Pioneer naturalists such as Whewell, Lyell, Humboldt, Darwin and Wallace acknowledged the interactions between ecological and evolutionary forces, as well as the roles of continental movement, mountain formation and climate variations, in shaping biodiversity patterns. Recent developments in computer modelling and paleo-environmental reconstruction have made it possible for scientists to study in silico how biodiversity emerges from eco-evolutionary and environmental dynamic processes and their interactions. Simulating emergent biodiversity enables the experimentation of multiple interconnected hypotheses in a largely fragmented scientific landscape, with the final objective of successfully approximating natural mechanisms (i.e. hypothetical spatio–temporally unrestricted generalizations that hold across multiple empirical biodiversity patterns). This new interdisciplinary approach opens unprecedented scientific pathways, facilitating the communication and contemplation of causal implications of complex eco-evolutionary and environmental interactions. In this review I provide a comprehensive overview of the available population-based spatially explicit mechanistic eco-evolutionary models (MEEMs) that rely on paleo-environmental reconstructions, critically discussing their relevance and limitations for our understanding of biodiversity. To achieve this, I first introduce diverse biodiversity models and contextualize MEEMs. Second, I define MEEMs and synthesize the major insights from studies using MEEMs combined with deep-time environmental dynamics (> 0.1 Ma). Lastly, I discuss the challenges and perspectives of solving long-standing biodiversity enigmas by coupling eco-evolutionary mechanisms with deep-time environmental dynamics. Studies show that linking dynamic environments and eco-evolutionary processes is necessary to reproduce multiple large-scale biodiversity patterns simultaneously. Mechanisms related to adaptations (e.g. niche evolution), dispersal abilities and other eco-evolutionary interactions (e.g. those resulting in speciation or extinction events) show universal importance, although their signatures across spatial and temporal scales remain largely unknown. Investigations with MEEMS spanning multiple levels of complexity in space and time foster interdisciplinary cooperation across the natural sciences and show promise for solving some of the enigmas in Earth's biodiversity. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/ecog.06132

Coupling eco‐evolutionary mechanisms with deep‐time environmental dynamics to understand biodiversity patterns

Long-distance dispersal (LDD) beyond the range of a species is an important driver of ecological and evolutionary patterns, but insufficient attention has been given to postdispersal establishment. In this review, we summarize current knowledge of the post-LDD establishment phase in plant colonization, identify six key determinants of establishment success, develop a general quantitative framework for post-LDD establishment, and address the major challenges and opportunities in future research. These include improving detection and understanding of LDD using novel approaches, investigating mechanisms determining post-LDD establishment success using mechanistic modeling and inference, and comparison of establishment between past and present. By addressing current knowledge gaps, we aim to further our understanding of how LDD affects plant distributions, and the long-term consequences of LDD events. 

/pdf/the-establishment-of-plants-following-long-distance-mrsrhx0y.pdf

The establishment of plants following long-distance dispersal.

Effective coding is fundamental to the study of biology. Computation underpins most research, and reproducible science can be promoted through clean coding practices. Clean coding is crafting code design, syntax and nomenclature in a manner that maximizes the potential to communicate its intent with other scientists. However, computational biologists are not software engineers, and many of our coding practices have developed ad hoc without formal training, often creating difficult-to-read code for others. Hard-to-understand code can thus be limiting our efficiency and ability to communicate as scientists with one another. The purpose of this paper is to provide a primer on some of the practices associated with crafting clean code by synthesizing a transformative text in software engineering along with recent articles on coding practices in computational biology. We review past recommendations to provide a series of best practices that transform coding into a human-accessible form of communication. Three common themes shared in this synthesis are the following: (a) code has value and you are responsible for its organization to enable clear communication, (b) use a formatting style to guide writing code that is easily understandable and consistent and (c) apply abstraction to emphasize important elements and declutter. While many of the provided practices and recommendations were developed with computational biologists in mind, we believe there is wider applicability to any biologist undertaking work in data management or statistical analyses. Clean code is thus a crucial step forward in resolving some of the crisis in reproducibility for science. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.13961

