Multitrophic Models of Predator–prey Energetics: Ii. A Realistic Model of Plant–herbivore–parasitoid–predator Interactions

Gutiérrez, A. P.; Baumgaertner, J.

doi:10.4039/ent116933-7

Cited by 88 publications

(38 citation statements)

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“…PBDM modelling of this level of complexity is facilitated by the fact that the same dynamics model are used for all species and the same forms for the biodynamic models reoccur for analogous processes in the life histories of all species across trophic levels (Gutierrez & Baumgärtner, 1984;Gutierrez et al, 2011;Gutierrez & Ponti, 2013). The development of PBDMs is facilitated by their modular structure enabling different combinations of species to be implemented in model runs using simple true-false Boolean variables, allowing the system to be viewed from the perspective of any of the interacting species (Gutierrez & Baumgärtner, 1984;Gutierrez & Ponti, 2013). The inclusion of rich biology allows prediction and mapping of the phenology, dynamics and relative abundance across a wide landscape with varied climates and future climate change.…”

Section: Discussionmentioning

confidence: 99%

“…eggs {7, 8}, larvae {9, 10} and pupae {11, 12}) and nondiapause adults {13}. The underpinning modelling concepts are found in Gutierrez and Baumgärtner (1984) and Gutierrez (1992Gutierrez ( , 1996, whereas the mathematics of the time invariant and time varying distributed maturation-time dynamics model are provided in Manetsch (1976), Vansickle (1977) and DiCola et al (1999). A brief review of the mathematics is given in the Appendix.…”

Section: Methodsmentioning

confidence: 99%

“…The number of eggs [E(t)] successfully deposited by all females N of age (x = 0 -max) is computed using a supply-demand functional response model (Gutierrez & Baumgärtner, 1984) (Eqn (A2)):…”

Section: Appendix: Brief Overview Of the L Botrana Dynamics Modelmentioning

confidence: 99%

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Climate warming effects on grape and grapevine moth (Lobesia botrana) in thePalearcticregion

Gutiérrez

Ponti

Gilioli³

et al. 2017

Agri and Forest Entomology

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is the principal native pest of grape in the Palearctic region. In the present study, we assessed prospectively the relative abundance of the moth in Europe and the Mediterranean Basin using linked physiologically-based demographic models for grape and L. botrana. The model includes the effects of temperature, day-length and fruit stage on moth development rates, survival and fecundity. 2 Daily weather data for 1980-2010 were used to simulate the dynamics of grapevine and L. botrana in 4506 lattice cells across the region. Average grape yield and pupae per vine were used as metrics of favourability. The results were mapped using the grass Geographic Information System (http://grass.osgeo.org). 3 The model predicts a wide distribution for L. botrana with highest populations in warmer regions in a wide band along latitude 40 ∘ N. 4 The effects of climate warming on grapevine and L. botrana were explored using regional climate model projections based on the A1B scenario of an average +1.8 ∘ C warming during the period 2040-2050 compared with the base period (1960)(1961)(1962)(1963)(1964)(1965)(1966)(1967)(1968)(1969)(1970). Under climate change, grape yields increase northwards and with a higher elevation but decrease in hotter areas. Similarly, L. botrana levels increase in northern areas but decrease in the hot areas where summer temperatures approach its upper thermal limit.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Climate warming effects on grape and grapevine moth (Lobesia botrana) in thePalearcticregion

Gutiérrez

Ponti

Gilioli³

et al. 2017

Agri and Forest Entomology

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“…Both ω and α in this model are taken to be linearly increasing functions of temperature determined by fitting data. This model is a modification of the temperature independent supplydemand response (Gutierrez et al [1995]) response, notwithstanding the comments by Royama [1971, p. 55], who questions the derivation of the response and has shown that it amounts to a simpler Ivlev response.…”

Section: Discrete Time Modelmentioning

confidence: 99%

Accounting for Temperature in Predator Functional Responses

Logan

Wolesensky

2007

Natural Resource Modeling

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ABSTRACT. A rational mechanism that integrates temperature-mediated activity cycles into standard predator functional responses is presented. Daily temperature variations strongly influence times that predators can search for prey, and they affect the activity periods of prey, thereby modifying their detection by predators. Thus, key parameters in the functional response, the search time and the detection, become temperature-dependent. These temperature mediated responses are included in discrete-time population growth models, and it is shown how environmental temperature variations, such as those that may occur under global climate change, can affect population levels. As an illustration, a logistic growth model with a stochastic, temperature-dependent predation term is examined, and the response to both average temperature levels and temperature variability is quantified. We infer, through simulations, that predation and prey abundance are strongly affected by mean temperature, temperature amplitudes, and increasing uncertainty in predicting temperature levels and variation, thus confirming many qualitative conclusions in the ecological literature. In particular, we show that increased temperature variability increases oscillations in the system and leads to increased probability of extinction of the prey.

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“…Our admitted bias is to use mechanistic physiologically based demographic models (PBDMs) that explicitly capture important aspects of the weather-driven biology and trophic interactions independent of distribution records [14][15][16][17]. The dynamics models and sub-model functions of PBDMs are largely the same across trophic levels and species, albeit with species-specific parameters, and when driven by daily weather or climate change scenarios predict prospectively the phenology and relative dynamics of a species' population across wide geographical areas [6,14,15].…”

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confidence: 99%