Alexander K. Fremier

Critical considerations for the application of environmental DNA methods to detect aquatic species

Summary Species detection using environmental DNA (eDNA) has tremendous potential for contributing to the understanding of the ecology and conservation of aquatic species. Detecting species using eDNA methods, rather than directly sampling the organisms, can reduce impacts on sensitive species and increase the power of field surveys for rare and elusive species. The sensitivity of eDNA methods, however, requires a heightened awareness and attention to quality assurance and quality control protocols. Additionally, the interpretation of eDNA data demands careful consideration of multiple factors. As eDNA methods have grown in application, diverse approaches have been implemented to address these issues. With interest in eDNA continuing to expand, supportive guidelines for undertaking eDNA studies are greatly needed. Environmental DNA researchers from around the world have collaborated to produce this set of guidelines and considerations for implementing eDNA methods to detect aquatic macroorganisms. Critical considerations for study design include preventing contamination in the field and the laboratory, choosing appropriate sample analysis methods, validating assays, testing for sample inhibition and following minimum reporting guidelines. Critical considerations for inference include temporal and spatial processes, limits of correlation of eDNA with abundance, uncertainty of positive and negative results, and potential sources of allochthonous DNA. We present a synthesis of knowledge at this stage for application of this new and powerful detection method.

Global sensitivity analysis for complex ecological models: a case study of riparian cottonwood population dynamics

Mechanism-based ecological models are a valuable tool for understanding the drivers of complex ecological systems and for making informed resource-management decisions. However, inaccurate conclusions can be drawn from models with a large degree of uncertainty around multiple parameter estimates if uncertainty is ignored. This is especially true in nonlinear systems with multiple interacting variables. We addressed these issues for a mechanism-based, demographic model of Populus fremontii (Fremont cottonwood), the dominant riparian tree species along southwestern U.S. rivers. Many cottonwood populations have declined following widespread floodplain conversion and flow regulation. As a result, accurate predictive models are needed to analyze effects of future climate change and water management decisions. To quantify effects of parameter uncertainty, we developed an analytical approach that combines global sensitivity analysis (GSA) with classification and regression trees (CART) and Random Forest, a bootstrapping CART method. We used GSA to quantify the interacting effects of the full range of uncertainty around all parameter estimates, Random Forest to rank parameters according to their total effect on model predictions, and CART to identify higher-order interactions. GSA simulations yielded a wide range of predictions, including annual germination frequency of 10-100%, annual first-year survival frequency of 0-50%, and patch occupancy of 0-100%. This variance was explained primarily by complex interactions among abiotic parameters including capillary fringe height, stage-discharge relationship, and floodplain accretion rate, which interacted with biotic factors to affect survival. Model precision was primarily influenced by well-studied parameter estimates with minimal associated uncertainty and was virtually unaffected by parameter estimates for which there are no available empirical data and thus a large degree of uncertainty. Therefore, research to improve model predictions should not always focus on the least-studied parameters, but rather those to which model predictions are most sensitive. We advocate the combined use of global sensitivity analysis, CART, and Random Forest to: (1) prioritize research efforts by ranking variable importance; (2) efficiently improve models by focusing on the most important parameters; and (3) illuminate complex model properties including nonlinear interactions. We present an analytical framework that can be applied to any model with multiple uncertain parameter estimates.

The Ectomycorrhizal Fungal Community in a Neotropical Forest Dominated by the Endemic Dipterocarp Pakaraimaea dipterocarpacea

