As modeling tools and approaches become more advanced, ecological models are becoming more complex. Traditional sensitivity analyses can struggle to identify the nonlinearities and interactions emergent from such complexity, especially across broad swaths of parameter space. This limits understanding of the ecological mechanisms underlying model behavior. Machine learning approaches are a potential answer to this issue, given their predictive ability when applied to complex large datasets. While perceptions that machine learning is a “black box” linger, we seek to illuminate its interpretive potential in ecological modeling. To do so, we detail our process of applying random forests to complex model dynamics to produce both high predictive accuracy and elucidate the ecological mechanisms driving our predictions. Specifically, we employ an empirically rooted ontogenetically stage-structured consumer-resource simulation model. Using simulation parameters as feature inputs and simulation output as dependent variables in our random forests, we extended feature analyses into a simple graphical analysis from which we reduced model behavior to three core ecological mechanisms. These ecological mechanisms reveal the complex interactions between internal plant demography and trophic allocation driving community dynamics while preserving the predictive accuracy achieved by our random forests.
- Home
- Search Results
- Page 1 of 1
Search for: All records
-
Total Resources3
- Resource Type
-
00000030000
- More
- Availability
-
21
- Author / Contributor
- Filter by Author / Creator
-
-
Valdovinos, Fernanda S. (3)
-
Glaum, Paul (2)
-
Simon, Sophia M. (2)
-
Dritz, Sabine (1)
-
Glaum, Paul R. (1)
-
Hale, Kayla R.S. (1)
-
McCann, Kevin S. (1)
-
Morris, Jonathan R. (1)
-
Poisot, ed., Timothée (1)
-
Thébault, Elisa (1)
-
Wetzel, William C. (1)
-
Wood, Thomas J. (1)
-
Wootton, Kate L. (1)
-
Yeakel, Justin D. (1)
-
#Tyler Phillips, Kenneth E. (0)
-
#Willis, Ciara (0)
-
& Abreu-Ramos, E. D. (0)
-
& Abramson, C. I. (0)
-
& Abreu-Ramos, E. D. (0)
-
& Adams, S.G. (0)
-
- Filter by Editor
-
-
& Spizer, S. M. (0)
-
& . Spizer, S. (0)
-
& Ahn, J. (0)
-
& Bateiha, S. (0)
-
& Bosch, N. (0)
-
& Brennan K. (0)
-
& Brennan, K. (0)
-
& Chen, B. (0)
-
& Chen, Bodong (0)
-
& Drown, S. (0)
-
& Ferretti, F. (0)
-
& Higgins, A. (0)
-
& J. Peters (0)
-
& Kali, Y. (0)
-
& Ruiz-Arias, P.M. (0)
-
& S. Spitzer (0)
-
& Sahin. I. (0)
-
& Spitzer, S. (0)
-
& Spitzer, S.M. (0)
-
(submitted - in Review for IEEE ICASSP-2024) (0)
-
-
Have feedback or suggestions for a way to improve these results?
!
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
Abstract Free, publicly-accessible full text available December 1, 2024 -
Valdovinos, Fernanda S. ; Hale, Kayla R.S. ; Dritz, Sabine ; Glaum, Paul R. ; McCann, Kevin S. ; Simon, Sophia M. ; Thébault, Elisa ; Wetzel, William C. ; Wootton, Kate L. ; Yeakel, Justin D. ( , Trends in Ecology & Evolution)
-
Glaum, Paul ; Wood, Thomas J. ; Morris, Jonathan R. ; Valdovinos, Fernanda S. ; Poisot, ed., Timothée ( , Ecology Letters)
Abstract Variation in dietary specialisation stems from fundamental interactions between species and their environment. Consequently, understanding the drivers of this variation is key to understanding ecological and evolutionary processes. Dietary specialisation in wild bees has received attention due to their close mutualistic dependence on plants, and because both groups are threatened by biodiversity loss. Many principles governing pollinator specialisation have been identified, but they remain largely unvalidated. Organismal phenology has the potential to structure realised specialisation by determining concurrent resource availability and pollinator foraging activity. We evaluate this principle using mechanistic models of adaptive foraging in pollinators within plant–pollinator networks. While temporal resource overlap has little impact on specialisation in pollinators with extended flight periods, reduced overlap increases specialisation as pollinator flight periods decrease. These results are corroborated empirically using pollen load data taken from bees with shorter and longer flight periods across environments with high and low temporal resource overlap.