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4. Sonoric geography – addressing the silence of biogeography

Author

Lomolino, Mark V. and Pijanowski, Bryan C.

Subjects
Acoustics, conservation, sonoric geography, sound, soundscapes
Abstract

In 2015, we called upon our colleagues to address a glaring oversight of a potentially transformative frontier in biogeography – the geography of sound (Lomolino et al. 2015).  Our purpose here is to lay the conceptual foundations, based on the fundamental unifying principles of biogeography, to guide the development of the nascent field of sonoric geography.  We define sonoric geography as an emerging subdiscipline of biogeography that attempts to discover and articulate patterns of geographic variation in the acoustic properties of biological communities and identify the underlying, causal explanations for those patterns.We see at least two major benefits to this initiative.  First, it will advance the field of biogeography by expanding the spectrum of biological properties studied – demonstrating how the field’s fundamental, unifying principles can be applied to a novel component of biological diversity – sound and acoustic assemblages across the principal geographic dimensions (area, isolation, elevation/depth, and latitude).  Second, a research program in sonoric geography will, in synergism, advance the fields of soundscape ecology (Pijanowski et al. 2011, Slabbekoorn 2018) and acoustic ecology (Wrightson 2000) by integrating an explicit geographic context into their conceptual foundations, empirical investigations, and applications for conserving biological diversity, sensu lato—again, all this guided by the fundamental unifying principles of biogeography.

