A robust-design formulation of the incidence function model of metapopulation dynamics applied to two species of rails

Ecology ◽  
2011 ◽  
Vol 92 (2) ◽  
pp. 462-474 ◽  
Author(s):  
Benjamin B. Risk ◽  
Perry de Valpine ◽  
Steven R. Beissinger
Keyword(s):  
Robust Design ◽  
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

Alternatives of Taguchi's Approach for Dynamic Robust Design Problems

1998 ◽  
Vol 05 (02) ◽  
pp. 115-131 ◽  
Author(s):  
Kwok-Leung Tsui
Keyword(s):  
Response Function ◽  
Robust Design ◽  
Dynamic Responses ◽  
Response Model ◽  
Function Model ◽  
Design Problems ◽  
Loss Model ◽  
Modeling Approaches ◽  
Noise Factors ◽  
Model Approach

Robust Design is an important method for improving product quality, manufacturability, and reliability at low cost. Most research in robust design has been focused on problems with static responses. This paper deals with the robust design problems with dynamic responses. The objective of the paper is to investigate and compare three modeling approaches: the loss model, the response function model, and the response model approaches. Taguchi16 proposes the loss model approach which models the loss measures as functions of the control factor effects. Miller and Wu10 propose the response function model approach which models the loss measures as functions of the effects of both control and noise factors. Tsui18 proposes the response model approach which directly models the response as a function of the effects of control, noise, and signal factors. In this paper, we identify and derive the relationships between the effect estimates of the three approaches and show that the loss model approach creates unnecessary biases for the factorial effect estimates and may lead to non-optimal solutions. The three modeling approaches are compared in a real example.

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.

Components of executive function model regional prefrontal volumes.

2019 ◽  
Vol 33 (7) ◽  
pp. 1007-1019 ◽  
Author(s):  
Ryan A. Mace ◽  
Abigail B. Waters ◽  
Kayle S. Sawyer ◽  
Taylor Turrisi ◽  
David A. Gansler
Keyword(s):  
Executive Function ◽  
Function Model
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