scholarly journals Towards a unified study of multiple stressors: divisions and common goals across research disciplines

2020 ◽  
Vol 287 (1926) ◽  
pp. 20200421 ◽  
Author(s):  
James A. Orr ◽  
Rolf D. Vinebrooke ◽  
Michelle C. Jackson ◽  
Kristy J. Kroeker ◽  
Rebecca L. Kordas ◽  
...  
Keyword(s):  
Environmental Changes ◽  
Multiple Stressors ◽  
Knowledge Exchange ◽  
Ecological Impacts ◽  
Temporal Complexity ◽  
Multiple Stressor ◽  
Interdisciplinary Knowledge ◽  
Shared Goal ◽  
Common Goals ◽  
Meta Analyses

Anthropogenic environmental changes, or ‘stressors’, increasingly threaten biodiversity and ecosystem functioning worldwide. Multiple-stressor research is a rapidly expanding field of science that seeks to understand and ultimately predict the interactions between stressors. Reviews and meta-analyses of the primary scientific literature have largely been specific to either freshwater, marine or terrestrial ecology, or ecotoxicology. In this cross-disciplinary study, we review the state of knowledge within and among these disciplines to highlight commonality and division in multiple-stressor research. Our review goes beyond a description of previous research by using quantitative bibliometric analysis to identify the division between disciplines and link previously disconnected research communities. Towards a unified research framework, we discuss the shared goal of increased realism through both ecological and temporal complexity, with the overarching aim of improving predictive power. In a rapidly changing world, advancing our understanding of the cumulative ecological impacts of multiple stressors is critical for biodiversity conservation and ecosystem management. Identifying and overcoming the barriers to interdisciplinary knowledge exchange is necessary in rising to this challenge. Division between ecosystem types and disciplines is largely a human creation. Species and stressors cross these borders and so should the scientists who study them.

scholarly journals Interactive effects of multiple stressors vary with consumer interactions, stressor dynamics and magnitude

2021 ◽  
Author(s):  
Mischa Turschwell ◽  
Roman Ashauer ◽  
Max Campbell ◽  
Rod Connolly ◽  
Sean Connolly ◽  
...  
Keyword(s):  
Multiple Stressors ◽  
Initial Conditions ◽  
Interactive Effects ◽  
Interaction Type ◽  
Multiple Stressor ◽  
Meta Analyses ◽  
Consumer Interactions ◽  
Underlying Processes ◽  
Process Based Models ◽  
Experiment Duration

Predicting the impacts of multiple stressors is important for informing ecosystem management, but is impeded by a lack of a general framework for predicting whether stressors interact synergistically, additively, or antagonistically. Here we use process-based models to study how interactions generalise across three levels of bio-logical organisation (physiological, population, and community) for a simulated two-stressor experiment on a seagrass model system. We found that the same underlying processes could result in synergistic, additive or antagonistic interactions, with interaction type depending on initial conditions, experiment duration, stressor dynamics, and consumer presence. Our results help explain why meta-analyses of multiple stressor experimental results have struggled to identify predictors of consistently non-additive interactions in the natural environment. Experiments run over longer temporal scales, with treatments across gradients of stressor magnitude, are needed to identify the processes that underpin how stressors interact and provide useful predictions to management.

Towards an interdisciplinary knowledge exchange model

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Santiago DE FRANCISCO ◽  
Diego MAZO
Keyword(s):  
Design Research ◽  
Knowledge Exchange ◽  
Academic Research ◽  
Innovation Process ◽  
The United States ◽  
Design Environment ◽  
Interdisciplinary Knowledge ◽  
One Year ◽  
The University ◽  
Common Situation

Universities and corporates, in Europe and the United States, have come to a win-win relationship to accomplish goals that serve research and industry. However, this is not a common situation in Latin America. Knowledge exchange and the co-creation of new projects by applying academic research to solve company problems does not happen naturally.To bridge this gap, the Design School of Universidad de los Andes, together with Avianca, are exploring new formats to understand the knowledge transfer impact in an open innovation network aiming to create fluid channels between different stakeholders. The primary goal was to help Avianca to strengthen their innovation department by apply design methodologies. First, allowing design students to proposed novel solutions for the traveller experience. Then, engaging Avianca employees to learn the design process. These explorations gave the opportunity to the university to apply design research and academic findings in a professional and commercial environment.After one year of collaboration and ten prototypes tested at the airport, we can say that Avianca’s innovation mindset has evolved by implementing a user-centric perspective in the customer experience touch points, building prototypes and quickly iterate. Furthermore, this partnership helped Avianca’s employees to experience a design environment in which they were actively interacting in the innovation process.

