doi: 10.1016/j.biocon.2021.109175. Epub 2021 May 20.
Global COVID-19 lockdown highlights humans as both threats and custodians of the environment
Amanda E Bates 1, Richard B Primack 2, Brandy S Biggar 1, Tomas J Bird 3, Mary E Clinton 1, Rylan J Command 4, Cerren Richards 1, Marc Shellard 5, Nathan R Geraldi 5, Valeria Vergara 6, Orlando Acevedo-Charry 7, Zuania Colón-Piñeiro 8, David Ocampo 7, Natalia Ocampo-Peñuela 9, Lina M Sánchez-Clavijo 9, Cristian M Adamescu 10, Sorin Cheval 11, Tudor Racoviceanu 10, Matthew D Adams 12, Egide Kalisa 12, Vincent Z Kuuire 12, Vikram Aditya 13, Pia Anderwald 14, Samuel Wiesmann 14, Sonja Wipf 14, Gal Badihi 15, Matthew G Henderson 15, Hanspeter Loetscher 16, Katja Baerenfaller 17, Lisandro Benedetti-Cecchi 18, Fabio Bulleri 18, Iacopo Bertocci 18, Elena Maggi 18, Luca Rindi 18, Chiara Ravaglioli 18, Kristina Boerder 19, Julien Bonnel 20, Delphine Mathias 21, Philippe Archambault 22, Laurent Chauvaud 23, Camrin D Braun 24, Simon R Thorrold 24, Jacob W Brownscombe 25, Jonathan D Midwood 25, Christine M Boston 25, Jill L Brooks 26, Steven J Cooke 26, Victor China 27, Uri Roll 27, Jonathan Belmaker 28 29, Assaf Zvuloni 30, Marta Coll 31, Miquel Ortega 32, Brendan Connors 33, Lisa Lacko 33, Dinusha R M Jayathilake 34, Mark J Costello 35, Theresa M Crimmins 36, LoriAnne Barnett 36, Ellen G Denny 36, Katharine L Gerst 36, R L Marsh 36, Erin E Posthumus 36, Reilly Rodriguez 36, Alyssa Rosemartin 36, Sara N Schaffer 36, Jeff R Switzer 36, Kevin Wong 36, Susan J Cunningham 37, Petra Sumasgutner 38, Arjun Amar 37, Robert L Thomson 37, Miqkayla Stofberg 37, Sally Hofmeyr 37, Jessleena Suri 37, Rick D Stuart-Smith 39, Paul B Day 40, Graham J Edgar 39, Antonia T Cooper 39, Fabio Cabrera De Leo 41 42, Grant Garner 42, Paulson G Des Brisay 43, Michael B Schrimpf 44, Nicola Koper 44, Michael S Diamond 45, Ross G Dwyer 46, Cameron J Baker 47, Craig E Franklin 47, Ron Efrat 27, Oded Berger-Tal 27, Ohad Hatzofe 48, Víctor M Eguíluz 49, Jorge P Rodríguez 50, Juan Fernández-Gracia 49, David Elustondo 51, Vicent Calatayud 52, Philina A English 53, Stephanie K Archer 54, Sarah E Dudas 53, Dana R Haggarty 53, Austin J Gallagher 55, Brendan D Shea 55, Oliver N Shipley 55, Ben L Gilby 46, Jasmine Ballantyne 46, Andrew D Olds 46, Christopher J Henderson 46, Thomas A Schlacher 46, William D Halliday 56, Nicholas A W Brown 42, Mackenzie B Woods 42, Sigal Balshine 57, Francis Juanes 42, Mitchell J Rider 58, Patricia S Albano 58, Neil Hammerschlag 58, Graeme C Hays 59, Nicole Esteban 60, Yuhang Pan 61, Guojun He 62, Takanao Tanaka 61, Marc J S Hensel 63, Robert J Orth 63, Christopher J Patrick 63, Jonas Hentati-Sundberg 64, Olof Olsson 65, Margot L Hessing-Lewis 66, Nicholas D Higgs 67, Mark A Hindell 68, Clive R McMahon 69, Rob Harcourt 70, Christophe Guinet 71, Sarah E Hirsch 72, Justin R Perrault 72, Shelby R Hoover 72, Jennifer D Reilly 72, Catherine Hobaiter 15, Thibaud Gruber 73, Charlie Huveneers 74, Vinay Udyawer 75, Thomas M Clarke 74, Laura P Kroesen 76, David S Hik 76, Seth G Cherry 77, Justin A Del Bel Belluz 78, Jennifer M Jackson 78, Shengjie Lai 79, Clayton T Lamb 80, Gregory D LeClair 81, Jeffrey R Parmelee 82, Matthew W H Chatfield 83, Cheryl A Frederick 83, Sangdon Lee 84, Hyomin Park 84, Jaein Choi 84, Frédéric LeTourneux 85, Thierry Grandmont 85, Frédéric Dulude de-Broin 85, Joël Bêty 86, Gilles Gauthier 85, Pierre Legagneux 85 