Published
2021

6. Co-limitation towards lower latitudes shapes global forest diversity gradients

Author

Liang, Jingjing, Gamarra, Javier G. P., Picard, Nicolas, Zhou, Mo, Pijanowski, Bryan, Jacobs, Douglass F., Reich, Peter B., Crowther, Thomas W., Nabuurs, Gert-Jan, de-Miguel, Sergio, Fang, Jingyun, Woodall, Christopher W., Svenning, Jens-Christian, Jucker, Tommaso, Bastin, Jean-Francois, Wiser, Susan K., Slik, Ferry, Hérault, Bruno, Alberti, Giorgio, Keppel, Gunnar, Hengeveld, Geerten M., Ibisch, Pierre L., Silva, Carlos A., ter Steege, Hans, Peri, Pablo L., Coomes, David A., Searle, Eric B., von Gadow, Klaus, Jaroszewicz, Bogdan, Abbasi, Akane O., Abegg, Meinrad, Yao, Yves C. Adou, Aguirre-Gutiérrez, Jesús, Zambrano, Angelica M. Almeyda, Altman, Jan, Alvarez-Dávila, Esteban, Álvarez-González, Juan Gabriel, Alves, Luciana F., Amani, Bienvenu H. K., Amani, Christian A., Ammer, Christian, Ilondea, Bhely Angoboy, Antón-Fernández, Clara, Avitabile, Valerio, Aymard, Gerardo A., Azihou, Akomian F., Baard, Johan A., Baker, Timothy R., Balazy, Radomir, Bastian, Meredith L., Batumike, Rodrigue, Bauters, Marijn, Beeckman, Hans, Benu, Nithanel Mikael Hendrik, Bitariho, Robert, Boeckx, Pascal, Bogaert, Jan, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro H. S., Brandl, Susanne, Brearley, Francis Q., Briseno-Reyes, Jaime, Broadbent, Eben N., Bruelheide, Helge, Bulte, Erwin, Catlin, Ann Christine, Cazzolla Gatti, Roberto, César, Ricardo G., Chen, Han Y. H., Chisholm, Chelsea, Cienciala, Emil, Colletta, Gabriel D., Corral-Rivas, José Javier, Cuchietti, Anibal, Cuni-Sanchez, Aida, Dar, Javid A., Dayanandan, Selvadurai, de Haulleville, Thales, Decuyper, Mathieu, Delabye, Sylvain, Derroire, Géraldine, DeVries, Ben, Diisi, John, Do, Tran Van, Dolezal, Jiri, Dourdain, Aurélie, Durrheim, Graham P., Obiang, Nestor Laurier Engone, Ewango, Corneille E. N., Eyre, Teresa J., Fayle, Tom M., Feunang, Lethicia Flavine N., Finér, Leena, Fischer, Markus, Fridman, Jonas, Frizzera, Lorenzo, de Gasper, André L., Gianelle, Damiano, Glick, Henry B., Gonzalez-Elizondo, Maria Socorro, Gorenstein, Lev, Habonayo, Richard, Hardy, Olivier J., Harris, David J., Hector, Andrew, Hemp, Andreas, Herold, Martin, Hillers, Annika, Hubau, Wannes, Ibanez, Thomas, Imai, Nobuo, Imani, Gerard, Jagodzinski, Andrzej M., Janecek, Stepan, Johannsen, Vivian Kvist, Joly, Carlos A., Jumbam, Blaise, Kabelong, Banoho L. P. R., Kahsay, Goytom Abraha, Karminov, Viktor, Kartawinata, Kuswata, Kassi, Justin N., Kearsley, Elizabeth, Kennard, Deborah K., Kepfer-Rojas, Sebastian, Khan, Mohammed Latif, Kigomo, John N., Kim, Hyun Seok, Klauberg, Carine, Klomberg, Yannick, Korjus, Henn, Kothandaraman, Subashree, Kraxner, Florian, Kumar, Amit, Kuswandi, Relawan, Lang, Mait, Lawes, Michael J., Leite, Rodrigo V., Lentner, Geoffrey, Lewis, Simon L., Libalah, Moses B., Lisingo, Janvier, López-Serrano, Pablito Marcelo, Lu, Huicui, Lukina, Natalia V., Lykke, Anne Mette, Maicher, Vincent, Maitner, Brian S., Marcon, Eric, Marshall, Andrew R., Martin, Emanuel H., Martynenko, Olga, Mbayu, Faustin M., Mbuvi, Musingo T. E., Meave, Jorge A., Merow, Cory, Miscicki, Stanislaw, Moreno, Vanessa S., Morera, Albert, Mukul, Sharif A., Müller, Jörg C., Murdjoko, Agustinus, Nava-Miranda, Maria Guadalupe, Ndive, Litonga Elias, Neldner, Victor J., Nevenic, Radovan V., Nforbelie, Louis N., Ngoh, Michael L., N’Guessan, Anny E., Ngugi, Michael R., Ngute, Alain S. K., Njila, Emile Narcisse N., Nyako, Melanie C., Ochuodho, Thomas O., Oleksyn, Jacek, Paquette, Alain, Parfenova, Elena I., Park, Minjee, Parren, Marc, Parthasarathy, Narayanaswamy, Pfautsch, Sebastian, Phillips, Oliver L., Piedade, Maria T. F., Piotto, Daniel, Pollastrini, Martina, Poorter, Lourens, Poulsen, John R., Poulsen, Axel Dalberg, Pretzsch, Hans, Rodeghiero, Mirco, Rolim, Samir G., Rovero, Francesco, Rutishauser, Ervan, Sagheb-Talebi, Khosro, Saikia, Purabi, Sainge, Moses Nsanyi, Salas-Eljatib, Christian, Salis, Antonello, Schall, Peter, Schepaschenko, Dmitry, Scherer-Lorenzen, Michael, Schmid, Bernhard, Schöngart, Jochen, Šebeň, Vladimír, Sellan, Giacomo, Selvi, Federico, Serra-Diaz, Josep M., Sheil, Douglas, Shvidenko, Anatoly Z., Sist, Plinio, Souza, Alexandre F., Stereńczak, Krzysztof J., Sullivan, Martin J. P., Sundarapandian, Somaiah, Svoboda, Miroslav, Swaine, Mike D., Targhetta, Natalia, Tchebakova, Nadja, Trethowan, Liam A., Tropek, Robert, Mukendi, John Tshibamba, Umunay, Peter Mbanda, Usoltsev, Vladimir A., Vaglio Laurin, Gaia, Valentini, Riccardo, Valladares, Fernando, van der Plas, Fons, Vega-Nieva, Daniel José, Verbeeck, Hans, Viana, Helder, Vibrans, Alexander C., Vieira, Simone A., Vleminckx, Jason, Waite, Catherine E., Wang, Hua-Feng, Wasingya, Eric Katembo, Wekesa, Chemuku, Westerlund, Bertil, Wittmann, Florian, Wortel, Verginia, Zawiła-Niedźwiecki, Tomasz, Zhang, Chunyu, Zhao, Xiuhai, Zhu, Jun, Zhu, Xiao, Zhu, Zhi-Xin, Zo-Bi, Irie C., and Hui, Cang

