vs. those that facilitate a shift to an undesired algal-dominated state is a critical step towards sound management and conservation of these, and other, important social–ecological systems. Resilience has gained popularity among both academicians and laypeople, as a term meant to describe a systems’ ability to withstand disturbance. Resilience has become a buzzword in the last decade, as shown by its increasing appearance in calls for research proposals and scientific citation data bases. The term resilience has in many cases lost the clarity of the original definition and in fact is frequently used in a manner in direct opposition to the original definition. Many current uses of the concept are loose and incorrect. The term is becoming increasingly used in a normative sense (Brand & Jax 2007 ), as if resilience were a desirable quality of systems. However, even systems in highly undesirable states, such as macro-algae dominated reefs, or city cores in poverty traps, may be highly resilient, which is to say they withstand attempts to transform them into different (desirable) states. Operationalizing the concept of resilience for application and management has been difficult. Misuse of the term can have significant negative impacts, because resilience is being used to help guide responses to natural disasters and to assess the sustainability of ecosystems and urban systems and has been driving international research priorities. Resilience has been argued to be a basic emergent property of systems, a process or a rate. We focus on the original concept as described by Holling, which is that of an emergent system property; when a system is in a desirable state and managers wish to enhance resilience, or when the system is in an undesirable state and managers wish to erode resilience and foster a transformation to an alternative state. Fostering or eroding resilience is a process. When a system is perturbed but resilience is not exceeded, then the recovery can be measured as a rate. Several frameworks to operationalize resilience have been proposed. A decade ago, a special feature focused on quantifying resilience was published in the journal Ecosystems (Carpenter, Westley & Turner 2005 ). The approach there was towards identifying surrogates of resilience, but few of the papers proposed quantifiable metrics. Consequently, many ecological resilience frameworks remain vague and difficult to quantify, a problem that this special feature aims to address. However, considerable progress has been made during the last decade (e.g. Pope, Allen & Angeler 2014 ). Although some argue that resilience is best kept as an unquantifiable, vague concept (Quinlan et al . 2016 ), to be useful for managers, there must be concrete guidance regarding how and what to manage and how to measure success (Garmestani, Allen & Benson 2013 ; Spears et al . 2015 ). Ideas such as ‘resilience thinking’ have utility in helping stakeholders conceptualize their systems, but provide little guidance on how to make resilience useful for ecosystem management, other than suggesting an ambiguous, Goldilocks approach of being just right (e.g. diverse, but not too diverse; connected, but not too connected). Here, we clarify some prominent resilience terms and concepts, introduce and synthesize the papers in this special feature on quantifying resilience and identify core unanswered questions related to resilience.

Journal of Applied Ecology↗
USGS2017

Safari Science: Assessing the reliability of citizen science data for wildlife surveys

Protected areas are the cornerstone of global conservation, yet financial support for basic monitoring infrastructure is lacking in 60% of them. Citizen science holds potential to address these shortcomings in wildlife monitoring, particularly for resource-limited conservation initiatives in developing countries – if we can account for the reliability of data produced by volunteer citizen scientists (VCS). This study tests the reliability of VCS data vs. data produced by trained ecologists, presenting a hierarchical framework for integrating diverse datasets to assess extra variability from VCS data. Our results show that while VCS data are likely to be overdispersed for our system, the overdispersion varies widely by species. We contend that citizen science methods, within the context of East African drylands, may be more appropriate for species with large body sizes, which are relatively rare, or those that form small herds. VCS perceptions of the charisma of a species may also influence their enthusiasm for recording it. Tailored programme design (such as incentives for VCS) may mitigate the biases in citizen science data and improve overall participation. However, the cost of designing and implementing high-quality citizen science programmes may be prohibitive for the small protected areas that would most benefit from these approaches. Synthesis and applications . As citizen science methods continue to gain momentum, it is critical that managers remain cautious in their implementation of these programmes while working to ensure methods match data purpose. Context-specific tests of citizen science data quality can improve programme implementation, and separate data models should be used when volunteer citizen scientists' variability differs from trained ecologists' data. Partnerships across protected areas and between protected areas and other conservation institutions could help to cover the costs of citizen science programme design and implementation.

Journal of Applied Ecology↗
USGS2020

A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species

Background Predictive models of post-fire tree and stem mortality are vital for management planning and understanding fire effects. Post-fire tree and stem mortality have been traditionally modeled as a simple empirical function of tree defenses ( e.g., bark thickness) and fire injury ( e.g., crown scorch). We used the Fire and Tree Mortality database (FTM)—which includes observations of tree mortality in obligate seeders and stem mortality in basal resprouting species from across the USA—to evaluate the accuracy of post-fire mortality models used in the First Order Fire Effects Model (FOFEM) software system. The basic model in FOFEM, the Ryan and Amman (R-A) model, uses bark thickness and percentage of crown volume scorched to predict post-fire mortality and can be applied to any species for which bark thickness can be calculated (184 species-level coefficients are included in the program). FOFEM (v6.7) also includes 38 species-specific tree mortality models (26 for gymnosperms, 12 for angiosperms), with unique predictors and coefficients. We assessed accuracy of the R-A model for 44 tree species and accuracy of 24 species-specific models for 13 species, using data from 93 438 tree-level observations and 351 fires that occurred from 1981 to 2016. Results For each model, we calculated performance statistics and provided an assessment of the representativeness of the evaluation data. We identified probability thresholds for which the model performed best, and the best thresholds with either ≥80% sensitivity or specificity. Of the 68 models evaluated, 43 had Area Under the Receiver Operating Characteristic Curve (AUC) values ≥0.80, indicating excellent performance, and 14 had AUCs <0.7, indicating poor performance. The R-A model often over-predicted mortality for angiosperms; 5 of 11 angiosperms had AUCs <0.7. For conifers, R-A over-predicted mortality for thin-barked species and for small diameter trees. The species-specific models had significantly higher AUCs than the R-A models for 10 of the 22 models, and five additional species-specific models had more balanced errors than R-A models, even though their AUCs were not significantly different or were significantly lower. Conclusions Approximately 75% of models tested had acceptable, excellent, or outstanding predictive ability. The models that performed poorly were primarily models predicting stem mortality of angiosperms or tree mortality of thin-barked conifers. This suggests that different approaches—such as different model forms, better estimates of bark thickness, and additional predictors—may be warranted for these taxa. Future data collection and research should target the geographical and taxonomic data gaps and poorly performing models identified in this study. Our evaluation of post-fire tree mortality models is the most comprehensive effort to date and allows users to have a clear understanding of the expected accuracy in predicting tree death from fire for 44 species.

Fire Ecology↗