71, Jesse S Lewis 87, Jeffrey Haight 88, Zhu Liu 89, Jarod P Lyon 90, Robin Hale 90, Dallas D'Silva 91, Ian MacGregor-Fors 92, Enrique Arbeláez-Cortés 93, Felipe A Estela 94, Camilo E Sánchez-Sarria 94, Michelle García-Arroyo 92, Giann K Aguirre-Samboní 94, Juan C Franco Morales 95, Shahar Malamud 28, Tal Gavriel 28, Yehezkel Buba 28, Shira Salingré 28, Mai Lazarus 28, Ruthy Yahel 96, Yigael Ben Ari 96, Eyal Miller 96, Rotem Sade 96, Guy Lavian 96, Ziv Birman 96, Manor Gury 96, Harel Baz 96, Ilia Baskin 96, Alon Penn 96, Amit Dolev 96, Ogen Licht 96, Tabi Karkom 96, Sharon Davidzon 96, Avi Berkovitch 96, Ofer Yaakov 96, Raoul Manenti 97, Emiliano Mori 98, Gentile Francesco Ficetola 97, Enrico Lunghi 99, David March 100, Brendan J Godley 100, Cecilia Martin 5, Steven F Mihaly 101, David R Barclay 102, Dugald J M Thomson 102, Richard Dewey 101, Jeannette Bedard 101, Aroha Miller 6, Amber Dearden 6, Jennifer Chapman 6, Lauren Dares 6, Laura Borden 6, Donna Gibbs 6, Jessica Schultz 6, Nikita Sergeenko 6, Fiona Francis 6, Amanda Weltman 6, Nicolas Moity 103, Jorge Ramírez-González 103, Gonzalo Mucientes 104, Alexandre Alonso-Fernández 104, Itai Namir 28, Avi Bar-Massada 105, Ron Chen 106, Shmulik Yedvab 107, Thomas A Okey 108, Steffen Oppel 109, Volen Arkumarev 110, Samuel Bakari 111, Vladimir Dobrev 110, Victoria Saravia-Mullin 112, Anastasios Bounas 112, Dobromir Dobrev 110, Elzbieta Kret 113, Solomon Mengistu 114, Cloé Pourchier 115, Alazar Ruffo 116, Million Tesfaye 117, Mengistu Wondafrash 114, Stoyan C Nikolov 110, Charles Palmer 118, Lorenzo Sileci 118, Patrick T Rex 119, Christopher G Lowe 119, Francesc Peters 120, Matthew K Pine 121, Craig A Radford 122, Louise Wilson 122, Lauren McWhinnie 123, Alessia Scuderi 124, Andrew G Jeffs 122, Kathleen L Prudic 125, Maxim Larrivée 126, Kent P McFarland 127, Rodrigo Solis 128, Rebecca A Hutchinson 129, Nuno Queiroz 130, Miguel A Furtado 130, David W Sims 131, Emily Southall 131, Claudio A Quesada-Rodriguez 132, Jessica P Diaz-Orozco 132, Ku'ulei S Rodgers 133, Sarah J L Severino 133, Andrew T Graham 133, Matthew P Stefanak 133, Elizabeth M P Madin 133, Peter G Ryan 37, Kyle Maclean 37, Eleanor A Weideman 37, Çağan H Şekercioğlu 134, Kyle D Kittelberger 134, Josip Kusak 135, Jeffrey A Seminoff 136, Megan E Hanna 137, Takahiro Shimada 138, Mark G Meekan 139, Martin K S Smith 140, Mohlamatsane M Mokhatla 140, Malcolm C K Soh 141, Roanna Y T Pang 141, Breyl X K Ng 141, Benjamin P Y-H Lee 141, Adrian H B Loo 141, Kenneth B H Er 141, Gabriel B G Souza 142, Christopher D Stallings 143, Joseph S Curtis 143, Meaghan E Faletti 143, Jonathan A Peake 143, Michael J Schram 143, Kara R Wall 143, Carina Terry 2, Matt Rothendler 2, Lucy Zipf 2, Juan Sebastián Ulloa 9, Angélica Hernández-Palma 9, Bibiana Gómez-Valencia 9, Cristian Cruz-Rodríguez 9, Yenifer Herrera-Varón 9, Margarita Roa 9, Susana Rodríguez-Buriticá 9, Jose Manuel Ochoa-Quintero 9, Reut Vardi 144, Víctor Vázquez 145, Christian Requena-Mesa 146, Miyako H Warrington 147, Michelle E Taylor 148, Lucy C Woodall 149, Paris V Stefanoudis 149, Xiangliang Zhang 150, Qiang Yang 150, Yuval Zukerman 27, Zehava Sigal 48, Amir Ayali 151, Eric E G Clua 152, Pamela Carzon 152, Clementine Seguine 152, Andrea Corradini 153, Luca Pedrotti 154, Catherine M Foley 133, Catherine Alexandra Gagnon 85, Elijah Panipakoochoo 155, Celene B Milanes 156, Camilo M Botero 157, Yunior R Velázquez 158, Nataliya A Milchakova 159, Simon A Morley 160, Stephanie M Martin 161, Veronica Nanni 162, Tanya Otero 163, Julia Wakeling 163, Sarah Abarro 164, Cyril Piou 165, Ana F L Sobral 166, Eulogio H Soto 167, Emily G Weigel 168, Alejandro Bernal-Ibáñez 169, Ignacio Gestoso 169, Eva Cacabelos 169, Francesca Cagnacci 170, Reny P Devassy 171, Matthias-Claudio Loretto 172, Paula Moraga 173, Christian Rutz 174, Carlos M Duarte 5
