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Browsing by Author "Chaplin-Kramer, Rebecca"

Now showing 1 - 4 of 4
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    Archetype models upscale understanding of natural pest control response to land-use change
    Alexandridis, Nikolaos; Marion, Glenn; Chaplin-Kramer, Rebecca; Dainese, Matteo; Ekroos, Johan; Grab, Heather; Jonsson, Mattias; Karp, Daniel S.; Meyer, Carsten; O'Rourke, Megan E.; Pontarp, Mikael; Poveda, Katja; Seppelt, Ralf; Smith, Henrik G.; Walters, Richard J.; Clough, Yann; Martin, Emily A. (Wiley, 2022-06)
    Control of crop pests by shifting host plant availability and natural enemy activity at landscape scales has great potential to enhance the sustainability of agriculture. However, mainstreaming natural pest control requires improved understanding of how its benefits can be realized across a variety of agroecological contexts. Empirical studies suggest significant but highly variable responses of natural pest control to land-use change. Current ecological models are either too specific to provide insight across agroecosystems or too generic to guide management with actionable predictions. We suggest obtaining the full benefit of available empirical, theoretical, and methodological knowledge by combining trait-mediated understanding from correlative studies with the explicit representation of causal relationships achieved by mechanistic modeling. To link these frameworks, we adapt the concept of archetypes, or context-specific generalizations, from sustainability science. Similar responses of natural pest control to land-use gradients across cases that share key attributes, such as functional traits of focal organisms, indicate general processes that drive system behavior in a context-sensitive manner. Based on such observations of natural pest control, a systematic definition of archetypes can provide the basis for mechanistic models of intermediate generality that cover all major agroecosystems worldwide. Example applications demonstrate the potential for upscaling understanding and improving predictions of natural pest control, based on knowledge transfer and scientific synthesis. A broader application of this mechanistic archetype approach promises to enhance ecology's contribution to natural resource management across diverse regions and social-ecological contexts.
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    A global synthesis reveals biodiversity-mediated benefits for crop production
    Dainese, Matteo; Martin, Emily A.; Aizen, Marcelo A.; Albrecht, Matthias; Bartomeus, Ignasi; Bommarco, Riccardo; Carvalheiro, Luisa G.; Chaplin-Kramer, Rebecca; Gagic, Vesna; Garibaldi, Lucas A.; Ghazoul, Jaboury; Grab, Heather; Jonsson, Mattias; Karp, Daniel S.; Kennedy, Christina M.; Kleijn, David; Kremen, Claire; Landis, Douglas A.; Letourneau, Deborah K.; Marini, Lorenzo; Poveda, Katja; Rader, Romina; Smith, Henrik G.; Tscharntke, Teja; Andersson, Georg K. S.; Badenhausser, Isabelle; Baensch, Svenja; Bezerra, Antonio Diego M.; Bianchi, Felix J. J. A.; Boreux, Virginie; Bretagnolle, Vincent; Caballero-Lopez, Berta; Cavigliasso, Pablo; Cetkovic, Aleksandar; Chacoff, Natacha P.; Classen, Alice; Cusser, Sarah; da Silva e Silva, Felipe D.; de Groot, G. Arjen; Dudenhoeffer, Jan H.; Ekroos, Johan; Fijen, Thijs; Franck, Pierre; Freitas, Breno M.; Garratt, Michael P. D.; Gratton, Claudio; Hipolito, Juliana; Holzschuh, Andrea; Hunt, Lauren; Iverson, Aaron L.; Jha, Shalene; Keasar, Tamar; Kim, Tania N.; Kishinevsky, Miriam; Klatt, Bjorn K.; Klein, Alexandra-Maria; Krewenka, Kristin M.; Krishnan, Smitha; Larsen, Ashley E.; Lavigne, Claire; Liere, Heidi; Maas, Bea; Mallinger, Rachel E.; Martinez Pachon, Eliana; Martinez-Salinas, Alejandra; Meehan, Timothy D.; Mitchell, Matthew G. E.; Molina, Gonzalo A. R.; Nesper, Maike; Nilsson, Lovisa; O'Rourke, Megan E.; Peters, Marcell K.; Plecas, Milan; Potts, Simon G.; Ramos, Davi de L.; Rosenheim, Jay A.; Rundlof, Maj; Rusch, Adrien; Saez, Agustin; Scheper, Jeroen; Schleuning, Matthias; Schmack, Julia M.; Sciligo, Amber R.; Seymour, Colleen; Stanley, Dara A.; Stewart, Rebecca M.; Stout, Jane C.; Sutter, Louis; Takada, Mayura B.; Taki, Hisatomo; Tamburini, Giovanni; Tschumi, Matthias; Viana, Blandina F.; Westphal, Catrin; Willcox, Bryony K.; Wratten, Stephen D.; Yoshioka, Akira; Zaragoza-Trello, Carlos; Zhang, Wei; Zou, Yi; Steffan-Dewenter, Ingolf (AAAS, 2019-10)