Published
2022
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11. Soundscapes indicate kelp forest condition

Author

Gottesman, Benjamin L., Sprague, Joshua, Kushner, David J., Bellisario, Kristen, Savage, David, McKenna, Megan F., Conlin, David L., DiDonato, Eva, Barkaszi, Mary J., Halvorsen, Michele B., and Pijanowski, Bryan C.

Published
2020

12. Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Author

Dietze, Michael C, Fox, Andrew, Beck-Johnson, Lindsay M, Betancourt, Julio L, Hooten, Mevin B, Jarnevich, Catherine S, Keitt, Timothy H, Kenney, Melissa A, Laney, Christine M, Larsen, Laurel G, Loescher, Henry W, Lunch, Claire K, Pijanowski, Bryan C, Randerson, James T, Read, Emily K, Tredennick, Andrew T, Vargas, Rodrigo, Weathers, Kathleen C, and White, Ethan P

Subjects
Bayes Theorem, Climate Change, Ecology, Ecosystem, Forecasting, Humans, Models, Theoretical, forecast, ecology, prediction
Abstract

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Published
2018

14. Mapping Planted Forests in the Korean Peninsula Using Artificial Intelligence.

Author

Mitra, Ankita, Alvarez, Cesar Ivan, Abbasi, Akane O., Harris, Nancy L., Shao, Guofan, Pijanowski, Bryan C., Jahanshahi, Mohammad Reza, Gamarra, Javier G. P., Kim, Hyun-Seok, Kim, Tae-Kyung, Ryu, Daun, and Liang, Jingjing

Subjects
MACHINE learning, ARTIFICIAL intelligence, CONVOLUTIONAL neural networks, DATA augmentation, FOREST plants, DEEP learning
Abstract

Forests are essential for maintaining the ecological balance of the planet and providing critical ecosystem services. Amidst an increasing rate of global forest loss due to various natural and anthropogenic factors, many countries are committed to battling forest loss by planting new forests. Despite the reported national statistics on the land area in plantations, accurately delineating boundaries of planted forests with remotely sensed data remains a great challenge. In this study, we explored several deep learning approaches based on Convolutional Neural Networks (CNNs) for mapping the extent of planted forests in the Korean Peninsula. Our methodology involved data preprocessing, the application of data augmentation techniques, and rigorous model training, with performance assessed using various evaluation metrics. To ensure robust performance and accuracy, we validated the model's predictions across the Korean Peninsula. Our analysis showed that the integration of the Near Infrared band from 10 m Sentinel-2 remote sensing images with the UNet deep learning model, incorporated with unfrozen ResNet-34 backbone architecture, produced the best model performance. With a recall of 64% and precision of 76.8%, the UNet model surpassed the other pixel-based deep learning models, including DeepLab and Pyramid Sense Parsing, in terms of classification accuracy. When compared to the ensemble-based Random Forest (RF) machine learning model, the RF approach demonstrates a significantly lower recall rate of 55.2% and greater precision of 92%. These findings highlight the unique strength of deep learning and machine learning approaches for mapping planted forests in diverse geographical regions on Earth. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Lessons and Challenges in Land Change Modeling Derived from Synthesis of Cross-Case Comparisons