scholarly journals Participatory Mapping for Strengthening Environmental Governance on Socio-Ecological Impacts of Infrastructure in the Amazon: Lessons to Improve Tools and Strategies

2021 ◽  
Vol 13 (24) ◽  
pp. 14048
Author(s):  
Carla Mere-Roncal ◽  
Gabriel Cardoso Carrero ◽  
Andrea Birgit Chavez ◽  
Angelica Maria Almeyda Zambrano ◽  
Bette Loiselle ◽  
...  
Keyword(s):  
Large Scale ◽  
Environmental Governance ◽  
Oil And Gas ◽  
Good Governance ◽  
Knowledge Exchange ◽  
Ecological Impacts ◽  
Participatory Mapping ◽  
Infrastructure Development ◽  
Development Agenda ◽  
Novel Approach

The Amazon region has been viewed as a source of economic growth based on extractive industry and large-scale infrastructure development endeavors, such as roads, dams, oil and gas pipelines and mining. International and national policies advocating for the development of the Amazon often conflict with the environmental sector tasked with conserving its unique ecosystems and peoples through a sustainable development agenda. New practices of environmental governance can help mitigate adverse socio-economic and ecological effects. For example, forming a “community of practice and learning” (CoP-L) is an approach for improving governance via collaboration and knowledge exchange. The Governance and Infrastructure in the Amazon (GIA) project, in which this study is embedded, has proposed that fostering a CoP-L on tools and strategies to improve infrastructure governance can serve as a mechanism to promote learning and action on factors related to governance effectiveness. A particular tool used by the GIA project for generating and sharing knowledge has been participatory mapping (Pmap). This study analyzes Pmap exercises conducted through workshops in four different Amazonian regions. The goal of Pmap was to capture different perspectives from stakeholders based on their experiences and interests to visualize and reflect on (1) areas of value, (2) areas of concern and (3) recommended actions related to reducing impacts of infrastructure development and improvement of governance processes. We used a mixed-methods approach to explore textual analysis, regional multi-iteration discussion with stakeholders, participatory mapping and integration with ancillary geospatial datasets. We believe that by sharing local-knowledge-driven data and strengthening multi-actor dialogue and collaboration, this novel approach can improve day to day practices of CoP-L members and, therefore, the transparency of infrastructure planning and good governance.

Studying the Human Health and Ecological Impacts of the Deep Water Horizon Oil Spill Disaster: Introduction to This Special Issue of New Solutions

2018 ◽  
Vol 28 (3) ◽  
pp. 410-415 ◽  
Author(s):  
Sharon Croisant ◽  
John Sullivan
Keyword(s):  
Oil Spill ◽  
Deep Water ◽  
Disaster Preparedness ◽  
Disaster Response ◽  
Gulf Coast ◽  
Multiple Stressors ◽  
Ecological Impacts ◽  
Health Impacts ◽  
Special Issue ◽  
Research Partnerships

Gulf Coast Health Alliance: Health Risks Related to the Macondo Spill (GC-HARMS) began in 2011 as a component project of the National Institute of Environmental Health Sciences’ (NIEHS) Deep Water Horizon (DWH) Research Consortia program. This Gulf-wide consortium created regional community-university research partnerships focused on addressing health impacts resulting from oil spill exposures. Findings from this trans-National Institutes of Health program have helped enhance and refine community disaster preparedness and reinforced local–regional disaster response networks. Focal points of individual projects included the following: effects of multiple stressors on individuals and vulnerable populations, exposure to contaminants associated with crude oil, and mental health impacts. This introduction to New Solutions Special Issue on the GC-HARMS response to the DWH disaster presents an overview of the project’s internal structure and relationship to the comprehensive NIEHS consortia response and lists articles and interviews featured currently with brief mention of additional articles slated for the next issue.