    Human land use threatens global biodiversity and compromises multiple ecosystem functions critical to food production. Whether crop yield-related ecosystem services can be maintained by a few dominant species or rely on high richness remains unclear. Using a global database from 89 studies (with 1475 locations), we partition the relative importance of species richness, abundance, and dominance for pollination; biological pest control; and final yields in the context of ongoing land-use change. Pollinator and enemy richness directly supported ecosystem services in addition to and independent of abundance and dominance. Up to 50% of the negative effects of landscape simplification on ecosystem services was due to richness losses of service-providing organisms, with negative consequences for crop yields. Maintaining the biodiversity of ecosystem service providers is therefore vital to sustain the flow of key agroecosystem benefits to society.
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    Influences of Satellite Sensor and Scale on Derivation of Ecosystem Functional Types and Diversity
    Liu, Lingling; Smith, Jeffrey R.; Armstrong, Amanda H.; Alcaraz-Segura, Domingo; Epstein, Howard E.; Echeverri, Alejandra; Langhans, Kelley E.; Schmitt, Rafael J. P.; Chaplin-Kramer, Rebecca (MDPI, 2023-12-01)
    Satellite-derived Ecosystem Functional Types (EFTs) are increasingly used in ecology and conservation to characterize ecosystem heterogeneity. The diversity of EFTs, also known as Ecosystem Functional Diversity (EFD), has been suggested both as a potential metric of ecosystem-level biodiversity and as a predictor for ecosystem functioning, ecosystem services, and resilience. However, the impact of key methodological choices on patterns of EFTs and EFD have not been formally assessed. Using Costa Rica as a study system, we compared EFTs and EFD, derived from MODIS and Landsat data using different methodological assumptions, at both national and local extents. Our results showed that the regional spatial patterns of EFTs and EFD derived from 250 m MODIS and 30 m Landsat are notably different. The selection of sensors for deriving EFTs and EFD is dependent on the study area, data quality, and the research objective. Given its finer spatial resolution, Landsat has greater capacity to differentiate more EFTs than MODIS, though MODIS could be a better choice in frequently cloudy areas due to its shorter revisiting time. We also found that the selection of spatial extent used to derive EFD is critical, as smaller extents (e.g., at a local rather than a national scale) can show much higher diversity. However, diversity levels derived at smaller extents appear to be nested within the diversity levels derived at larger extents. As EFTs and EFD continue to develop as a tool for ecosystem ecology, we highlight the important methodological choices to ensure that these metrics best fit research objectives.
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    Near-term ecological forecasting for climate change action
    Dietze, Michael; White, Ethan P.; Abeyta, Antoinette; Boettiger, Carl; Bueno Watts, Nievita; Carey, Cayelan C.; Chaplin-Kramer, Rebecca; Emanuel, Ryan E.; Ernest, S. K. Morgan; Figueiredo, Renato J.; Gerst, Michael D.; Johnson, Leah R.; Kenney, Melissa A.; McLachlan, Jason S.; Paschalidis, Ioannis Ch.; Peters, Jody A.; Rollinson, Christine R.; Simonis, Juniper; Sullivan-Wiley, Kira; Thomas, R. Quinn; Wardle, Glenda M.; Willson, Alyssa M.; Zwart, Jacob (Springer Nature, 2024-11-08)
    A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term (daily to decadal) iterative ecological forecasting to improve decision-making on actionable time frames. We summarize the current status of ecological forecasting and focus on how to scale up, build on lessons from weather forecasting, and take advantage of recent technological advances. We also highlight the need to focus on equity, workforce development, and broad cross-disciplinary and non-academic partnerships.