Author

Pontius, Robert Gilmore, Jr., Castella, Jean-Christophe, de Nijs, Ton, Duan, Zengqiang, Fotsing, Eric, Goldstein, Noah, Kok, Kasper, Koomen, Eric, Lippitt, Christopher D., McConnell, William, Mohd Sood, Alias, Pijanowski, Bryan, Verburg, Peter, Veldkamp, A. Tom, Gatrell, Jay D., Series editor, Jensen, Ryan R., Series editor, Behnisch, Martin, editor, and Meinel, Gotthard, editor

Published
2018
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21. Evidence That an Informal Environmental Summer Camp Can Contribute to the Construction of the Conceptual Understanding and Situational Interest of STEM in Middle-School Youth

Author

Ghadiri Khanaposhtani, Maryam, Liu, ChangChia James, Gottesman, Benjamin L., Shepardson, Daniel, and Pijanowski, Bryan

Abstract

Youth are introduced to STEM topics through informal settings like science camps, aquaria, and zoos. In these interactive and sensory-rich environments, a well-designed programme can help participants to acquire knowledge and cultivate interest through experiential learning. Given the importance of informal activities in environmental education, it is crucial to identify which contextual components lead to successful learning outcomes. Thus far, research in environmental STEM education has focused on brief experiences, such as one-time visits to curated environments like aquaria. Investigating the impact of multi-day/longer experiences in natural settings is critical because the level of engagement with the STEM topic and the interaction with the learning environment in such experiences have different cognitive and affective impacts. To address these current limitations, we explored whether there is evidence that a four-day, immersive outdoor soundscape ecology camp contributed to situational interest and conceptual understanding of middle-school youth. During the soundscape camp, a variety of evidence was collected through different instruments including drawing activities, questionnaires, an interview, and field-observations. Through a qualitative analysis and open coding, we identified three core principles of informal outdoor curricular design that positively contribute to participants' learning experiences, including direct experience with nature, the use of authentic technology, and exercises that promote collaborative teamwork. We argue that activities that promote scientific practices and engagement with authentic tools in a real-world context creates a learning environment in which participants collaboratively construct deep conceptual understanding of different aspects of environmental STEM topics and foster interest in the context of science inquiry.

Published
2018
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24. Developing Bayesian Information Entropy-based Techniques for Spatially Explicit Model Assessment

Author

Alexandridis, Kostas and Pijanowski, Bryan C.

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

The aim of this paper is to explore and develop advanced spatial Bayesian assessment methods and techniques for land use modeling. The paper provides a comprehensive guide for assessing additional informational entropy value of model predictions at the spatially explicit domain of knowledge, and proposes a few alternative metrics and indicators for extracting higher-order information dynamics from simulation tournaments. A seven-county study area in South-Eastern Wisconsin (SEWI) has been used to simulate and assess the accuracy of historical land use changes (1963-1990) using artificial neural network simulations of the Land Transformation Model (LTM). The use of the analysis and the performance of the metrics helps: (a) understand and learn how well the model runs fits to different combinations of presence and absence of transitions in a landscape, not simply how well the model fits our given data; (b) derive (estimate) a theoretical accuracy that we would expect a model to assess under the presence of incomplete information and measurement; (c) understand the spatially explicit role and patterns of uncertainty in simulations and model estimations, by comparing results across simulation runs; (d) compare the significance or estimation contribution of transitional presence and absence (change versus no change) to model performance, and the contribution of the spatial drivers and variables to the explanatory value of our model; and (e) compare measurements of informational uncertainty at different scales of spatial resolution., Comment: 13 pages, 10 figures, 3 tables, 25 equations Submitted to IEEE Trans Inf Theory

Published
2008

33. Acoustic recording complements camera traps for monitoring sensitive penguin populations.

Author

Francomano, Dante, Raya Rey, Andrea N., Gottesman, Benjamin L., and Pijanowski, Bryan C.

