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4. Global COVID-19 lockdown highlights humans as both threats and custodians of the environment

Author

Bates, Amanda E, Primack, Richard B, Biggar, Brandy S, Bird, Tomas J, Clinton, Mary E, Command, Rylan J, Richards, Cerren, Shellard, Marc, Geraldi, Nathan R, Vergara, Valeria, Acevedo-Charry, Orlando, Colón-Piñeiro, Zuania, Ocampo, David, Ocampo-Peñuela, Natalia, Sánchez-Clavijo, Lina M, Adamescu, Cristian M, Cheval, Sorin, Racoviceanu, Tudor, Adams, Matthew D, Kalisa, Egide, Kuuire, Vincent Z, Aditya, Vikram, Anderwald, Pia, Wiesmann, Samuel, Wipf, Sonja, Badihi, Gal, Henderson, Matthew G, Loetscher, Hanspeter, Baerenfaller, Katja, Benedetti-Cecchi, Lisandro, Bulleri, Fabio, Bertocci, Iacopo, Maggi, Elena, Rindi, Luca, Ravaglioli, Chiara, Boerder, Kristina, Bonnel, Julien, Mathias, Delphine, Archambault, Philippe, Chauvaud, Laurent, Braun, Camrin D, Thorrold, Simon R, Brownscombe, Jacob W, Midwood, Jonathan D, Boston, Christine M, Brooks, Jill L, Cooke, Steven J, China, Victor, Roll, Uri, Belmaker, Jonathan, Zvuloni, Assaf, Coll, Marta, Ortega, Miquel, Connors, Brendan, Lacko, Lisa, Jayathilake, Dinusha RM, Costello, Mark J, Crimmins, Theresa M, Barnett, LoriAnne, Denny, Ellen G, Gerst, Katharine L, Marsh, RL, Posthumus, Erin E, Rodriguez, Reilly, Rosemartin, Alyssa, Schaffer, Sara N, Switzer, Jeff R, Wong, Kevin, Cunningham, Susan J, Sumasgutner, Petra, Amar, Arjun, Thomson, Robert L, Stofberg, Miqkayla, Hofmeyr, Sally, Suri, Jessleena, Stuart-Smith, Rick D, Day, Paul B, Edgar, Graham J, Cooper, Antonia T, De Leo, Fabio Cabrera, Garner, Grant, Brisay, Paulson G Des, Schrimpf, Michael B, Koper, Nicola, Diamond, Michael S, Dwyer, Ross G, Baker, Cameron J, Franklin, Craig E, Efrat, Ron, Berger-Tal, Oded, Hatzofe, Ohad, Eguíluz, Víctor M, Rodríguez, Jorge P, Fernández-Gracia, Juan, Elustondo, David, Calatayud, Vicent, English, Philina A, Archer, Stephanie K, Dudas, Sarah E, and Haggarty, Dana R

Subjects
Life on Land, Pandemic, Biodiversity, Restoration, Global monitoring, Environmental Sciences, Biological Sciences, Agricultural and Veterinary Sciences, Ecology
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.

Published
2021

7. Arithmetic normalisation models for the effects of lipid on carbon stable isotope values in silver carp (<italic>Hypophthalmichthys molitrix</italic>) tissue.

Author

Seghers, Bridget, Archer, Stephanie K., Host, Abigail, Bockus, Abigail, Stahl, Angela R., and Polito, Michael J.

Subjects
*SILVER carp, *NATIVE species, *STABLE isotopes, *ISOTOPIC analysis, *CARBON isotopes
Abstract