Agent-Based System for Discovering and Building Collaborative Communities

2006 ◽  
pp. 12-17 ◽  
Author(s):  
Olga Nabuco ◽  
Mauro F. Koyama ◽  
Edeneziano D. Pereira ◽  
Khalil Drira
Keyword(s):  
Tacit Knowledge ◽  
Knowledge Exchange ◽  
Innovation Process ◽  
Work Force ◽  
Mutual Understanding ◽  
Community Members ◽  
Agent Based ◽  
Collaborative Practices ◽  
Collaborative Community ◽  
Common Goals

Currently, organizations are under a regime of rapid economic, social, and technological change. Such a regime has been impelling organizations to increase focus on innovation, learning, and forms of enterprise cooperation. To assure innovation success and make it measurable, it is indispensable for members of teams to systematically exchange information and knowledge. McLure and Faraj (2000) see an evolution in the way knowledge exchange is viewed from “knowledge as object” to “knowledge embedded in people,” and finally as “knowledge embedded in the community.” The collaborative community is a group of people, not necessarily co-located, that share interests and act together to contribute positively toward the fulfillment of their common goals. The community’s members develop a common vocabulary and language by interacting continuously. They also create the reciprocal trust and mutual understanding needed to establish a culture in which collaborative practices pre-dominate. Such practices can grasp and apply the tacit knowledge dispersed in the organization, embodied in the people’s minds. Tacit knowledge is a concept proposed by Polanyi (1966) meaning a kind of knowledge that cannot be easily transcripted into a code. It can be profitably applied on process and/or product development and production. Therefore, community members can powerfully contribute to the innovation process and create value for the organization. In doing so, they become a fundamental work force to the organization.

scholarly journals Seasonal stress drives predictable changes in inbreeding depression in field-tested captive populations of Drosophila melanogaster

2012 ◽  
Vol 279 (1743) ◽  
pp. 3756-3764 ◽  
Author(s):  
Laramy S. Enders ◽  
Leonard Nunney
Keyword(s):  
Drosophila Melanogaster ◽  
Linear Relationship ◽  
Inbreeding Depression ◽  
Stress Level ◽  
Multiple Stressors ◽  
Laboratory Data ◽  
Natural Environments ◽  
Captive Populations ◽  
Primary Determinant ◽  
Meta Analyses

Recent meta-analyses conducted across a broad range of taxa have demonstrated a strong linear relationship between the change in magnitude of inbreeding depression under stress and stress level, measured as fitness loss in outbred individuals. This suggests that a general underlying response may link stress and inbreeding depression. However, this relationship is based primarily on laboratory data, and it is unknown whether natural environments with multiple stressors and fluctuating stress levels alter how stress affects inbreeding depression. To test whether the same pattern persists in the field, we investigated the effect of seasonal variation on stress level and inbreeding depression in a 3-year field study measuring the productivity of captive populations of inbred and outbred Drosophila melanogaster . We found cold winter temperatures were most stressful and induced the greatest inbreeding depression. Furthermore, these data, collected under natural field conditions, conformed to the same predictive linear relationship seen in Drosophila laboratory studies, with inbreeding depression increasing by 0.17 lethal equivalents for every 10 per cent increase in stress level. Our results suggest that under natural conditions stress level is a primary determinant of the magnitude of inbreeding depression and should be considered when assessing extinction vulnerability in small populations.

Meta-analysis of the impacts of water management on aquatic communities

2008 ◽  
Vol 65 (3) ◽  
pp. 437-447 ◽  
Author(s):  
Tim J Haxton ◽  
C Scott Findlay
Keyword(s):  
Water Management ◽  
Confidence Intervals ◽  
Effect Size ◽  
Meta Analysis ◽  
Ecological Impacts ◽  
Aquatic Communities ◽  
Flow Modification ◽  
Meta Analyses ◽  
Fail Safe ◽  
Habitat Generalists