Affiliations
- PMID:34035536
- PMCID: PMC8135229
- DOI: 10.1016/j.biocon.2021.109175
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Global COVID-19 lockdown highlights humans as both threats and custodians of the environment
Amanda E Bates et al. Biol Conserv.2021 Nov.
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Abstract
The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus, initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness.
Keywords: Biodiversity; Global monitoring; Pandemic; Restoration.
© 2021 Published by Elsevier Ltd.
Conflict of interest statement
Authors declare no competing interests.
Figures
Total humans under COVID-19 mobility restrictions. Time series of the number of humans under lockdown across the global population under the 2020 COVID-19 mitigation policies. This assumes that in countries with targeted restrictions, a fraction of 20% of the population was under lockdown. Assuming different fractions, similar time patterns but different magnitudes of populations under lockdown are obtained. For example, assuming fractions of 20% and 30%, April 5th was the day with the maximum population under lockdown equal to 57% and 61% of the global population, respectively. Assuming fractions of 5% and 10%, April 26th was the day with the maximum population under lockdown equal to 53% and 54% of the population, respectively.
Change in mobility. Percent change in time spent within home residences (residential) following implementation of confinement measures in each country.
Reports of 275 species that occupied an unusual area (distribution change), or shifted in number (abundance change) were attributed to a reduction in human activities. Changes in species distributions were observed around the world as qualitative observations (Appendix 3, Table A3, albeit with biases in effort such as greater coverage in the Northern Hemisphere and South Africa), and based on empirical data of time series surveys and bio-logging data using statistical modeling to quantify change. Only changes that were attributed to the lockdown with high confidence are included here (Appendix 4, Table A4). Bubble size represents data density (the largest bubble represents 41–60 observations and the smallest is 1–20). Reports of 275 species that occupied an unusual area (distribution change), or shifted in number (abundance change) were attributed to a reduction in human activities. Changes in species distributions were observed around the world as qualitative observations (Appendix 3, Table A3, albeit with biases in effort such as greater coverage in the Northern Hemisphere and South Africa), and based on empirical data of time series surveys and bio-logging data using statistical modeling to quantify change. Only changes that were attributed to the lockdown with high confidence are included here (Appendix 4, Table A4). Bubble size represents data density (the largest bubble represents 41–60 observations and the smallest is 1–20).