Invasive silver carp (Hypophthalmichthys molitrix) threaten Mississippi River basin ecosystems due to their ability to outcompete native species. Stable carbon (δ13C) and nitrogen (δ15N) isotope analysis has been used to study how silver carp impact native ecosystems, but lipids in fish tissues commonly bias their δ13C values. Chemical lipid extraction and mathematical equations that normalise δ13C values for lipid content can account for this bias, but have not been assessed for silver carp. We examined δ13C, δ15N, and C:N ratios before and after chemical lipid extraction using 2:1 chloroform:methanol in silver carp muscle and whole fish collected along the Mississippi River. We used linear and natural log models to estimate lipid-extracted δ13C values in silver carp muscle and whole fish samples based on their non-lipid-extracted δ13C values and elemental C:N ratios. Arithmetic models were evaluated for best fit, parsimony, and accuracy between mathematically normalised and chemically lipid-extracted δ13C values. Chemical lipid extraction increased silver carp δ13C values and decreased C:N ratio in muscle and whole fish, and increased δ15N values in whole fish but not fish muscle. While both linear and natural log models accurately estimated lipid extracted δ13C values, natural log models provided better fit and parsimony throughout a wide range of C:N ratios. These results confirmed the need to account for lipid effects on δ13C values in silver carp. Moreover, our study will allow researchers to conduct isotopic analysis without the added time and cost of chemical lipid extraction and facilitate the comparison of silver carp muscle and whole fish isotopic values across studies. [ABSTRACT FROM AUTHOR]

Published
2025
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8. Global COVID-19 lockdown highlights humans as both threats and custodians of the environment