Systematic meta-analyses were conducted on the ecological impacts of water management, including effects of (i) dewatering on macroinvertebrates, (ii) a hypolimnetic release on downstream aquatic fish and macro invertebrate communities, and (iii) flow modification on fluvial and habitat generalists. Our meta-analysis indicates, in general, that (i) macroinvertebrate abundance is lower in zones or areas that have been dewatered as a result of water fluctuations or low flows (overall effect size, –1.64; 95% confidence intervals (CIs), –2.51, –0.77), (ii) hypolimnetic draws are associated with reduced abundance of aquatic (fish and macroinvertebrates) communities (overall effect size, –0.84; 95% CIs, –1.38, –0.33) and macroinvertebrates (overall effect size, –0.73; 95% CIs, –1.24, –0.22) downstream of a dam, and (iii) altered flows are associated with reduced abundance of fluvial specialists (–0.42; 95% CIs, –0.81, –0.02) but not habitat generalists (overall effect size, –0.14; 95% CIs, –0.61, 0.32). Publication bias is evident in several of the meta-analyses; however, multiple experiments from a single study may be contributing to this bias. Fail-safe Ns suggest that many (>100) studies showing positive or no effects of water management on the selected endpoints would be required to qualitatively change the results of the meta-analysis, which in turn suggests that the conclusions are reasonably robust.

scholarly journals ExMarine: An experimental field mesocosm system to study multiple-stressor impacts on rock pool biodiversity

2016 ◽  
Author(s):  
Jan Niklas Macher
Keyword(s):  
Multiple Stressors ◽  
Anthropogenic Activities ◽  
Block Design ◽  
High Tide ◽  
Biodiversity Loss ◽  
Rock Pool ◽  
Experimental Field ◽  
Multiple Stressor ◽  
Urgent Task ◽  
Field Mesocosm

Biodiversity loss due to increasing anthropogenic activities is one of the biggest threats to humanity. Understanding the impacts of multiple-stressors on ecosystems and biodiversity is therefore an urgent task. Shore ecosystems are especially valuable, as they harbour a high biodiversity and provide important ecosystems services. Until now, experimental approaches addressing multiple-stressor impacts on these ecosystems have been rare and mostly run with a limited number of replicates and under non-natural conditions. Here, an experimental field mesocosm system that allows studying multiple-stressor impacts on rock pool biodiversity is proposed. The ExMarine mesocosm system is composed of 64 experimental rock pool mesocosms in a fully randomised block design, which allows studying multiple-stressor impacts under highly standardised conditions. Water is taken directly from the sea, allowing biota to immigrate and emigrate freely. Water flow into the mesocosms can be regulated and it is possible to simulate disturbance through waves during high tide. The system can help to understand the impacts of multiple stressors on biodiversity, to monitor ecosystem health and to plan measures preventing the further loss of biodiversity.

scholarly journals Multiple stressor null models frequently fail to detect most interactions due to low statistical power

2021 ◽  
Author(s):  
Benjamin J Burgess ◽  
Michelle C Jackson ◽  
David J Murrell
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Multiple Stressors ◽  
Statistical Significance ◽  
Null Model ◽  
Null Models ◽  
Data Uncertainty ◽  
Sample Sizes ◽  
Multiple Stressor ◽  
Null Expectation

1. Most ecosystems are subject to co-occurring, anthropogenically driven changes and understanding how these multiple stressors interact is a pressing concern. Stressor interactions are typically studied using null models, with the additive and multiplicative null expectation being those most widely applied. Such approaches classify interactions as being synergistic, antagonistic, reversal, or indistinguishable from the null expectation. Despite their wide-spread use, there has been no thorough analysis of these null models, nor a systematic test of the robustness of their results to sample size or sampling error in the estimates of the responses to stressors. 2. We use data simulated from food web models where the true stressor interactions are known, and analytical results based on the null model equations to uncover how (i) sample size, (ii) variation in biological responses to the stressors and (iii) statistical significance, affect the ability to detect non-null interactions. 3. Our analyses lead to three main results. Firstly, it is clear the additive and multiplicative null models are not directly comparable, and over one third of all simulated interactions had classifications that were model dependent. Secondly, both null models have weak power to correctly classify interactions at commonly implemented sample sizes (i.e., ≤6 replicates), unless data uncertainty is unrealistically low. This means all but the most extreme interactions are indistinguishable from the null model expectation. Thirdly, we show that increasing sample size increases the power to detect the true interactions but only very slowly. However, the biggest gains come from increasing replicates from 3 up to 25 and we provide an R function for users to determine sample sizes required to detect a critical effect size of biological interest for the additive model. 4. Our results will aid researchers in the design of their experiments and the subsequent interpretation of results. We find no clear statistical advantage of using one null model over the other and argue null model choice should be based on biological relevance rather than statistical properties. However, there is a pressing need to increase experiment sample sizes otherwise many biologically important synergistic and antagonistic stressor interactions will continue to be missed.

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