Subjects
COLONIES (Biology), ANIMAL sexual behavior, ECOLOGICAL disturbances, PENGUINS, ARTIFICIAL satellites, ECOSYSTEMS, SATELLITE-based remote sensing
Abstract

Copyright of Ibis is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2024
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43. Silent·Cities

Author

Challéat, Samuel, Farrugia, Nicolas, Gasc, Amandine, Froidevaux, Jeremy, Hatlauf, Jennifer, Dziock, Frank, CHARBONNEAU, Adrien, Linossier, Juliette, Watson, Christopher, Ullrich, Paul, Jůnek, Tomáš, Hulme, Mark, Deoniziak, Krzysztof, Marchais, Guillaume, Gattus, Jean-Christophe, Beason, Richard, López-Baucells, Adrià, Cibulka, Jan, Barbaro, Luc, VIVAT, Agnès, Sandfort, Robin, Médoc, Vincent, da Silva, Luis, Dodgin, Sarah, Briggs, Philip, Linhart, Pavel, Eldridge, Alice, Mueller, Sandra, Gomez, Oscar, BURENS, albane, Puechmaille, Sébastien, Savage, David, Balbuena, Diego, López-Bosch, David, Belshaw, Simon, Cortes, Eliana, Barbier, Eder, Finch, Domhnall, Ceuppens, Ada, Couturier, Clément, Peyronnet, Peyronnet, Vidal Oliveras, Josep, Moreno-Gómez, Felipe, Jones, Steve, Pajusco, Nicolas, Godet, Laurent, Griesberger, Paul, Hintze, Frederico, Thierry, Besche, Ocampo, David, garcia, juan, Belá, Gívan, Bader, Elias, Jégou, Laurent, Bedoya, Daniela, VINET, Olivier, Gilmour, Lia, VRIGNAULT, Jean-Do., Cardoso, Paulo, Sinclair, Peter, Reginster, Jean-Benoît, Abrahams, Carlos, Tarrant, Ian, Acevedo-Charry, Orlando, Colón-Piñeiro, Zuania, Rocha, Ricardo, Tuninetti, Amaro, Victor, Pierre, Scherer-Lorenzen, Michael, Navarro, Carolina, Wearn, Oliver, Quinn, John, Machado, Ricardo, Challéat, Alain, Matheu, Eloisa, Brinkkemper, Joost, Bredewold, Wijnand, Beeston, Amy, Dubset, Chloé, Lauvin, Gregoire, Elliott, David, Davis, Thomas, Quiroga, Nancy, latifi, milad, Amorim, Francisco, Lace, Natalija, Romaine, Luke, Simon, Victoire, Tucker, David, Ferreira, Diogo, Claus, Caroline, Alice, Baudouin, Thompson, Michelle, Newton, Adrian, Durán, Cárol, Schai-Braun, Stéphanie, Clavijo-Bustos, Julián, Nyssen, Pierrette, Korstjens, Amanda, Deans, Sarah, Drolet, Bruno, Marion, PARISOT, Jacobs, Michel, Lizcano, Diego, Townsend, Karina, Canavero, Andrés, Esztl, Karl, Whyte, Damion, Payor, Eve, sebag, mathilde, Habib, Bilal, Reckinger, Georges, Conde, Sofia, Shaw, Taylor, O'Mara, M., Freitas, Bárbara, Montagne, Basile, Lopes, Ricardo, David, PAPAÏX, Villén-Pérez, Sara, Quentin, Rouy, Aucoin, Serge, Garside, Daniel, Ouertani, Marcolin, Fabio, Godart, Louis, Pinto, Nuno, BELTRAN, JUAN, López-Bao, José, Prugh, Laura, Charnaux, Mina, Marques, Carina, PHILIPPE, Magali, Cusack, Peter, Budinski, Ivana, Roos, Andrei, Martin, Tilla, Paiva, Vitor, Sadhukhan, Sougata, Ferreira, Daniel, Duarte, Marina, Pereira, Jorge, Laranjeiras, Thiago, Mackenzie, Michelle, Soto, Juan, Poff, Zach, Cotão, António, Puccherelli, Ignacio, Santos, Ivo, O'Connell, Darren, haupert, sylvain, Aguiar, Ludmilla, Sébastien, LAGUET, Ducruet, Sylvain, PALACINO, JULIAN, Naka, Luciano, Kitson, James, Windsor, Fredric, Beckmann, Christa, Quaglietta, Lorenzo, Templeton, Christopher, Mande, Claude, Elise, Simon, Vega, Kevin, Owren, Jan, Herrera, José, Geoffray, Ferreira, Sonia, Walton, William, Barreiro, Sílvia, Draganoiu, Tudor-Ion, Nunes, João, Villagra, Jose, Carrasco-Rueda, Farah, Segurado, Pedro, de