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368 results on '"Rodriguez, Cristian"'

1. Manual-PA: Learning 3D Part Assembly from Instruction Diagrams

Author

Zhang, Jiahao, Cherian, Anoop, Rodriguez, Cristian, Deng, Weijian, and Gould, Stephen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Assembling furniture amounts to solving the discrete-continuous optimization task of selecting the furniture parts to assemble and estimating their connecting poses in a physically realistic manner. The problem is hampered by its combinatorially large yet sparse solution space thus making learning to assemble a challenging task for current machine learning models. In this paper, we attempt to solve this task by leveraging the assembly instructions provided in diagrammatic manuals that typically accompany the furniture parts. Our key insight is to use the cues in these diagrams to split the problem into discrete and continuous phases. Specifically, we present Manual-PA, a transformer-based instruction Manual-guided 3D Part Assembly framework that learns to semantically align 3D parts with their illustrations in the manuals using a contrastive learning backbone towards predicting the assembly order and infers the 6D pose of each part via relating it to the final furniture depicted in the manual. To validate the efficacy of our method, we conduct experiments on the benchmark PartNet dataset. Our results show that using the diagrams and the order of the parts lead to significant improvements in assembly performance against the state of the art. Further, Manual-PA demonstrates strong generalization to real-world IKEA furniture assembly on the IKEA-Manual dataset.

Published
2024

2. Probing Light Scalars and Vector-like Quarks at the High-Luminosity LHC

Author

Qureshi, Umar Sohail, Flórez, Andres, Gurrola, Alfredo, and Rodriguez, Cristian

Subjects
High Energy Physics - Phenomenology
Abstract

A model based on a $U(1)_{T^3_R}$ extension of the Standard Model can address the mass hierarchy between generations of fermions, explain thermal dark matter abundance, and the muon $g - 2$, $R_{(D)}$, and $R_{(D^*)}$ anomalies. The model contains a light scalar boson $\phi'$ and a heavy vector-like quark $\chi_\mathrm{u}$ that can be probed at CERN's Large Hadron Collider (LHC). We perform a phenomenology study on the production of $\phi'$ and ${\chi}_u$ particles from proton-proton $(\mathrm{pp})$ collisions at the LHC at $\sqrt{s}=13.6$ TeV, primarily through $g{-g}$ and $t{-\chi_\mathrm{u}}$ fusion. We work under an effective field theory approach, in which the $\chi_\mathrm{u}$ and $\phi'$ masses are free parameters. We perform a phenomenological analysis considering $\chi_\mathrm{u}$ final states to b-quarks, muons, and neutrinos, and $\phi'$ decays to $\mu^+\mu^-$. A machine learning algorithm is used to maximize the signal sensitivity, considering an integrated luminosity of $3000$ $\textrm{fb}^{-1}$. The proposed methodology can be a key mode for discovery over a large mass range, including low masses, traditionally considered difficult due to experimental constraints.

Published
2024

3. Temporally Grounding Instructional Diagrams in Unconstrained Videos

Author

Zhang, Jiahao, Zhang, Frederic Z., Rodriguez, Cristian, Ben-Shabat, Yizhak, Cherian, Anoop, and Gould, Stephen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We study the challenging problem of simultaneously localizing a sequence of queries in the form of instructional diagrams in a video. This requires understanding not only the individual queries but also their interrelationships. However, most existing methods focus on grounding one query at a time, ignoring the inherent structures among queries such as the general mutual exclusiveness and the temporal order. Consequently, the predicted timespans of different step diagrams may overlap considerably or violate the temporal order, thus harming the accuracy. In this paper, we tackle this issue by simultaneously grounding a sequence of step diagrams. Specifically, we propose composite queries, constructed by exhaustively pairing up the visual content features of the step diagrams and a fixed number of learnable positional embeddings. Our insight is that self-attention among composite queries carrying different content features suppress each other to reduce timespan overlaps in predictions, while the cross-attention corrects the temporal misalignment via content and position joint guidance. We demonstrate the effectiveness of our approach on the IAW dataset for grounding step diagrams and the YouCook2 benchmark for grounding natural language queries, significantly outperforming existing methods while simultaneously grounding multiple queries., Comment: Accepted to WACV'25

