Knowledge sharing for shared success in the decade on ecosystem restoration

Research output: Journal contributionsJournal articlesResearchpeer-review

Standard

Knowledge sharing for shared success in the decade on ecosystem restoration. / Ladouceur, Emma; Shackelford, Nancy; Bouazza, Karma et al.
In: Ecological Solutions and Evidence, Vol. 3, No. 1, e12117, 01.01.2022.

Research output: Journal contributionsJournal articlesResearchpeer-review

Harvard

Ladouceur, E, Shackelford, N, Bouazza, K, Brudvig, L, Bucharova, A, Conradi, T, Erickson, TE, Garbowski, M, Garvy, K, Harpole, WS, Jones, HP, Knight, TM, Nsikani, MM, Paterno, G, Suding, K, Temperton, V, Török, P, Winkler, DE & Chase, JM 2022, 'Knowledge sharing for shared success in the decade on ecosystem restoration', Ecological Solutions and Evidence, vol. 3, no. 1, e12117. https://doi.org/10.1002/2688-8319.12117

APA

Ladouceur, E., Shackelford, N., Bouazza, K., Brudvig, L., Bucharova, A., Conradi, T., Erickson, T. E., Garbowski, M., Garvy, K., Harpole, W. S., Jones, H. P., Knight, T. M., Nsikani, M. M., Paterno, G., Suding, K., Temperton, V., Török, P., Winkler, D. E., & Chase, J. M. (2022). Knowledge sharing for shared success in the decade on ecosystem restoration. Ecological Solutions and Evidence, 3(1), Article e12117. https://doi.org/10.1002/2688-8319.12117

Vancouver

Ladouceur E, Shackelford N, Bouazza K, Brudvig L, Bucharova A, Conradi T et al. Knowledge sharing for shared success in the decade on ecosystem restoration. Ecological Solutions and Evidence. 2022 Jan 1;3(1):e12117. doi: 10.1002/2688-8319.12117

Bibtex

@article{5a497265de6843b6997f18f6911335a6,
title = "Knowledge sharing for shared success in the decade on ecosystem restoration",
abstract = "The Decade on Ecosystem Restoration aims to provide the means and incentives for upscaling restoration efforts worldwide. Although ecosystem restoration is a broad, interdisciplinary concept, effective ecological restoration requires sound ecological knowledge to successfully restore biodiversity and ecosystem services in degraded landscapes. We emphasize the critical role of knowledge and data sharing to inform synthesis for the most robust restoration science possible. Such synthesis is critical for helping restoration ecologists better understand how context affects restoration outcomes, and to increase predictive capacity of restoration actions. This predictive capacity can help to provide better information for evidence-based decision-making, and scale-up approaches to meet ambitious targets for restoration. We advocate for a concerted effort to collate species-level, fine-scale, ecological community data from restoration studies across a wide range of environmental and ecological gradients. Well-articulated associated metadata relevant to experience and social or landscape contexts can further be used to explain outcomes. These data could be carefully curated and made openly available to the restoration community to help to maximize evidence-based knowledge sharing, enable flexible re-use of existing data and support predictive capacity in ecological community responses to restoration actions. We detail how integrated data, analysis and knowledge sharing via synthesis can support shared success in restoration ecology by identifying successful and unsuccessful outcomes across diverse systems and scales. We also discuss potential interdisciplinary solutions and approaches to overcome challenges associated with bringing together subfields of restoration practice. Sharing this knowledge and data openly can directly inform actions and help to improve outcomes for the Decade on Ecosystem Restoration.",
keywords = "Ecosystems Research, data synthesis, dissemination, ecological restoration, evidence-based knowledge, networks, open data, practitioner-scientist collaboration, restoration ecology, data synthesis, dissemination, ecological restoration, evidence-based knowledge, networks, open data, practitioner–scientist collaboration, restoration ecology",
author = "Emma Ladouceur and Nancy Shackelford and Karma Bouazza and Lars Brudvig and Anna Bucharova and Timo Conradi and Erickson, {Todd E.} and Magda Garbowski and Kelly Garvy and Harpole, {W. Stanley} and Jones, {Holly P.} and Knight, {Tiffany M.} and Nsikani, {Mlungele M.} and Gustavo Paterno and Katharine Suding and Vicky Temperton and P{\'e}ter T{\"o}r{\"o}k and Winkler, {Daniel E.} and Chase, {Jonathan M.}",
note = "Publisher Copyright: {\textcopyright} 2022 The Authors. Ecological Solutions and Evidence published by John Wiley & Sons Ltd on behalf of British Ecological Society.",
year = "2022",
month = jan,
day = "1",
doi = "10.1002/2688-8319.12117",
language = "English",
volume = "3",
journal = "Ecological Solutions and Evidence",
issn = "2688-8319",
publisher = "John Wiley & Sons Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Knowledge sharing for shared success in the decade on ecosystem restoration

AU - Ladouceur, Emma

AU - Shackelford, Nancy

AU - Bouazza, Karma

AU - Brudvig, Lars

AU - Bucharova, Anna

AU - Conradi, Timo

AU - Erickson, Todd E.