Ectomycorrhizal (ECM) plants and fungi can be diverse and abundant in certain tropical ecosystems. For example, the primarily paleotropical ECM plant family Dipterocarpaceae is one of the most speciose and ecologically important tree families in Southeast Asia. Pakaraimaea dipterocarpacea is one of two species of dipterocarp known from the Neotropics, and is also the only known member of the monotypic Dipterocarpaceae subfamily Pakaraimoideae. This Guiana Shield endemic is only known from the sandstone highlands of Guyana and Venezuela. Despite its unique phylogenetic position and unusual geographical distribution, the ECM fungal associations of P. dipterocarpacea are understudied throughout the tree’s range. In December 2010 we sampled ECM fungi on roots of P. dipterocarpacea and the co-occurring ECM tree Dicymbe jenmanii (Fabaceae subfamily Caesalpinioideae) in the Upper Mazaruni River Basin of Guyana. Based on ITS rDNA sequencing we documented 52 ECM species from 11 independent fungal lineages. Due to the phylogenetic distance between the two host tree species, we hypothesized that P. dipterocarpacea would harbor unique ECM fungi not found on the roots of D. jenmanii. Although statistical tests suggested that several ECM fungal species did exhibit host preferences for either P. dipterocarpacea or D. jenmanii, most of the ECM fungi were multi-host generalists. We also detected several ECM fungi that have never been found in long-term studies of nearby rainforests dominated by other Dicymbe species. One particular mushroom-forming fungus appears to be unique and may represent a new ECM lineage of Agaricales that is endemic to the Neotropics.

Understanding Spatiotemporal Lags in Ecosystem Services to Improve Incentives

Ecosystem-service production is strongly influenced by the landscape configuration of natural and human systems. Ecosystem services are not only produced and consumed locally but can be transferred within and among ecosystems. The time and distance between the producer and the consumer of ecosystem services can be considered lags in ecosystem-service provisioning. Incorporation of heterogeneity and lag effects into conservation incentives helps identify appropriate governance systems and incentive mechanisms for effective ecosystem-service management. These spatiotemporal dimensions are particularly apparent in river—riparian systems, which provide a suite of important ecosystem services and promote biodiversity conservation at multiple scales, including habitat protection and functional connectivity. Management of ecosystem services with spatiotemporal lags requires an interdisciplinary consideration of both the biophysical landscape features that produce services and the human actors that control and benefit from the creation of those services.

Scaling up functional traits for ecosystem services with remote sensing: concepts and methods

Abstract Ecosystem service‐based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot‐level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community‐weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.

The relative stability of salmon redds and unspawned streambeds

Where female salmon build nests (“redds”), streambed material is mixed, fine sediment is winnowed, and bed material is moved into a tailspill mound resembling the shape of a dune. Completed redd surfaces are coarser and better sorted than unspawned beds, which is thought to increase redd stability because larger grains are heavier and harder to move, and sorting increases friction angles for mobility. However, spawning also loosens sediment and creates topography that accelerates flow, which can increase particle mobility. We address these factors controlling the relative stability of redds and unspawned beds in flume experiments where redds were constructed with a technique that mimics the nesting behavior of female salmon. Although redds exhibited relatively coarse surfaces, measured entrainment forces indicate particle loosening by spawning lowered grain resistance to motion by 12-37% on average compared to unspawned beds. In addition, for the same discharges, boundary shear stress was 13-41% higher on a redd due to flow convergence on the tailspill. Visual measurements of particle entrainment further indicated redd instability, as bed average shear stress was 22% lower at incipient motion and 29% lower at the discharge that mobilized all grain sizes on a redd. Overall, results demonstrate redds are unstable compared to unspawned beds, which increases the risk of scour for buried eggs, but may facilitate fine-sediment flushing and improve the quality of spawning gravels for future generations of spawners. Therefore, managing salmon returns to increase streambed disturbance may be an effective tool for reducing sedimentation impacts on salmon reproduction. This article is protected by copyright. All rights reserved.

Regime shifts and panarchies in regional scale social-ecological water systems

In this article we summarize histories of nonlinear, complex interactions among societal, legal, and ecosystem dynamics in six North American water basins, as they respond to changing climate. These case studies were chosen to explore the conditions for emergence of adaptive governance in heavily regulated and developed social-ecological systems nested within a hierarchical governmental system. We summarize resilience assessments conducted in each system to provide a synthesis and reference by the other articles in this special feature. We also present a general framework used to evaluate the interactions between society and ecosystem regimes and the governance regimes chosen to mediate those interactions. The case studies show different ways that adaptive governance may be triggered, facilitated, or constrained by ecological and/or legal processes. The resilience assessments indicate that complex interactions among the governance and ecosystem components of these systems can produce different trajectories, which include patterns of (a) development and stabilization, (b) cycles of crisis and recovery, which includes lurches in adaptation and learning, and (3) periods of innovation, novelty, and transformation. Exploration of cross scale (Panarchy) interactions among levels and sectors of government and society illustrate that they may constrain development trajectories, but may also provide stability during crisis or innovation at smaller scales; create crises, but may also facilitate recovery; and constrain system transformation, but may also provide windows of opportunity in which transformation, and the resources to accomplish it, may occur. The framework is the starting point for our exploration of how law might play a role in enhancing the capacity of social-ecological systems to adapt to climate change.