Published
2024

4. A Naive Model of Covid-19 Spread From A Dry Cough

Author

Rodriguez, Cristian Ramirez

Subjects
Physics - Computational Physics
Abstract

Health experts have suggested that social distancing measures are one of the most effective ways of preventing the spread of Covid-19. Research primarily focused on large Covid filled droplets suggested that these droplets can move further than regulated social distancing guidelines (2 meters apart) in the presence of wind. This project aims to model the paths of smaller Covid virions that last longer in the air to see if they also move beyond social distancing norms in the presence of wind. By numerically solving a 2-dimensional Langevin equation for 3000 particles and modeling wind as one-dimensional and steady, velocities were found that translated into particles' positions. With wind, infectious doses of virions appeared to travel farther and faster, increasing the risk of infection before dispersion. The two-dimensional model given implies that social distancing norms are reasonable in cases with no wind, yet not conservative enough when there is wind., Comment: 9 pages, 7 figures

Published
2024

5. Bringing the forest back : Restoration priorities in Colombia

Author

Williams, Brooke A., López-Cubillos, Sofía, Ochoa-Quintero, Jose Manuel, Crouzeilles, Renato, Villa-Piñeros, Marcelo, Cubides, Paola Johanna Isaacs, Schmoeller, Marina, Marin, Wilmer, Tedesco, Anazelia, Bastos, Diego, Suárez-Castro, Andrés Felipe, Jiménez, Luis Hernando Romero, Broadbent, Eben N., Zambrano, Angelica M. Almeyda, Vincent, Jeffrey R., Yi, Yuanyuan, Chazdon, Robin L., Watson, James E. M., Urbano, Elkin Alexi Noguera, Rodriguez, Cristian Alexander Cruz, and Beyer, Hawthorne L.

Published
2024

6. Characterization of diamond-turned optics for SCALES

Author

Kain, Isabel J., Hinz, Phil, Doetz, Marius, Bulla, Benjamin, Kupke, Renate, Dillon, Daren, Skemer, Andrew, Stelter, Deno, Gonzales, Michael, MacDonald, Nicholas, Gangadharan, Aditi, Rodriguez, Cristian, Ratliff, Christopher, Lach, Mackenzie R., and Sallum, Steph

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

High-contrast imaging has been used to discover and characterize dozens of exoplanets to date. The primary limiting performance factor for these instruments is contrast, the ratio of exoplanet to host star brightness that an instrument can successfully resolve. Contrast is largely determined by wavefront error, consisting of uncorrected atmospheric turbulence and optical aberrations downstream of AO correction. Single-point diamond turning allows for high-precision optics to be manufactured for use in astronomical instrumentation, presenting a cheaper and more versatile alternative to conventional glass polishing. This work presents measurements of wavefront error for diamond-turned aluminum optics in the Slicer Combined with an Array of Lenslets for Exoplanet Spectroscopy (SCALES) instrument, a 2-5 micron coronagraphic integral field spectrograph under construction for Keck Observatory. Wavefront error measurements for these optics are used to simulate SCALES' point spread function using physical optics propagation software poppy, showing that SCALES' contrast performance is not limited by wavefront error from internal instrument optics., Comment: Techniques and Instrumentation for Detection of Exoplanets XI

Published
2023
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7. MAVIS: Multi-Camera Augmented Visual-Inertial SLAM using SE2(3) Based Exact IMU Pre-integration

Author

Wang, Yifu, Ng, Yonhon, Sa, Inkyu, Parra, Alvaro, Rodriguez, Cristian, Lin, Tao Jun, and Li, Hongdong

Subjects
Computer Science - Robotics
Abstract

We present a novel optimization-based Visual-Inertial SLAM system designed for multiple partially overlapped camera systems, named MAVIS. Our framework fully exploits the benefits of wide field-of-view from multi-camera systems, and the metric scale measurements provided by an inertial measurement unit (IMU). We introduce an improved IMU pre-integration formulation based on the exponential function of an automorphism of SE_2(3), which can effectively enhance tracking performance under fast rotational motion and extended integration time. Furthermore, we extend conventional front-end tracking and back-end optimization module designed for monocular or stereo setup towards multi-camera systems, and introduce implementation details that contribute to the performance of our system in challenging scenarios. The practical validity of our approach is supported by our experiments on public datasets. Our MAVIS won the first place in all the vision-IMU tracks (single and multi-session SLAM) on Hilti SLAM Challenge 2023 with 1.7 times the score compared to the second place., Comment: OpenMAVIS available at: https://github.com/MAVIS-SLAM/ORB_SLAM3_MULTI

