The spatial population dynamics of insects exploiting a patchy food resource

Dempster, J. P.; Atkinson, David; Cheesman, O. D.

doi:10.1007/bf00328370

Cited by 19 publications

(10 citation statements)

References 23 publications

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“…The fact that genetic differentiation among M. nausithous host populations does not increase with isolation (at least at the scale of our metapopulation) suggests that effective gene flow is less influenced by isolation at this lower trophic level where populations are generally larger. According to Cronin & Reeve (2005), spatially discrete host–parasitoid systems may in fact be quite variable in their genetic coherence, ranging from classic metapopulations with high connectedness (Lei & Hanski 1997; Johannesen & Seitz 2003) to subdivided populations where gene flow is constrained (Dempster et al . 1995).…”

Section: Discussionmentioning

confidence: 99%

Population structure of a large blue butterfly and its specialist parasitoid in a fragmented landscape

et al. 2007

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Habitat fragmentation may interrupt trophic interactions if herbivores and their specific parasitoids respond differently to decreasing connectivity of populations. Theoretical models predict that species at higher trophic levels are more negatively affected by isolation than lower trophic level species. By combining ecological data with genetic information from microsatellite markers we tested this hypothesis on the butterfly Maculinea nausithous and its specialist hymenopteran parasitoid Neotypus melanocephalus. We assessed the susceptibility of both species to habitat fragmentation by measuring population density, rate of parasitism, overall genetic differentiation (theta(ST)) and allelic richness in a large metapopulation. We also simulated the dynamics of genetic differentiation among local populations to asses the relative effects of migration rate, population size, and haplodiploid (parasitoid) and diploid (host) inheritance on metapopulation persistence. We show that parasitism by N. melanocephalus is less frequent at larger distances to the nearest neighbouring population of M. nausithous hosts, but that host density itself is not affected by isolation. Allelic richness was independent of isolation, but the mean genetic differentiation among local parasitoid populations increased with the distance between these populations. Overall, genetic differentiation in the parasitoid wasp was much greater than in the butterfly host and our simulations indicate that this difference is due to a combination of haplodiploidy and small local population sizes. Our results thus support the hypothesis that Neotypus parasitoid wasps are more sensitive to habitat fragmentation than their Maculinea butterfly hosts.

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Section: Discussionmentioning

confidence: 99%

Population structure of a large blue butterfly and its specialist parasitoid in a fragmented landscape

et al. 2007

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“…that misleadingly indicate the presence of density dependence. Ecologists have been aware of this problem for some time (Bulmer 1975; Reddingius & den Boer 1989; Dempster et al . 1995).…”

Section: Introductionmentioning

confidence: 99%

Census error and the detection of density dependence

Freckleton

Watkinson

Green³

et al. 2006

Journal of Animal Ecology

261

335

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Summary1. Studies aiming to identify the prevalence and nature of density dependence in ecological populations have often used statistical analysis of ecological time-series of population counts. Such time-series are also being used increasingly to parameterize models that may be used in population management. 2. If time-series contain measurement errors, tests that rely on detecting a negative relationship between log population change and population size are biased and prone to spuriously detecting density dependence (Type I error). This is because the measurement error in density for a given year appears in the corresponding change in population density, with equal magnitude but opposite sign. 3. This effect introduces bias that may invalidate comparisons of ecological data with density-independent time-series. Unless census error can be accounted for, time-series may appear to show strongly density-dependent dynamics, even though the densitydependent signal may in reality be weak or absent. 4. We distinguish two forms of census error, both of which have serious consequences for detecting density dependence. 5. First, estimates of population density are based rarely on exact counts, but on samples. Hence there exists sampling error, with the level of error depending on the method employed and the number of replicates on which the population estimate is based. 6. Secondly, the group of organisms measured is often not a truly self-contained population, but part of a wider ecological population, defined in terms of location or behaviour. Consequently, the subpopulation studied may effectively be a sample of the population and spurious density dependence may be detected in the dynamics of a single subpopulation. In this case, density dependence is detected erroneously, even if numbers within the subpopulation are censused without sampling error. 7. In order to illustrate how process variation and measurement error may be distinguished we review data sets (counts of numbers of birds by single observers) for which both census error and long-term variance in population density can be estimated. 8. Tests for density dependence need to obviate the problem that measured population sizes are typically estimates rather than exact counts. It is possible that in some cases it may be possible to test for density dependence in the presence of unknown levels of census error, for example by uncovering nonlinearities in the density response. However, it seems likely that these may lack power compared with analyses that are able to explicitly include census error and we review some recently developed methods.

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“…Gall‐forming insects and their natural enemies have played an important role in testing general theories in insect population dynamics such as the regulation of host abundance, and the effect of dispersal on host–parasitoid dynamics (Varley, 1947; Ehler & Kinsey, 1991; Dempster et al ., 1995a, b; Latto & Briggs, 1995; Price et al ., 1995; Briggs & Latto, 2000; Briggs & Latto, 2001). In the models used to describe these communities, variation in susceptibility of individual galls to parasitism or predation has largely been omitted despite the fact that this factor is related to gall size (Weis et al ., 1985; Plakidas & Weis, 1994; Redfearn & Cameron, 1994), morphology (Plantard & Hochberg, 1998), or toughness (Craig et al ., 1990).…”

Section: Introductionmentioning

confidence: 99%

Gall size determines the structure of theRabdophaga strobiloideshost–parasitoid community

Hezewijk

Roland

2003

Ecological Entomology

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Abstract. 1. The relationship between gall size and mortality of the willow pinecone gall midge Rabdophaga strobiloides (Diptera: Cecidomyiidae) was examined by determining the fate of all galls in a 30‐ha area in central Alberta, Canada over 4 years. It was found that gall size has a large effect on the type and intensity of mortality experienced by the gall midge, and consequently this factor has the potential to influence the dynamics of the host–parasitoid interaction through the creation of phenotypic refuges. 2. Total midge mortality ranged from 51% to 78% over the course of the study and was dominated by parasitism by Torymus cecidomyiae (Hymenoptera: Torymidae) and Gastrancistrus sp. (Hymenoptera: Pteromalidae) as well as predation by birds. Gall size had a strong, non‐linear effect on the attack rates of each of these natural enemies. 3. Birds attacked the smallest size classes. Torymus cecidomyiae preferentially attacked medium diameter galls and thus avoided predation by birds in smaller galls. Gastrancistrus sp. preferentially attacked the largest galls and consequently suffered lower rates of predation by both T. cecidomyiae and birds. 4. This study emphasises the importance of understanding the interactions among mortality factors in order to describe adequately the susceptibility of R. strobiloides to parasitism and predation, and ultimately its population dynamics.

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The spatial population dynamics of insects exploiting a patchy food resource

Cited by 19 publications

References 23 publications

Population structure of a large blue butterfly and its specialist parasitoid in a fragmented landscape

Population structure of a large blue butterfly and its specialist parasitoid in a fragmented landscape

Census error and the detection of density dependence

Gall size determines the structure of theRabdophaga strobiloideshost–parasitoid community

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