Author

Bates, Amanda E., Primack, Richard B., Biggar, Brandy S., Bird, Tomas J., Clinton, Mary E., Command, Rylan J., Richards, Cerren, Shellard, Marc, Geraldi, Nathan R., Vergara, Valeria, Acevedo-Charry, Orlando, Colón-Piñeiro, Zuania, Ocampo, David, Ocampo-Peñuela, Natalia, Sánchez-Clavijo, Lina M., Adamescu, Cristian M., Cheval, Sorin, Racoviceanu, Tudor, Adams, Matthew D., Kalisa, Egide, Kuuire, Vincent Z., Aditya, Vikram, Anderwald, Pia, Wiesmann, Samuel, Wipf, Sonja, Badihi, Gal, Henderson, Matthew G., Loetscher, Hanspeter, Baerenfaller, Katja, Benedetti-Cecchi, Lisandro, Bulleri, Fabio, Bertocci, Iacopo, Maggi, Elena, Rindi, Luca, Ravaglioli, Chiara, Boerder, Kristina, Bonnel, Julien, Mathias, Delphine, Archambault, Philippe, Chauvaud, Laurent, Braun, Camrin D., Thorrold, Simon R., Brownscombe, Jacob W., Midwood, Jonathan D., Boston, Christine M., Brooks, Jill L., Cooke, Steven J., China, Victor, Roll, Uri, Belmaker, Jonathan, Zvuloni, Assaf, Coll, Marta, Ortega, Miquel, Connors, Brendan, Lacko, Lisa, Jayathilake, Dinusha R.M., Costello, Mark J., Crimmins, Theresa M., Barnett, LoriAnne, Denny, Ellen G., Gerst, Katharine L., Marsh, R.L., Posthumus, Erin E., Rodriguez, Reilly, Rosemartin, Alyssa, Schaffer, Sara N., Switzer, Jeff R., Wong, Kevin, Cunningham, Susan J., Sumasgutner, Petra, Amar, Arjun, Thomson, Robert L., Stofberg, Miqkayla, Hofmeyr, Sally, Suri, Jessleena, Stuart-Smith, Rick D., Day, Paul B., Edgar, Graham J., Cooper, Antonia T., De Leo, Fabio Cabrera, Garner, Grant, Des Brisay, Paulson G., Schrimpf, Michael B., Koper, Nicola, Diamond, Michael S., Dwyer, Ross G., Baker, Cameron J., Franklin, Craig E., Efrat, Ron, Berger-Tal, Oded, Hatzofe, Ohad, Eguíluz, Víctor M., Rodríguez, Jorge P., Fernández-Gracia, Juan, Elustondo, David, Calatayud, Vicent, English, Philina A., Archer, Stephanie K., Dudas, Sarah E., Haggarty, Dana R., Gallagher, Austin J., Shea, Brendan D., Shipley, Oliver N., Gilby, Ben L., Ballantyne, Jasmine, Olds, Andrew D., Henderson, Christopher J., Schlacher, Thomas A., Halliday, William D., Brown, Nicholas A.W., Woods, Mackenzie B., Balshine, Sigal, Juanes, Francis, Rider, Mitchell J., Albano, Patricia S., Hammerschlag, Neil, Hays, Graeme C., Esteban, Nicole, Pan, Yuhang, He, Guojun, Tanaka, Takanao, Hensel, Marc J.S., Orth, Robert J., Patrick, Christopher J., Hentati-Sundberg, Jonas, Olsson, Olof, Hessing-Lewis, Margot L., Higgs, Nicholas D., Hindell, Mark A., McMahon, Clive R., Harcourt, Rob, Guinet, Christophe, Hirsch, Sarah E., Perrault, Justin R., Hoover, Shelby R., Reilly, Jennifer D., Hobaiter, Catherine, Gruber, Thibaud, Huveneers, Charlie, Udyawer, Vinay, Clarke, Thomas M., Kroesen, Laura P., Hik, David S., Cherry, Seth G., Del Bel Belluz, Justin A., Jackson, Jennifer M., Lai, Shengjie, Lamb, Clayton T., LeClair, Gregory D., Parmelee, Jeffrey R., Chatfield, Matthew W.H., Frederick, Cheryl A., Lee, Sangdon, Park, Hyomin, Choi, Jaein, LeTourneux, Frédéric, Grandmont, Thierry, de-Broin, Frédéric Dulude, Bêty, Joël, Gauthier, Gilles, Legagneux, Pierre, Lewis, Jesse S., Haight, Jeffrey, Liu, Zhu, Lyon, Jarod P., Hale, Robin, D'Silva, Dallas, MacGregor-Fors, Ian, Arbeláez-Cortés, Enrique, Estela, Felipe A., Sánchez-Sarria, Camilo E., García-Arroyo, Michelle, Aguirre-Samboní, Giann K., Franco Morales, Juan C., Malamud, Shahar, Gavriel, Tal, Buba, Yehezkel, Salingré, Shira, Lazarus, Mai, Yahel, Ruthy, Ari, Yigael Ben, Miller, Eyal, Sade, Rotem, Lavian, Guy, Birman, Ziv, Gury, Manor, Baz, Harel, Baskin, Ilia, Penn, Alon, Dolev, Amit, Licht, Ogen, Karkom, Tabi, Davidzon, Sharon, Berkovitch, Avi, Yaakov, Ofer, Manenti, Raoul, Mori, Emiliano, Ficetola, Gentile Francesco, Lunghi, Enrico, March, David, Godley, Brendan J., Martin, Cecilia, Mihaly, Steven F., Barclay, David R., Thomson, Dugald J.M., Dewey, Richard, Bedard, Jeannette, Miller, Aroha, Dearden, Amber, Chapman, Jennifer, Dares, Lauren, Borden, Laura, Gibbs, Donna, Schultz, Jessica, Sergeenko, Nikita, Francis, Fiona, Weltman, Amanda, Moity, Nicolas, Ramírez-González, Jorge, Mucientes, Gonzalo, Alonso-Fernández, Alexandre, Namir, Itai, Bar-Massada, Avi, Chen, Ron, Yedvab, Shmulik, Okey, Thomas A., Oppel, Steffen, Arkumarev, Volen, Bakari, Samuel, Dobrev, Vladimir, Saravia-Mullin, Victoria, Bounas, Anastasios, Dobrev, Dobromir, Kret, Elzbieta, Mengistu, Solomon, Pourchier, Cloé, Ruffo, Alazar, Tesfaye, Million, Wondafrash, Mengistu, Nikolov, Stoyan C., Palmer, Charles, Sileci, Lorenzo, Rex, Patrick T., Lowe, Christopher G., Peters, Francesc, Pine, Matthew K., Radford, Craig A., Wilson, Louise, McWhinnie, Lauren, Scuderi, Alessia, Jeffs, Andrew G., Prudic, Kathleen L., Larrivée, Maxim, McFarland, Kent P., Solis, Rodrigo, Hutchinson, Rebecca A., Queiroz, Nuno, Furtado, Miguel A., Sims, David W., Southall, Emily, Quesada-Rodriguez, Claudio A., Diaz-Orozco, Jessica P., Rodgers, Ku'ulei S., Severino, Sarah J.L., Graham, Andrew T., Stefanak, Matthew P., Madin, Elizabeth M.P., Ryan, Peter G., Maclean, Kyle, Weideman, Eleanor A., Şekercioğlu, Çağan H., Kittelberger, Kyle D., Kusak, Josip, Seminoff, Jeffrey A., Hanna, Megan E., Shimada, Takahiro, Meekan, Mark G., Smith, Martin K.S., Mokhatla, Mohlamatsane M., Soh, Malcolm C.K., Pang, Roanna Y.T., Ng, Breyl X.K., Lee, Benjamin P.Y.-H., Loo, Adrian H.B., Er, Kenneth B.H., Souza, Gabriel B.G., Stallings, Christopher D., Curtis, Joseph S., Faletti, Meaghan E., Peake, Jonathan A., Schram, Michael J., Wall, Kara R., Terry, Carina, Rothendler, Matt, Zipf, Lucy, Ulloa, Juan Sebastián, Hernández-Palma, Angélica, Gómez-Valencia, Bibiana, Cruz-Rodríguez, Cristian, Herrera-Varón, Yenifer, Roa, Margarita, Rodríguez-Buriticá, Susana, Ochoa-Quintero, Jose Manuel, Vardi, Reut, Vázquez, Víctor, Requena-Mesa, Christian, Warrington, Miyako H., Taylor, Michelle E., Woodall, Lucy C., Stefanoudis, Paris V., Zhang, Xiangliang, Yang, Qiang, Zukerman, Yuval, Sigal, Zehava, Ayali, Amir, Clua, Eric E.G., Carzon, Pamela, Seguine, Clementine, Corradini, Andrea, Pedrotti, Luca, Foley, Catherine M., Gagnon, Catherine Alexandra, Panipakoochoo, Elijah, Milanes, Celene B., Botero, Camilo M., Velázquez, Yunior R., Milchakova, Nataliya A., Morley, Simon A., Martin, Stephanie M., Nanni, Veronica, Otero, Tanya, Wakeling, Julia, Abarro, Sarah, Piou, Cyril, Sobral, Ana F.L., Soto, Eulogio H., Weigel, Emily G., Bernal-Ibáñez, Alejandro, Gestoso, Ignacio, Cacabelos, Eva, Cagnacci, Francesca, Devassy, Reny P., Loretto, Matthias-Claudio, Moraga, Paula, Rutz, Christian, and Duarte, Carlos M.