Qualitative negative and positive effects observed which were relative to the response observed (Appendix 4, Table A4). Negative effects indicate a dampening in the responses which were grouped into categories representing “Human Mobility & Activities”, Biodiversity Threats”, “Wildlife Responses” and “Social Systems & Structures”, while positive effects indicate an increase. The effect score is based on the criteria outlined in Appendix 1, Table A2, and considered the duration, spatial extent and total impact of the effect on the response. A negative or positive effect direction is relative to each category is based on the observed effect, rather than an interpreted impact. For instance, a negative effect on noise is a decrease in noise (which may have had positive wildlife impacts). a) Distribution of effects showing the direction and magnitude. The dotted line is the intercept, and the colored line indicates the median effect score. b) The mean effect score for categories falling within effects on human activities (blue), biodiversity threats (orange), biodiversity (green) and social systems (purple). Bars are the mean across reports pooled for positive and negative effects on the y-axis category, and white numbers are the number of observations upon which the mean is based. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Qualitative negative and positive effects observed which were relative to the response observed (Appendix 4, Table A4). Negative effects indicate a dampening in the responses which were grouped into categories representing “Human Mobility & Activities”, Biodiversity Threats”, “Wildlife Responses” and “Social Systems & Structures”, while positive effects indicate an increase. The effect score is based on the criteria outlined in Appendix 1, Table A2, and considered the duration, spatial extent and total impact of the effect on the response. A negative or positive effect direction is relative to each category is based on the observed effect, rather than an interpreted impact. For instance, a negative effect on noise is a decrease in noise (which may have had positive wildlife impacts). a) Distribution of effects showing the direction and magnitude. The dotted line is the intercept, and the colored line indicates the median effect score. b) The mean effect score for categories falling within effects on human activities (blue), biodiversity threats (orange), biodiversity (green) and social systems (purple). Bars are the mean across reports pooled for positive and negative effects on the y-axis category, and white numbers are the number of observations upon which the mean is based. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Responses during the lockdown based on our empirical data (Appendix 5, Table A5) where positive and negative effects represent the observed direction of change for the different response categories. 71 studies that attributed the observed effect to the lockdown with high confidence are included (i.e., a qualitative confidence score of 3 or greater out of a maximum of 5). Frequency histograms (panels a-d) show bars representing data density and a curve representing a smoothed distribution of effect sizes and direction. The dotted line is zero, and the solid colored line is the median. Only responses that were attributed to the lockdown with high confidence are included. a) Human activities and mobility (blue) includes measured responses in human activities and mobility, such as related to commuting and recreational activities (categories are described in Appendix 1, Table A1). b) Biodiversity threats (orange) include categories that harm wildlife and natural systems, such as hunting, fishing, mining, vehicle strikes, wildlife trade, environmental pollution, and deforestation. c) Wildlife responses (green) incorporate observations of animals and plants related to performance (e.g., reproduction, health, foraging) and habitat use (abundance and distribution) and community change (species richness). d) Social systems and structures (purple) include environmental monitoring, restoration, conservation, and enforcement. The chord diagrams highlighted the observed positive and negative effects which were attributed to different lockdown-related drivers as identified by each study (black), and linked to what was measured by each study where responses were grouped into: human activities and mobility, biodiversity threats, wildlife responses, and social systems and structures. One chord represents one measured response. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Responses during the lockdown based on our empirical data (Appendix 5, Table A5) where positive and negative effects represent the observed direction of change for the different response categories. 71 studies that attributed the observed effect to the lockdown with high confidence are included (i.e., a qualitative confidence score of 3 or greater out of a maximum of 5). Frequency histograms (panels a-d) show bars representing data density and a curve representing a smoothed distribution of effect sizes and direction. The dotted line is zero, and the solid colored line is the median. Only responses that were attributed to the lockdown with high confidence are included. a) Human activities and mobility (blue) includes measured responses in human activities and mobility, such as related to commuting and recreational activities (categories are described in Appendix 1, Table A1). b) Biodiversity threats (orange) include categories that harm wildlife and natural systems, such as hunting, fishing, mining, vehicle strikes, wildlife trade, environmental pollution, and deforestation. c) Wildlife responses (green) incorporate observations of animals and plants related to performance (e.g., reproduction, health, foraging) and habitat use (abundance and distribution) and community change (species richness). d) Social systems (purple) include environmental monitoring, restoration, conservation, and enforcement. The chord diagrams highlighted the observed positive and negative effects which were attributed to different lockdown-related drivers as identified by each study (black), and linked to what was measured by each study where responses were grouped into the four categories: human activities and mobility, biodiversity threats, wildlife responses, and social systems and structures. One chord represents one measured response. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
References
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