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195 results on '"Lahoz‐Monfort, José J."'

2. A standard protocol for reporting species distribution models

Author

Zurell, Damaris, Franklin, Janet, König, Christian, Bouchet, Phil J, Dormann, Carsten F, Elith, Jane, Fandos, Guillermo, Feng, Xiao, Guillera‐Arroita, Gurutzeta, Guisan, Antoine, Lahoz‐Monfort, José J, Leitão, Pedro J, Park, Daniel S, Peterson, A Townsend, Rapacciuolo, Giovanni, Schmatz, Dirk R, Schröder, Boris, Serra‐Diaz, Josep M, Thuiller, Wilfried, Yates, Katherine L, Zimmermann, Niklaus E, and Merow, Cory

Subjects
Life on Land, biodiversity assessment, ecological niche model, habitat suitability model, reproducibility, Shiny, transparency, Ecological Applications, Environmental Science and Management, Ecology
Published
2020

4. Identifying technology solutions to bring conservation into the innovation era

Author

Iacona, Gwenllian, Ramachandra, Anurag, McGowan, Jennifer, Davies, Alasdair, Joppa, Lucas, Koh, Lian Pin, Fegraus, Eric, Game, Edward, Guillera-Arroita, Gurutzeta, Harcourt, Rob, Indraswari, Karlina, Lahoz-Monfort, José J, Oliver, Jessica L, Possingham, Hugh P, Ward, Adrian, Watson, David W, Watson, James EM, Wintle, Brendan A, and Chadès, Iadine

Published
2019

9. Open access solutions for biodiversity journals : Do not replace one problem with another

Author

Peterson, A. Townsend, Anderson, Robert P., Beger, Maria, Bolliger, Janine, Brotons, Lluís, Burridge, Christopher P., Cobos, Marlon E., Cuervo-Robayo, Angela P., Di Minin, Enrico, Diez, Jeffrey, Elith, Jane, Embling, Clare B., Escobar, Luis E., Essl, Franz, Feeley, Kenneth J., Hawkes, Lucy, Jiménez-García, Daniel, Jimenez, Laura, Green, David M., Knop, Eva, Kühn, Ingolf, Lahoz-Monfort, José J., Lira-Noriega, Andres, Lobo, Jorge M., Loyola, Rafael, Nally, Ralph Mac, Machado-Stredel, Fernando, Martínez-Meyer, Enrique, McCarthy, Michael, Merow, Cory, Nori, Javier, Nuñez-Penichet, Claudia, Osorio-Olvera, Luis, Pyšek, Petr, Rejmánek, Marcel, Ricciardi, Anthony, Robertson, Mark, Soto, Octavio Rojas, Romero-Alvarez, Daniel, Roura-Pascual, Núria, Santini, Luca, Schoeman, David S., Schröder, Boris, Soberon, Jorge, Strubbe, Diederik, Thuiller, Wilfried, Traveset, Anna, Treml, Eric A., Václavík, Tomáš, Varela, Sara, Watson, James E. M., Wiersma, Yolanda, Wintle, Brendan, Yañez-Arenas, Carlos, and Zurell, Damaris

Published
2019

11. Model averaging in ecology : a review of Bayesian, information-theoretic, and tactical approaches for predictive inference

Author

Dormann, Carsten F., Calabrese, Justin M., Guillera-Arroita, Gurutzeta, Matechou, Eleni, Bahn, Volker, Bartoń, Kamil, Beale, Colin M., Ciuti, Simone, Elith, Jane, Gerstner, Katharina, Guelat, Jérôme, Keil, Petr, Lahoz-Monfort, José J., Pollock, Laura J., Reineking, Björn, Roberts, David R., Schröder, Boris, Thuiller, Wilfried, Warton, David I., Wintle, Brendan A., Wood, Simon N., Wüest, Rafael O., and Hartig, Florian

Published
2018

33. Challenge 3: Preserving biodiversity and its functions under global change

Author

Arroyo, Beatriz, Bartomeus, Ignasi, Brotons, Lluís, Cebrian, Emma, Delibes-Mateos, Miguel, Gallardo, Belinda, Hortal, Joaquín, Lahoz-Monfort, José J., Leonard, Jennifer A., López-Bao, José V., Casamayor, Emilio O., and Tomàs, Fiona