AU - Garbowski, Magda

AU - Garvy, Kelly

AU - Harpole, W. Stanley

AU - Jones, Holly P.

AU - Knight, Tiffany M.

AU - Nsikani, Mlungele M.

AU - Paterno, Gustavo

AU - Suding, Katharine

AU - Temperton, Vicky

AU - Török, Péter

AU - Winkler, Daniel E.

AU - Chase, Jonathan M.

N1 - Publisher Copyright: © 2022 The Authors. Ecological Solutions and Evidence published by John Wiley & Sons Ltd on behalf of British Ecological Society.

PY - 2022/1/1

Y1 - 2022/1/1

N2 - The Decade on Ecosystem Restoration aims to provide the means and incentives for upscaling restoration efforts worldwide. Although ecosystem restoration is a broad, interdisciplinary concept, effective ecological restoration requires sound ecological knowledge to successfully restore biodiversity and ecosystem services in degraded landscapes. We emphasize the critical role of knowledge and data sharing to inform synthesis for the most robust restoration science possible. Such synthesis is critical for helping restoration ecologists better understand how context affects restoration outcomes, and to increase predictive capacity of restoration actions. This predictive capacity can help to provide better information for evidence-based decision-making, and scale-up approaches to meet ambitious targets for restoration. We advocate for a concerted effort to collate species-level, fine-scale, ecological community data from restoration studies across a wide range of environmental and ecological gradients. Well-articulated associated metadata relevant to experience and social or landscape contexts can further be used to explain outcomes. These data could be carefully curated and made openly available to the restoration community to help to maximize evidence-based knowledge sharing, enable flexible re-use of existing data and support predictive capacity in ecological community responses to restoration actions. We detail how integrated data, analysis and knowledge sharing via synthesis can support shared success in restoration ecology by identifying successful and unsuccessful outcomes across diverse systems and scales. We also discuss potential interdisciplinary solutions and approaches to overcome challenges associated with bringing together subfields of restoration practice. Sharing this knowledge and data openly can directly inform actions and help to improve outcomes for the Decade on Ecosystem Restoration.

AB - The Decade on Ecosystem Restoration aims to provide the means and incentives for upscaling restoration efforts worldwide. Although ecosystem restoration is a broad, interdisciplinary concept, effective ecological restoration requires sound ecological knowledge to successfully restore biodiversity and ecosystem services in degraded landscapes. We emphasize the critical role of knowledge and data sharing to inform synthesis for the most robust restoration science possible. Such synthesis is critical for helping restoration ecologists better understand how context affects restoration outcomes, and to increase predictive capacity of restoration actions. This predictive capacity can help to provide better information for evidence-based decision-making, and scale-up approaches to meet ambitious targets for restoration. We advocate for a concerted effort to collate species-level, fine-scale, ecological community data from restoration studies across a wide range of environmental and ecological gradients. Well-articulated associated metadata relevant to experience and social or landscape contexts can further be used to explain outcomes. These data could be carefully curated and made openly available to the restoration community to help to maximize evidence-based knowledge sharing, enable flexible re-use of existing data and support predictive capacity in ecological community responses to restoration actions. We detail how integrated data, analysis and knowledge sharing via synthesis can support shared success in restoration ecology by identifying successful and unsuccessful outcomes across diverse systems and scales. We also discuss potential interdisciplinary solutions and approaches to overcome challenges associated with bringing together subfields of restoration practice. Sharing this knowledge and data openly can directly inform actions and help to improve outcomes for the Decade on Ecosystem Restoration.

KW - Ecosystems Research

KW - data synthesis

KW - dissemination

KW - ecological restoration

KW - evidence-based knowledge

KW - networks

KW - open data

KW - practitioner-scientist collaboration

KW - restoration ecology

KW - data synthesis

KW - dissemination

KW - ecological restoration

KW - evidence-based knowledge

KW - networks

KW - open data

KW - practitioner–scientist collaboration

KW - restoration ecology

UR - https://www.mendeley.com/catalogue/977f3364-7499-3051-85b7-aed3ce9e1ae5/

UR - http://www.scopus.com/inward/record.url?scp=85129923473&partnerID=8YFLogxK

U2 - 10.1002/2688-8319.12117

DO - 10.1002/2688-8319.12117

M3 - Journal articles

VL - 3

JO - Ecological Solutions and Evidence

JF - Ecological Solutions and Evidence

SN - 2688-8319

IS - 1

M1 - e12117

ER -

DOI