A call for clean code to effectively communicate science

The study of eco‐evolutionary dynamics, that is of the intertwinning between ecological and evolutionary processes when they occur at comparable time scales, is of growing interest in the current context of global change. However, many eco‐evolutionary studies overlook the role of interindividual interactions, which are hard to predict and yet central to selective values. Here, we aimed at putting forward models that simulate interindividual interactions in an eco‐evolutionary framework: the demo‐genetic agent‐based models (DG‐ABMs). Being demo‐genetic, DG‐ABMs consider the feedback loop between ecological and evolutionary processes. Being agent‐based, DG‐ABMs follow populations of interacting individuals with sets of traits that vary among the individuals. We argue that the ability of DG‐ABMs to take into account the genetic heterogeneity—that affects individual decisions/traits related to local and instantaneous conditions—differentiates them from analytical models, another type of model largely used by evolutionary biologists to investigate eco‐evolutionary feedback loops. Based on the review of studies employing DG‐ABMs and explicitly or implicitly accounting for competitive, cooperative or reproductive interactions, we illustrate that DG‐ABMs are particularly relevant for the exploration of fundamental, yet pressing, questions in evolutionary ecology across various levels of organization. By jointly modelling the effects of management practices and other eco‐evolutionary processes on interindividual interactions and population dynamics, DG‐ABMs are also effective prospective and decision support tools to evaluate the short‐ and long‐term evolutionary costs and benefits of management strategies and to assess potential trade‐offs. Finally, we provide a list of the recent practical advances of the ABM community that should facilitate the development of DG‐ABMs.

/pdf/importance-of-interindividual-interactions-in-eco-2c1ao87e.pdf

Importance of interindividual interactions in eco‐evolutionary population dynamics: The rise of demo‐genetic agent‐based models

Introgressive hybridization is a process that enables gene flow across species barriers through the backcrossing of hybrids into a parent population. This may make genetic material, potentially including relevant environmental adaptations, rapidly available in a gene pool. Consequently, it has been postulated to be an important mechanism for enabling evolutionary rescue, that is the recovery of threatened populations through rapid evolutionary adaptation to novel environments. However, predicting the likelihood of such evolutionary rescue for individual species remains challenging. Here, we use the example of Zosterops silvanus, an endangered East African highland bird species suffering from severe habitat loss and fragmentation, to investigate whether hybridization with its congener Zosterops flavilateralis might enable evolutionary rescue of its Taita Hills population. To do so, we employ an empirically parameterized individual‐based model to simulate the species' behaviour, physiology and genetics. We test the population's response to different assumptions of mating behaviour and multiple scenarios of habitat change. We show that as long as hybridization does take place, evolutionary rescue of Z. silvanus is likely. Intermediate hybridization rates enable the greatest long‐term population growth, due to trade‐offs between adaptive and maladaptive introgressed alleles. Habitat change did not have a strong effect on population growth rates, as Z. silvanus is a strong disperser and landscape configuration is therefore not the limiting factor for hybridization. Our results show that targeted gene flow may be a promising avenue to help accelerate the adaptation of endangered species to novel environments, and demonstrate how to combine empirical research and mechanistic modelling to deliver species‐specific predictions for conservation planning.

https://helda.helsinki.fi/bitstream/10138/347696/1/Evolutionary_Applications_2022_Vedder_Hybridization_may_aid_evolutionary_rescue_of_an_endangered_East_African.pdf

Hybridization may aid evolutionary rescue of an endangered East African passerine

Temporal environmental variation may impose differential selection on both genomic and ecological traits

The success of species invasions depends on multiple factors acting over the four invasion stages transport, colonisation, establishment, and landscape spread. Each of these stages is influenced simultaneously by particular species traits and abiotic factors. While the importance of many of these determinants has already been investigated in relative isolation, they are rarely studied in combination and even then mostly ignore the final phase, i.e., landscape spread. Here we address this shortcoming by exploring the effect of both species traits and abiotic factors on the success of invasions using an individual-based mechanistic model, and relate those factors to the stages of invasion. This approach enables us to explicitly control abiotic factors (temperature as surrogate for productivity, disturbance and propagule pressure) as well as to monitor whole-community trait distributions of environmental adaptation, mass and dispersal abilities. We simulated introductions of plant individuals to an oceanic island to assess which abiotic factors and species traits contribute to invasion success. We found that the most influential factors were higher propagule pressure and a particular set of traits. This invasion trait syndrome was characterized by a relative similarity in functional traits of invasive species to natives, while invasives had on average higher environmental tolerances, higher body mass and increased dispersal abilities, i.e., were more generalist and dispersive. Our results highlight the importance in management practice of reducing the import of alien species, especially from similar habitats.

/pdf/effects-of-species-traits-and-abiotic-factors-during-the-2u1id1kom7.pdf

Effects of species traits and abiotic factors during the stages of plant invasions

Dealing with software complexity in individual-based models

AquaCrop.jl: A Process-Based Model of Crop Growth

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