Subjects
Invasive species, Ecosystem service, Human-wildlife conflicts, Microbial communities, Enviromental policies, Next-generation monitoring
Abstract

Libros Blancos. Desafíos Científicos 2030 del CSIC, vol. 7; coordinado por Jesús Marco de Lucas y M. Victoria Moreno-Arribas; 174 p.: 17 cm. Descarga completa gratuita en el enlace: libros.csic.es/product_info.php?products_id=1483, The environmental sustainability of the Earth system is at risk, and so do human welfare because of our dependency on it. Here we present challenges dealing with the understanding of how drivers of global change work, and how to minimize their effects on natural and human managed systems, with the aid of new concepts and edge-cutting technology. Their achievement should allow us to detect, understand, forecast and mitigate global change impacts related to climate change, the biodiversity crisis, polar regions, and managed ecosystems, and to improve the health of our planet in the coming decades. In the twenty-first century, biodiversity erosion has become a key scientific question at the time societal concern has increased. We propose theoretical, technological and policy-relevant challenges to preserve biodiversity and safeguard our options for future solutions to global environmental problems. We outline research areas to uncover naturally occurring processes, and to predict and mitigate the impact of global change.

Published
2021

38. Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code.

Author

Valavi, Roozbeh, Guillera‐Arroita, Gurutzeta, Lahoz‐Monfort, José J., and Elith, Jane

Subjects
SPECIES distribution, RANDOM forest algorithms, REGRESSION trees, SUPPORT vector machines
Abstract

Species distribution modeling (SDM) is widely used in ecology and conservation. Currently, the most available data for SDM are species presence‐only records (available through digital databases). There have been many studies comparing the performance of alternative algorithms for modeling presence‐only data. Among these, a 2006 paper from Elith and colleagues has been particularly influential in the field, partly because they used several novel methods (at the time) on a global data set that included independent presence–absence records for model evaluation. Since its publication, some of the algorithms have been further developed and new ones have emerged. In this paper, we explore patterns in predictive performance across methods, by reanalyzing the same data set (225 species from six different regions) using updated modeling knowledge and practices. We apply well‐established methods such as generalized additive models and MaxEnt, alongside others that have received attention more recently, including regularized regressions, point‐process weighted regressions, random forests, XGBoost, support vector machines, and the ensemble modeling framework biomod. All the methods we use include background samples (a sample of environments in the landscape) for model fitting. We explore impacts of using weights on the presence and background points in model fitting. We introduce new ways of evaluating models fitted to these data, using the area under the precision‐recall gain curve, and focusing on the rank of results. We find that the way models are fitted matters. The top method was an ensemble of tuned individual models. In contrast, ensembles built using the biomod framework with default parameters performed no better than single moderate performing models. Similarly, the second top performing method was a random forest parameterized to deal with many background samples (contrasted to relatively few presence records), which substantially outperformed other random forest implementations. We find that, in general, nonparametric techniques with the capability of controlling for model complexity outperformed traditional regression methods, with MaxEnt and boosted regression trees still among the top performing models. All the data and code with working examples are provided to make this study fully reproducible. [ABSTRACT FROM AUTHOR]

Published
2022
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41. Assessing the accuracy of density‐independent demographic models for predicting species ranges.

Author

Holden, Matthew H., Yen, Jian D. L., Briscoe, Natalie J., Lahoz‐Monfort, José J., Salguero‐Gómez, Roberto, Vesk, Peter A., and Guillera‐Arroita, Gurutzeta

Subjects
BIOLOGICAL extinction, SPECIES distribution, VITAL statistics, NUTRIENT density, SPECIES
Abstract

