A Mathematical Model for the Effect of Densities of Attacked and Attacking Species on the Number Attacked

Watt, Kenneth E. F.

doi:10.4039/ent91129-3

Cited by 210 publications

(91 citation statements)

References 35 publications

(33 reference statements)

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“…Furthermore, flower mortality caused by all of the aforementioned insects showed a tendency to increase as flower densities per branch increased. This is to be expected because, in general, the number of prey attacked increases with both the density of predators and the prey (Watt, 1959). At areas 1, 2, 3 flower mortality was caused by all of the aforementioned insects.…”

Section: Mortality Factorsmentioning

confidence: 91%

The role of insects in the dynamics of cone production of red pine

Mattson

1978

Oecologia

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The mortality of red pine cones was studied for several consecutive years using life tables in eight different stands in the northern Great Lakes States. Usually less than 20% of a cohort of female flowers survived to maturity. Insects were the main source of mortality and were primarily responsible for annual fluctuations in the abundance of mature cones. Insects were probably also responsible, along with late summer weather (especially precipitation), for influencing the rate of initiation and differentiation of flower bud primordia, as shown in a model of the flowering-cone production processes. These studies suggest that the devastation of developing cone crops by insects may actually enhance flower primordia production and thus the future abundance of feeding and breeding sites for cone insects.

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Section: Mortality Factorsmentioning

confidence: 91%

The role of insects in the dynamics of cone production of red pine

Mattson

1978

Oecologia

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“…A2) is a modified form of Watt's (1959) model (see Gutierrez 1996, p. 81), and for olive fly we use the integrated parasitoid form that allows multiple oviposition in the same fruit (i.e. the metabolic pool model; see Petrusewicz and MacFayden 1970;de Wit and Goudriaan 1978).…”

Section: Appendix 1: Model Overviewmentioning

confidence: 99%

Effects of climate warming on Olive and olive fly (Bactrocera oleae (Gmelin)) in California and Italy

Gutiérrez

Ponti

Cossu³

2009

Climatic Change

109

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Climate change is expected to alter the geographic distribution and abundance of many species. Here we examine the potential effects of climate warming on olive (Olea europaea) and olive fly (Bactrocera oleae) across the ecological zones of Arizona-California (AZ-CA) and Italy. A weather-driven physiologically-based demographic model was developed from the extensive literature and used to simulate the phenology, growth and population dynamics of both species. Observed weather for several years from 151 sites in AZ-CA and 84 sites in Italy were used in the study. Three climate-warming scenarios were developed by increasing observed average daily temperature 1 • , 2 • and 3 • C. Predictions of bloom dates, yield, total fly pupae and percent infestation were mapped using GRASS GIS. Linear multiple-regression was used to estimate the effects of weather on yield and fly abundance. Olive has a much wider temperature range of favorability than olive fly. The model predicted the present distributions of both species and gave important insights on the potential effects of climate warming on them. In AZ-CA, climate warming is expected to 196 Climatic Change (2009) 95:195-217 contract the range of olive in southern desert areas, and expand it northward and along coastal areas. Olive fly is currently limited by high temperature in the southern part of its range and by cold weather in northern areas. Climate warming is expected to increase the range of olive fly northward and in coastal areas, but decrease it in southern areas. In Italy, the range of olive is expected to increase into currently unfavorable cold areas in higher elevations in the Apennine Mountains in central Italy, and in the Po Valley in the north. Climate warming is expected to increase the range of olive fly northward throughout most of Italy.

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“…For example, Khasminskiǐ and Klebaner in [10] gave an analysis of Lotka-Volterra system with small random perturbations, Ji and Jiang in [9] analyzed a stochastic predator-prey system with BeddingtonDeAngelis functional response, Bandyopadhyay and Chattopadhyay in [2] studied the effect of environmental fluctuations on a ratio-dependent predator-prey system, Mandal and Banerjee in [13] investigated stochastic persistence and stationary distribution of a Holling-Tanner type prey-predator model, Dung in [5] provided an explicit solution to delayed logistic equations with fractional noise, etc. proposed by Watt [23], which can be described by the following differential equations…”

Section: Introductionmentioning

confidence: 99%