Metapopulation Dynamics of the Tephritid Fly Urophora cardui: An Evaluation of Incidence-Function Model Assumptions with Field Data

10.2307/5741 ◽  
1996 ◽  
Vol 65 (5) ◽  
pp. 621 ◽  
Author(s):  
Sabine Eber ◽  
Roland Brandl
Keyword(s):  
Field Data ◽  
Metapopulation Dynamics ◽  
Function Model ◽  
Incidence Function Model ◽  
Incidence Function

scholarly journals The Limiting Behaviour of Hanski's Incidence Function Metapopulation Model

2014 ◽  
Vol 51 (2) ◽  
pp. 297-316 ◽  
Author(s):  
R. McVinish ◽  
P. K. Pollett
Keyword(s):  
Markov Chain ◽  
Transition Probabilities ◽  
Metapopulation Model ◽  
Function Model ◽  
Discrete Time Markov Chain ◽  
Incidence Function Model ◽  
Incidence Function ◽  
Limiting Behaviour ◽  
Metapopulation Models ◽  
Near Extinction

Hanski's incidence function model is one of the most widely used metapopulation models in ecology. It models the presence/absence of a species at spatially distinct habitat patches as a discrete-time Markov chain whose transition probabilities are determined by the physical landscape. In this analysis, the limiting behaviour of the model is studied as the number of patches increases and the size of the patches decreases. Two different limiting cases are identified depending on whether or not the metapopulation is initially near extinction. Basic properties of the limiting models are derived.

The Quantitative Incidence Function Model and Persistence of an Endangered Butterfly Metapopulation

1996 ◽  
Vol 10 (2) ◽  
pp. 578-590 ◽  
Author(s):  
Ilkka Hanski ◽  
Atte Moilanen ◽  
Timo Pakkala ◽  
Mikko Kuussaari
Keyword(s):  
Function Model ◽  
Incidence Function Model ◽  
Incidence Function

scholarly journals The Limiting Behaviour of Hanski's Incidence Function Metapopulation Model

2014 ◽  
Vol 51 (02) ◽  
pp. 297-316 ◽  
Author(s):  
R. McVinish ◽  
P. K. Pollett
Keyword(s):  
Markov Chain ◽  
Transition Probabilities ◽  
Metapopulation Model ◽  
Function Model ◽  
Discrete Time Markov Chain ◽  
Incidence Function Model ◽  
Incidence Function ◽  
Limiting Behaviour ◽  
Metapopulation Models ◽  
Near Extinction

Hanski's incidence function model is one of the most widely used metapopulation models in ecology. It models the presence/absence of a species at spatially distinct habitat patches as a discrete-time Markov chain whose transition probabilities are determined by the physical landscape. In this analysis, the limiting behaviour of the model is studied as the number of patches increases and the size of the patches decreases. Two different limiting cases are identified depending on whether or not the metapopulation is initially near extinction. Basic properties of the limiting models are derived.

The stochastic resonance for the incidence function model of metapopulation

2017 ◽  
Vol 476 ◽  
pp. 70-83 ◽  
Author(s):  
Jiang-Cheng Li ◽  
Zhi-Wei Dong ◽  
Ruo-Wei Zhou ◽  
Yun-Xian Li ◽  
Zhen-Wei Qian
Keyword(s):  
Stochastic Resonance ◽  
Function Model ◽  
Incidence Function Model ◽  
Incidence Function

scholarly journals How to find a metapopulationThis review is one of a series dealing with some aspects of the impact of habitat fragmentation on animals and plants. This series is one of several virtual symposia focussing on ecological topics that will be published in the Journal from time to time.

2007 ◽  
Vol 85 (10) ◽  
pp. 1031-1048 ◽  
Author(s):  
D.A. Driscoll
Keyword(s):  
Native Species ◽  
Field Data ◽  
Spatial Dynamics ◽  
Metapopulation Dynamics ◽  
Data Sets ◽  
Fragmented Landscapes ◽  
Metapopulation Theory ◽  
Metapopulation Models ◽  
The Impact ◽  
Analytical Approaches

Where habitat loss and fragmentation is severe, many native species are likely to have reduced levels of dispersal between remnant populations. For those species to avoid regional extinction in fragmented landscapes, they must undergo some kind of metapopulation dynamics so that local extinctions are countered by recolonisation. The importance of spatial dynamics for regional survival means that research into metapopulation dynamics is essential. In this review I explore the approaches taken to examine metapopulation dynamics, highlight the analytical methods used to get the most information out of field data, and discover some of the major research gaps. Statistical models, including Hanski’s incidence function model (IFM) are frequently applied to presence–absence data, an approach that is often strengthened using long-term data sets that document extinctions and colonisations. Recent developments are making the IFM more biologically realistic and expanding the range of situations for which the model is relevant. Although accurate predictions using the IFM seem unlikely, it may be useful for ranking management decisions. A key weakness of presence–absence modelling is that the mechanisms underlying spatial dynamics remain inferential, so combining modelling approaches with detailed demographic research is warranted. For species where very large data sets cannot be obtained to facilitate statistical modelling, a demographic approach alone or with stochastic modelling may be the only viable research angle to take. Dispersal is a central process in metapopulation dynamics. Research combining mark–recapture or telemetry methods with model-selection procedures demonstrate that dispersal is frequently oversimplified in conceptual and statistical metapopulation models. Dispersal models like the island model that underlies classic metapopulation theory do not approximate the behaviour of real species in fragmented landscapes. Nevertheless, it remains uncertain if additional biological realism will improve predictions of statistical metapopulation models. Genetic methods can give better estimates of dispersal than direct methods and take less effort, so they should be routinely explored alongside direct ecological methods. Recent development of metacommunity theory (communities connected by dispersal) emphasises a range of mechanisms that complement metapopulation theory. Taking both theories into account will enhance interpretation of field data. The extent of metapopulation dynamics in human modified landscapes remains uncertain, but we have a powerful array of field and analytical approaches for reducing this knowledge gap. The most informative way forward requires that many species are studied in the same fragmented landscape by applying a selection of approaches that reveal complementary aspects of spatial dynamics.

scholarly journals Modelling the effect of habitat fragmentation on range expansion in a butterfly

2009 ◽  
Vol 276 (1661) ◽  
pp. 1421-1427 ◽  
Author(s):  
Robert J Wilson ◽  
Zoe G Davies ◽  
Chris D Thomas
Keyword(s):  
Climate Change ◽  
Habitat Fragmentation ◽  
Range Expansion ◽  
Metapopulation Model ◽  
Function Model ◽  
Test Species ◽  
Fragmented Landscapes ◽  
Incidence Function Model ◽  
Habitat Patches ◽  
Model Expansion

There is an increasing need for conservation programmes to make quantitative predictions of biodiversity responses to changed environments. Such predictions will be particularly important to promote species recovery in fragmented landscapes, and to understand and facilitate distribution responses to climate change. Here, we model expansion rates of a test species (a rare butterfly, Hesperia comma ) in five landscapes over 18 years (generations), using a metapopulation model (the incidence function model). Expansion rates increased with the area, quality and proximity of habitat patches available for colonization, with predicted expansion rates closely matching observed rates in test landscapes. Habitat fragmentation constrained expansion, but in a predictable way, suggesting that it will prove feasible both to understand variation in expansion rates and to develop conservation programmes to increase rates of range expansion in such species.

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