Published
2023

8. Aligning Step-by-Step Instructional Diagrams to Video Demonstrations

Author

Zhang, Jiahao, Cherian, Anoop, Liu, Yanbin, Ben-Shabat, Yizhak, Rodriguez, Cristian, and Gould, Stephen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multimodal alignment facilitates the retrieval of instances from one modality when queried using another. In this paper, we consider a novel setting where such an alignment is between (i) instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) and (ii) video segments from in-the-wild videos; these videos comprising an enactment of the assembly actions in the real world. To learn this alignment, we introduce a novel supervised contrastive learning method that learns to align videos with the subtle details in the assembly diagrams, guided by a set of novel losses. To study this problem and demonstrate the effectiveness of our method, we introduce a novel dataset: IAW for Ikea assembly in the wild consisting of 183 hours of videos from diverse furniture assembly collections and nearly 8,300 illustrations from their associated instruction manuals and annotated for their ground truth alignments. We define two tasks on this dataset: First, nearest neighbor retrieval between video segments and illustrations, and, second, alignment of instruction steps and the segments for each video. Extensive experiments on IAW demonstrate superior performances of our approach against alternatives., Comment: Accepted to CVPR'23

Published
2023

9. Morbid Polarization: Exposure to COVID‐19 and Partisan Disagreement about Pandemic Response

Author

Rodriguez, Cristian G, Gadarian, Shana Kushner, Goodman, Sara Wallace, and Pepinsky, Thomas B

Subjects
Political Science, Human Society, COVID-19, partisanship, polarization, pandemic policies, risk avoidance, terror management, COVID‐19, Psychology, Cognitive Sciences, Social Psychology, Political science, Cognitive and computational psychology
Abstract

The COVID-19 pandemic has affected the lives of all Americans, but the severity of the pandemic has been experienced unevenly across space and time. Some states saw sharp rises in COVID-19 cases in early March, whereas case counts rose much later in the rest of the country. In this article, we examine the relationship between exposure to COVID-19 and citizens' views on what type of measures are required to deal with the crises and how experience with and exposure to COVID-19 is associated with greater partisan polarization. We find consistent evidence of partisan divergence in pandemic-response policy preferences across the first six months of the COVID-19 pandemic: Republicans support national control measures whereas Democrats support welfare policies, and interparty differences grow over time. We find only limited evidence that exposure or experience moderates these partisan differences. Our findings are consistent with the view that Americans interpret the COVID-19 pandemic in fundamentally partisan manner, and that objective pandemic conditions play at most a minor role in shaping mass preferences.

Published
2022

19. The impact of a two-year long COVID-19 public health restriction program on mechanical thrombectomy outcomes in a stroke network

Author

Rivera, Rodrigo, Amudio, Cristian, Cruz, Juan Pablo, Brunetti, Enzo, Catalan, Pascual, Sordo, Juan Gabriel, Echeverria, Daniel, Badilla, Lautaro, Chamorro, Alex, Gonzalez, Christian, Ojeda, Héctor, Rodriguez, Cristian, Rogers, Nicole, Bravo, Loreto, Bravo, Fabian, Carrasco, Alejandra, Gonzalez, Walter, Lopez, Sofia, Orellana, Maria Luisa, Oportus, Monica, Salazar, Alejandro, Palacios, Gerardo, and Nguyen, Prof. Thanh

Published
2023
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21. Action Anticipation By Predicting Future Dynamic Images

Author

Rodriguez, Cristian, Fernando, Basura, and Li, Hongdong

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Human action-anticipation methods predict what is the future action by observing only a few portion of an action in progress. This is critical for applications where computers have to react to human actions as early as possible such as autonomous driving, human-robotic interaction, assistive robotics among others. In this paper, we present a method for human action anticipation by predicting the most plausible future human motion. We represent human motion using Dynamic Images and make use of tailored loss functions to encourage a generative model to produce accurate future motion prediction. Our method outperforms the currently best performing action-anticipation methods by 4% on JHMDB-21, 5.2% on UT-Interaction and 5.1% on UCF 101-24 benchmarks., Comment: 14 pages

Published
2018

25. 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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28. Partisan selective exposure: The role of party, ideology and ideological extremity over time.