Five ways to support interdisciplinary work before tenure

Academic institutions often claim to promote interdisciplinary teaching and research. Prescriptions for successfully engaging in interdisciplinary efforts, however, are usually directed at the individuals doing the work rather than the institutions evaluating them for the purpose of tenure and promotion. Where institutional recommendations do exist, they are often general in nature and lacking concrete guidance. Here, we draw on our experiences as students and faculty participating in three interdisciplinary water resource management programs in the USA to propose five practices that academic institutions can adopt to effectively support interdisciplinary work. We focus on reforms that will support pre-tenure faculty because we believe that an investment in interdisciplinary work early in one’s career is both particularly challenging and seldom rewarded. Recommended reforms include (1) creating metrics that reward interdisciplinary scholarship, (2) allowing faculty to “count” teaching and advising loads in interdisciplinary programs, (3) creating a “safe fail” for interdisciplinary research proposals and projects, (4) creating appropriate academic homes for interdisciplinary programs, and (5) rethinking “advancement of the discipline” as a basis for promotion and tenure.

Quantifying model uncertainty to improve watershed-level ecosystem service quantification: a global sensitivity analysis of the RUSLE

ABSTRACT Ecosystem service-support tools are commonly used to guide natural resource management. Often, empirically based models are preferred due to low data requirements, simplicity and clarity. Yet, uncertainty produced by local context or parameter estimation remains poorly quantified and documented. We assessed model uncertainty of the Revised Universal Soil Loss Equation – RUSLE developed mainly from US data. RUSLE is the most commonly applied model to assess watershed-level soil loss. We performed a global sensitivity analysis (GSA) on RUSLE with four dissimilar datasets to understand uncertainty and to provide recommendations for data collection and model parameterization. The datasets cover varying spatial levels (plot, watershed and continental) and environmental conditions (temperate and tropical). We found cover management and topography create the most uncertainty regardless of environmental conditions or data parameterization techniques. The importance of other RUSLE factors varies across contexts. We argue that model uncertainty could be reduced through better parameterization of cover management and topography factors while avoiding severe soil losses by targeting soil conservation practices in areas where both factors interact and enhance soil loss. We recommend incorporating GSA to assess empirical models’ uncertainty, to guide model parameterization and to target soil conservation efforts. EDITED BY Rob Alkemade

Degradation and dispersion limit environmental DNA detection of rare amphibians in wetlands: Increasing efficacy of sampling designs

The detection of rare macroorganisms using environmental DNA (eDNA) is a powerful new method for conservation and management; the efficacy of this method is affected by physiological, ecological, and hydrological processes. Understanding the processes limiting eDNA detection and accounting for those factors with optimized sampling designs is critical for realizing the potential of this tool. Amphibians are a focus of conservation programs globally and are often difficult to detect, presenting a challenge for effective action. To increase the ability of eDNA techniques to inform conservation and management programs, we investigated the eDNA detection of amphibians compared with field surveys for six species across a gradient of environmental factors expected to affect eDNA detection in three different systems: perennial wetlands, intermittent wetlands, and acidic intermittent wetlands. We applied a baseline sampling design in each wetland and used an occupancy modeling approach to evaluate evidence for processes limiting detection for each species given the presence of the target species. Evidence weights indicated that limiting processes varied across systems and included those associated with increased degradation (pH<5, temperature>25°C) and limited dispersion (wetland area>1200m2, sample volume<200mL). Optimized sampling protocols based on model results included an increased number of sampling locations in large and highly degradative (acidic) wetlands and increased filter pore size in high-particulate systems. These improved designs compensated for the previously limiting factors and yielded average detection rates of 0.62-0.86 per water sample. Degradation and dispersion processes appear to strongly influence the detection of amphibians in wetlands. Optimized, adaptive sampling designs can greatly increase the efficacy of eDNA monitoring approaches.