Lacoste, Nathalie, Alves, Flávia, Briglia-Ferreira, Sylvio, Ascensão, Fernando, Poppele, Jonathan, Aiple, Franz, Ross, Samuel, R, PAULA, Rodríguez-González, Patricia, ENGUEHARD, Arthur, Albert, Cécile, Rivera, Vanesa, Levenson, Jacob, Fialas, Penelope, PARIS, Stephane, Bossaert, Mathieu, Khanwilkar, Sarika, Ventrice, Gabriella, Garcia, Fernando, Lavin, Samuel, Buxton, Rachel, Miller, Leah, Ahlin, Zachary, Bates, Henry, Bradley, Ally, Whitten, Hunter, Larrea, Angela, Herzberger, Sierra, Reed, Mimi, Ryser, Alyssa, Foster-Shaner, Lauren, Hallet, Maddy, Buckner, Emily, Todd, Marcela, Ovale, Fenêtre, Hoecherl, Isabelle, WU, Yanchen, Moreno, JoMari, Marques, J., Xie, Selena, Whiteside, Courtney, Martin, Nate, Dickinson, Paul, Araújo, Pedro, Marcaigh, Fionn, Moore, Allen, Patankar, Swaroop, Hodder, Kathy, Marques, Inês, Beltrão-Mendes, Raone, Power, Andrew, Miller, Kirsten, Dentin, Serge, Cerveira, Lara, Evans, Darren, Duarte, Gonçalo, Choksi, Pooja, Lopes, Pedro, Proppe, Darren, Serronha, Ana, Krähenbühl-Künzli, Kim, Llusia, Diego, Lockett, Marty, Puig-Montserrat, Xavier, Bellisario, Kristen, Pijanowski, Bryan, Tattersall, Fran, Branco, Paulo, Biro, Peter, Santos, José, Khalatbari, Leili, Silva, Bruno, de Almeida Scarpelli, Marina, DE ALMEIDA SCARPELLI, MARINA, Boléat, Cathie, Hyland, Emilia, Zina, Vera, Costa, Maria, Friedman, Nicholas, Lankau, Hedwig, Braz, Luís, Sueur, Jerome, BARRIE, LUIS, and GALOP, Didier

Subjects
landscape ecology, SARS-CoV-2, noise pollution, social distancing, deep learning, audioset, urban planning, bioacoustics, ecoacoustics, audio tagging, machine learning, urban ecology, 2019-nCoV, soundscape ecology, environmental geography, urban wildlife
Abstract

A participatory monitoring programme of an exceptional modification of urban soundscapes during Covid-19 containment (Data collection in progress)

Published
2022
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49. Lessons and Challenges in Land Change Modeling Derived from Synthesis of Cross-Case Comparisons

Author

Pontius, Robert Gilmore, primary, Castella, Jean-Christophe, additional, de Nijs, Ton, additional, Duan, Zengqiang, additional, Fotsing, Eric, additional, Goldstein, Noah, additional, Kok, Kasper, additional, Koomen, Eric, additional, Lippitt, Christopher D., additional, McConnell, William, additional, Mohd Sood, Alias, additional, Pijanowski, Bryan, additional, Verburg, Peter, additional, and Veldkamp, A. Tom, additional

Published
2017
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