Figure - available from: Ecology Letters
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The dynamics of the lottery model (a) for a population of species 1 (shown in green) in the presence of a competitor (in purple) with and without the ability to detect an environmental cue that helps it predict favourable environmental conditions for germination (see Box 2 for model description). (b) The environment is simulated by taking draws from a normal distribution to set the conditions for each time step, without autocorrelation (μ = 0.5 and σ = 0.1, b). Each species (green and purple lines) has a different optimum environment modelled as Gaussian curves so that reproductive fitness decays with the distance of an environmental state from the optimum. (c) The varying environmental state, reproduction, and germination rates are simulated as a time series. When germination is informed (because 𝜌i(E|C) is high) then germination rates match closely to the reproduction rates in that year, as seen in the solid germination lines. When germination is not informed then there is no correlation between germination rates and reproduction; this is seen by comparing the dotted germination lines to the reproduction rates. See the Appendix S1 for simulation code.
Source publication
Information processing is increasingly recognized as a fundamental component of life in variable environments, including the evolved use of environmental cues, biomolecular networks, and social learning. Despite this, ecology lacks a quantitative framework for understanding how population, community, and ecosystem dynamics depend on information pro...
Citations
... temperature, humidity and light) or biotic (e.g. conspecifics' density, presence and density of heterospecifics like competitors, resource/prey or predators/parasites) [52] and can be acquired via visual, auditory, olfactory, chemical or haptic cues. Information can also be transmitted by ascendants [53,54] or other (unrelated) individuals. ...
Suitable conditions for species to survive and reproduce constitute their ecological niche, which is built by abiotic conditions and interactions with conspecifics and heterospecifics. Organisms should ideally assess and use information about all these environmental dimensions to adjust their dispersal decisions depending on their own internal conditions. Dispersal plasticity is often considered through its dependence on abiotic conditions or conspecific density and, to a lesser extent, with reference to the effects of interactions with heterospecifics, potentially leading to misinterpretation of dispersal drivers. Here, we first review the evidence for the effects of and the potential interplays between abiotic factors, biotic interactions with conspecifics and heterospecifics and phenotype on dispersal decisions. We then present an experimental test of these potential interplays, investigating the effects of density and interactions with conspecifics and heterospecifics on temperature-dependent dispersal in microcosms of Tetrahymena ciliates. We found significant differences in dispersal rates depending on the temperature, density and presence of another strain or species. However, the presence and density of conspecifics and heterospecifics had no effects on the thermal-dependency of dispersal. We discuss the causes and consequences of the (lack of) interplay between the different environmental dimensions and the phenotype for metacommunity assembly and dynamics.
This article is part of the theme issue ‘Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics’.
... In animals with overlapping generations and social learning, the effect on range shifts could be stronger and transgenerational. Learning about novel resources can be considered in the concept of the 'fitness value of information', which quantifies how species' per capita population growth rates can depend on the use of information in their environment (Usinowicz & O'Connor, 2023). ...
Human‐induced rapid environmental change (HIREC) is creating environments deviating considerably from natural habitats in which species evolved. Concurrently, climate warming is pushing species’ climatic envelopes to geographic regions that offer novel ecological conditions. The persistence of species is likely affected by the interplay between the degree of ecological novelty and phenotypic plasticity, which in turn may shape an organism's range‐shifting ability. Current modelling approaches that forecast animal ranges are characterized by a static representation of the relationship between habitat use and fitness, which may bias predictions under conditions imposed by HIREC. We argue that accounting for dynamic species‐resource relationships can increase the ecological realism of range shift predictions. Our rationale builds on the concepts of ecological fitting, the process whereby individuals form successful novel biotic associations based on the suite of traits they carry at the time of encountering the novel condition, and behavioural plasticity, in particular learning. These concepts have revolutionized our view on fitness in novel ecological settings, and the way these processes may influence species ranges under HIREC. We have integrated them into a model of range expansion as a conceptual proof of principle highlighting the potentially substantial role of learning ability in range shifts under HIREC.
... The fitness value of information has been recently proposed in an ecological setting, investigating the role of information in species growth using population distributions as proxy for probability measures [35]. We note that while the KW18 paper outlines the formalism for semantic information, there is yet to be any application of their framework in a biological setting, or indeed in any realistic individual organism model. ...
We explore the application of a theory of semantic information to the well-motivated problem of resource foraging. Semantic information is defined as the subset of correlations, which is here measured via the transfer entropy, between agent A and environment E that is necessary for the agent to maintain its viability V. Viability, in turn, is endogenously defined as opposed to the use of exogenous quantities like utility functions. In our model, the forager's movements are determined by its ability to measure, via a sensor, the presence of an individual unit of resource, while the viability function is its expected lifetime. Through “interventions”—scrambling the correlations between agent and environment by noising the sensor—we demonstrate the presence of a critical value of the noise parameter, ηc, above which the forager's expected lifetime is dramatically reduced. On the other hand, for η<ηc there is little to no effect on its ability to survive. We refer to this boundary as the semantic threshold, quantifying the subset of agent-environment correlations that the agent actually needs to maintain its desired state of staying alive. Each bit of information affects the agent's ability to persist both above and below the semantic threshold. Modeling the viability curve and its semantic threshold via forager and/or environment parameters, we show how the correlations are instantiated. Our work demonstrates the successful application of semantic information to a well-known agent-based model of biological and ecological interest. Additionally, we demonstrate that the concept of semantic thresholds may prove useful for understanding the role information plays in allowing systems to become autonomous agents.