Published
2021
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9. Conserving ecosystem integrity: Ecological theory as a guide for marine protected area monitoring.

Author

Dunham, Anya, Iacarella, Josephine C., Hunter, Karen L., Davies, Sarah C., Dudas, Sarah, Gale, Katie S. P., Rubidge, Emily, and Archer, Stephanie K.

Subjects
ECOLOGICAL integrity, BIOINDICATORS, MARINE parks & reserves, BIOMASS, SCIENTIFIC community
Abstract

Global policies increasingly focus on the importance of maintaining or improving the integrity of ecosystems, but defining, assessing, and monitoring integrity in marine protected areas (MPAs) remains a challenge. In this paper, we conceptualized ecological integrity along dimensions of heterogeneity and stability containing seven components: physical structure, diversity, function, persistence, resistance, resilience, and natural variability. Through a structured literature search, we identified indicators and metrics used for quantifying ecosystem status components in the marine environment, then reviewed MPA management plans worldwide for inclusion of these components. We evaluated 202 papers applying 83 ecological indicators built from 72 metrics. Ecosystem components were most comprehensively addressed by metrics of taxa presence, organisms count, and area occupied by benthic organisms, and community structure, biomass, and percent cover indicators. Of the 557 MPA management plans we reviewed globally, 93% used at least one ecosystem status term or its synonym in an ecologically relevant context, but 39% did not address any components of stability. In particular, resistance was mentioned in only 1% of management plans, but in some cases it may be inferred from indicators and metrics used to track the best addressed component in management plans, diversity. Plans for MPAs with both an ecological/biological purpose and a research and education purpose contained ecosystem status terms more frequently than other plans, suggesting that engagement with the scientific community may have improved the application of these terms. An improved understanding of how to operationalize and measure ecological integrity can help MPA monitoring and management. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Automatic detection of unidentified fish sounds: a comparison of traditional machine learning with deep learning.

