Evidence map›Paper›PMID 31615360›Full record

ArticleProceedings. Biological sciences2019

Predicting population responses to environmental change from individual-level mechanisms: towards a standardized mechanistic approach.

A S A Johnston, R J Boyd, J W Watson, A Paul, L C Evans, E L Gardner, V L Boult

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

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  7. Simulating the efficacy of wolf-dog hybridization management with individual-based modeling.Conservation biology : the journal of the Society for Conservation Biology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

A S A JohnstonSchool of Biological Sciences, University of Reading, Reading RG6 6AH, UK.
R J BoydSchool of Archaeology, Geography and Environmental Science, University of Reading, Reading RG6 6AX, UK.
J W WatsonSchool of Biological Sciences, University of Reading, Reading RG6 6AH, UK.
A PaulSchool of Archaeology, Geography and Environmental Science, University of Reading, Reading RG6 6AX, UK.
L C EvansSchool of Biological Sciences, University of Reading, Reading RG6 6AH, UK.
E L GardnerSchool of Biological Sciences, University of Reading, Reading RG6 6AH, UK.
V L BoultSchool of Biological Sciences, University of Reading, Reading RG6 6AH, UK.

Funding

Biotechnology and Biological Sciences Research Council BB/N504129/1Biotechnology and Biological Sciences Research Council BB/R00580X/1
6 · The paper itself

Abstract

Animal populations will mediate the response of global biodiversity to environmental changes. Population models are thus important tools for both understanding and predicting animal responses to uncertain future conditions. Most approaches, however, are correlative and ignore the individual-level mechanisms that give rise to population dynamics. Here, we assess several existing population modelling approaches and find limitations to both 'correlative' and 'mechanistic' models. We advocate the need for a standardized mechanistic approach for linking individual mechanisms (physiology, behaviour, and evolution) to population dynamics in spatially explicit landscapes. Such an approach is potentially more flexible and informative than current population models. Key to realizing this goal, however, is overcoming current data limitations, the development and testing of eco-evolutionary theory to represent interactions between individual mechanisms, and standardized multi-dimensional environmental change scenarios which incorporate multiple stressors. Such progress is essential in supporting environmental decisions in uncertain future conditions.

Indexed as

Population DynamicsAnimalsBiodiversityBiological EvolutionClimate ChangeEcosystemModels, Biologicalbehaviourenvironmental changeevolutionindividualsphysiologypopulation models

Identifiers

PMID31615360
PMCPMC6834044

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Registered trials

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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.