Author

Rodriguez, Cristian G, Moskowitz, Jake P, Salem, Rammy M, and Ditto, Peter H

Subjects
selective exposure, political bias, media bias, polarization, ideology
Abstract

The preference for media that confirms prior attitudes and beliefs is problematic in democratic societies based on dialogue and joint deliberation. Over the last decades, partisan selective exposure (PSE) is argued to have increased along with other indices of polarization. We address the question of the increase in PSE, and possible differences by party, ideology and ideological extremity. Using data from the Pew Research Survey, we analyzed self-reported media consumption in 8 nationally representative surveys from the period 2000–2012 (n = 23,381). We relied on previous research on ideological classification of media outlets to conduct confirmatory factor analyses establishing the existence of two different variables, conservative and liberal media consumption. We predicted latent variables of media consumption using Item Response Theory models and analyzed the trajectories running latent growth curve models. An unconditional growth model revealeda general and sustained increase in PSE across ideological groups over time. Republicans showed a greater increase over time than did Democrats, after controlling for demographics. Introducing ideological extremity in the model revealed no differences in the trajectories of PSE between liberals and extreme liberals, whereas subjects identified as “very conservative” show a much steeper increase in PSE than any other group, whereas conservatives showed the lowest growth over time. We discuss theoretical implications for ongoing debates about political polarization and ideological asymmetry.

Published
2017

32. TrAC: Visualizing Students Academic Trajectories

Author

Guerra, Julio, Scheihing, Eliana, Henríquez, Valeria, Olivares-Rodríguez, Cristian, Chevreux, Henrique, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Scheffel, Maren, editor, Broisin, Julien, editor, Pammer-Schindler, Viktoria, editor, Ioannou, Andri, editor, and Schneider, Jan, editor

Published
2019
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33. Action Anticipation by Predicting Future Dynamic Images

Author

Rodriguez, Cristian, Fernando, Basura, Li, Hongdong, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Leal-Taixé, Laura, editor, and Roth, Stefan, editor

Published
2019
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34. Insights from an Integrated View of the Biology of Apple Snails (Caenogastropoda: Ampullariidae)

Author

Hayes, Kenneth A, Burks, Romi L, Castro-Vazquez, Alfredo, Darby, Philip C, Heras, Horacio, Martín, Pablo R, Qiu, Jian-Wen, Thiengo, Silvana, Vega, Israel A., Wada, Takashi, Yusa, Yoichi, Burela, Silvana, Cadierno, M Pilar, Cueto, Juan A, Dellagnola, Federico A, Dreon, Marcos S, Frassa, M Victoria, Giraud-Billoud, Maximiliano, Godoy, Martín S, Ituarte, Santiago, Koch, Eduardo, Matsukura, Keiichiro, Pasquevich, M Yanina, Rodriguez, Cristian, Saveanu, Lucía, Seuffert, Maria E, Strong, Ellen E, Sun, Jin, Tamburi, Nicolás E, Tiecher, María J, Turner, Richard L., Valentine-Darby, Patricia L., Cowie, Robert H., and BioStor

Published
2015

48. The influence of scale-dependent geodiversity on species distribution models in a biodiversity hotspot.

Author

Gerstner, Beth E., Blair, Mary E., Bills, Patrick, Cruz-Rodriguez, Cristian A., and Zarnetske, Phoebe L.

Subjects
SPECIES distribution, GEODIVERSITY, GEOLOGIC hot spots, BIODIVERSITY conservation, BIODIVERSITY, MAMMAL conservation, LITHOSPHERE, ECOLOGICAL niche
Abstract

Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'. [ABSTRACT FROM AUTHOR]

Published
2024
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