Author

Mouy, Xavier, Archer, Stephanie K., Dosso, Stan, Dudas, Sarah, English, Philina, Foord, Colin, Halliday, William, Juanes, Francis, Lancaster, Darienne, Van Parijs, Sofie, and Haggarty, Dana

Subjects
DEEP learning, AUTOMATIC classification, CONVOLUTIONAL neural networks, ACOUSTIC transients, MACHINE learning, RANDOM forest algorithms
Abstract

Many species of fishes around the world are soniferous. The types of sounds fishes produce vary among species and regions but consist typically of low-frequency (< 1.5 kHz) pulses and grunts. These sounds can potentially be used to monitor fishes non-intrusively and could complement traditional monitoring techniques. However, the significant time required for human analysts to manually label fish sounds in acoustic recordings does not yet allow passive acoustics to be used as a viable tool for monitoring fishes. In this paper, we compare two different approaches to automatically detect fish sounds. One is a more traditional machine learning technique based on the detection of acoustic transients in the spectrogram and the classification using Random Forest (RF). The other is using a deep learning approach and is based on the classification of overlapping segments (0.2 s) of spectrogram using a ResNet18 Convolutional Neural Network (CNN). Both algorithms were trained using 21,950 manually annotated fish and non-fish sounds collected from 2014 to 2019 at five different locations in the Strait of Georgia, British Columbia, Canada. The performance of the detectors was tested on part of the data from the Strait of Georgia that was withheld from the training phase, data from Barkley Sound, British Columbia, and data collected in the Port of Miami, Florida, United States. The CNN performed up to 1.9 times better than the RF (F1 score: 0.82 vs. 0.43). In some cases, the CNN was able to find more faint fish sounds than the analyst and performed well in environments different from the one it was trained in (Miami F1 score: 0.88). Noise analysis in the 20--1,000 Hz frequency band shows that the CNN is still reliable in noise levels greater than 130 dB re 1 μPa in the Port of Miami but becomes less reliable in Barkley Sound past 100 dB re 1 μPa due to mooring noise. The proposed approach can efficiently monitor (unidentified) fish sounds in a variety of environments and can also facilitate the development of species-specific detectors. We provide the software FishSound Finder, an easy-to-use open-source implementation of the CNN detector with detailed documentation. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Glass sponge reef soundscapes

Author

Archer, Stephanie K., primary, Halliday, William D., additional, Riera, Amalis, additional, Mouy, Xavier, additional, Pine, Matthew, additional, Dunham, Anya, additional, and Juanes, Francis, additional

Published
2018
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27. Total live mangrove coverage and annual NDVI classifications for the mangrove die-off region based on Landsat 5 and 7 transformed imagery.

Author

Layman, Craig, Rossi, Ryann, Archer, Stephanie K, Layman, Craig, Rossi, Ryann, and Archer, Stephanie K

Abstract

Dataset: Live area and NDVI measurements, These data were compiled to better determine when the mangrove die-off began. NDVI values of 0.2 or greater correspond to live mangrove cover. These data suggest the mangrove die-off may have started in 2008 and was exacerbated in following years with some recovery in 2013. Live area was calculated by determining the area of the die-off region with NDVI values greater than 0.2. For a complete list of measurements, refer to the supplemental document 'Field_names.pdf', and a full dataset description is included in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: http://www.bco-dmo.org/dataset/720270, NSF Division of Ocean Sciences (NSF OCE) OCE-1541637

Published
2017

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