Accurately predicting species ranges is a primary goal of ecology. Demographic distribution models (DDMs), which correlate underlying vital rates (e.g. survival and reproduction) with environmental conditions, can potentially predict species ranges through time and space. However, tests of DDM accuracy across wide ranges of species' life histories are surprisingly lacking. Using simulations of 1.5 million hypothetical species' range dynamics, we evaluated when DDMs accurately predicted future ranges, to provide clear guidelines for the use of this emerging approach. We limited our study to deterministic demographic models ignoring density dependence, since these models are the most commonly used in the literature. We found that density‐independent DDMs overpredicted extinction if populations were near carrying capacity in the locations where demographic data were available. However, DDMs accurately predicted species ranges if demographic data were limited to sites with mean initial abundance less than one half of carrying capacity. Additionally, the DDMs required demographic data from at least 25 sites, over a short time‐interval (< 10 time‐steps), as populations initially below carrying capacity can saturate in long‐term studies. For species with demographic data from many low density sites, DDMs predicted occurrence more accurately than correlative species distribution models (SDMs) in locations where the species eventually persisted, but not where the species went extinct. These results were insensitive to differences in simulated dispersal, levels of environmental stochasticity, the effects of the environmental variables and the functional forms of density dependence. Our findings suggest that deterministic, density‐independent DDMs are appropriate for applications where locating all possible sites the species might occur in is prioritized over reducing false presence predictions in absent sites. This makes DDMs a promising tool for mapping invasion risk. However, demographic data are often collected at sites where a species is abundant. Density‐independent DDMs are inappropriate in this case. [ABSTRACT FROM AUTHOR]

Published
2021
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42. blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models

Author

Valavi, Roozbeh, Elith, Jane, Lahoz-Monfort, José J., and Guillera-Arroita, Gurutzeta

Subjects
Computer science, Model selection, Species distribution, Covariate, Data mining, Fold (geology), computer.software_genre, Spatial analysis, computer, Cross-validation, Environmental niche modelling
Abstract

SummaryWhen applied to structured data, conventional random cross-validation techniques can lead to underestimation of prediction error, and may result in inappropriate model selection.We present the R package blockCV, a new toolbox for cross-validation of species distribution modelling.The package can generate spatially or environmentally separated folds. It includes tools to measure spatial autocorrelation ranges in candidate covariates, providing the user with insights into the spatial structure in these data. It also offers interactive graphical capabilities for creating spatial blocks and exploring data folds.Package blockCV enables modellers to more easily implement a range of evaluation approaches. It will help the modelling community learn more about the impacts of evaluation approaches on our understanding of predictive performance of species distribution models.

Published
2018

43. Forecasting species range dynamics with process‐explicit models: matching methods to applications

Author

Briscoe, Natalie J., primary, Elith, Jane, additional, Salguero‐Gómez, Roberto, additional, Lahoz‐Monfort, José J., additional, Camac, James S., additional, Giljohann, Katherine M., additional, Holden, Matthew H., additional, Hradsky, Bronwyn A., additional, Kearney, Michael R., additional, McMahon, Sean M., additional, Phillips, Ben L., additional, Regan, Tracey J., additional, Rhodes, Jonathan R., additional, Vesk, Peter A., additional, Wintle, Brendan A., additional, Yen, Jian D.L., additional, and Guillera‐Arroita, Gurutzeta, additional

Published
2019
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47. Animal movements in fire‐prone landscapes

Author

Nimmo, Dale G., primary, Avitabile, Sarah, additional, Banks, Sam C., additional, Bliege Bird, Rebecca, additional, Callister, Kate, additional, Clarke, Michael F., additional, Dickman, Chris R., additional, Doherty, Tim S., additional, Driscoll, Don A., additional, Greenville, Aaron C., additional, Haslem, Angie, additional, Kelly, Luke T., additional, Kenny, Sally A., additional, Lahoz‐Monfort, José J., additional, Lee, Connie, additional, Leonard, Steven, additional, Moore, Harry, additional, Newsome, Thomas M., additional, Parr, Catherine L., additional, Ritchie, Euan G., additional, Schneider, Kathryn, additional, Turner, James M., additional, Watson, Simon, additional, Westbrooke, Martin, additional, Wouters, Mike, additional, White, Matthew, additional, and Bennett, Andrew F., additional

Published
2018
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48. Managing uncertainty in movement knowledge for environmental decisions

Author

Smith, Annabel L., primary, Kujala, Heini, additional, Lahoz‐Monfort, José J., additional, Guja, Lydia K., additional, Burns, Emma L., additional, Nathan, Ran, additional, Alacs, Erika, additional, Barton, Philip S., additional, Bau, Sana, additional, Driscoll, Don A., additional, Lentini, Pia E., additional, Mortelliti, Alessio, additional, Rowe, Ross, additional, and Buckley, Yvonne M., additional

Published
2018
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