Evidence map›Paper›PMID 35467160›Full record

ArticleJournal of mathematical biology2022

The effects of random and seasonal environmental fluctuations on optimal harvesting and stocking.

Alexandru Hening, Ky Quan Tran, Sergiu C Ungureanu

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Article in Journal of mathematical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Alexandru HeningDepartment of Mathematics, Texas A &M University, Mailstop 3368, College Station, TX, 77843-3368, USA. ahening@tamu.edu.
Ky Quan TranDepartment of Mathematics and Statistics, The State University of New York in Korea, 119 SongdoMoonhwa-Ro, Yeonsu-Gu, Incheon, 21985, Korea.
Sergiu C UngureanuDepartment of Economics City, University of London, Northampton Square, London, EC1V 0HB, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We analyze the harvesting and stocking of a population that is affected by random and seasonal environmental fluctuations. The main novelty comes from having three layers of environmental fluctuations. The first layer is due to the environment switching at random times between different environmental states. This is similar to having sudden environmental changes or catastrophes. The second layer is due to seasonal variation, where there is a significant change in the dynamics between seasons. Finally, the third layer is due to the constant presence of environmental stochasticity-between the seasonal or random regime switches, the species is affected by fluctuations which can be modelled by white noise. This framework is more realistic because it can capture both significant random and deterministic environmental shifts as well as small and frequent fluctuations in abiotic factors. Our framework also allows for the price or cost of harvesting to change deterministically and stochastically, something that is more realistic from an economic point of view. The combined effects of seasonal and random fluctuations make it impossible to find the optimal harvesting-stocking strategy analytically. We get around this roadblock by developing rigorous numerical approximations and proving that they converge to the optimal harvesting-stocking strategy. We apply our methods to multiple population models and explore how prices, or costs, and environmental fluctuations influence the optimal harvesting-stocking strategy. We show that in many situations the optimal way of harvesting and stocking is not of threshold type.

Indexed as

Models, BiologicalPopulation DynamicsSeasonsStochastic ProcessesControlled diffusionDensity-dependent priceHarvestingSeasonalityStochastic environmentSwitching environment

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.