Climatic effects on niche evolution in a passerine bird clade depend on paleoclimate reconstruction method

Eyres, Alison; Eronen, Jussi T.; Hagen, Oskar; Böhning‐Gaese, Katrin; Fritz, Susanne A.

doi:10.1111/evo.14209

Cited by 9 publications

(8 citation statements)

References 93 publications

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“…For example, Rangel et al ( 33 ) looked exclusively at the evolution of Neotropical diversity, while the global study of Saupe et al ( 32 ), which considered paleoenvironmental change over the past 120 ky, successfully reconstructed the latitudinal diversity gradient but overpredicted diversity in Afrotropical moist forests; therefore, it did not capture the PDD. The inclusion of extended paleoenvironmental reconstructions from the Mesozoic (110 Ma) to the present day ( 41 , 49 , 51 ) in the present study may explain why the model was able to additionally predict pantropical diversity patterns.…”

Section: Resultsmentioning

confidence: 76%

Earth history events shaped the evolution of uneven biodiversity across tropical moist forests

Hagen

Skeels

Onstein

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Far from a uniform band, the biodiversity found across Earth’s tropical moist forests varies widely between the high diversity of the Neotropics and Indomalaya and the relatively lower diversity of the Afrotropics. Explanations for this variation across different regions, the “pantropical diversity disparity” (PDD), remain contentious, due to difficulty teasing apart the effects of contemporary climate and paleoenvironmental history. Here, we assess the ubiquity of the PDD in over 150,000 species of terrestrial plants and vertebrates and investigate the relationship between the present-day climate and patterns of species richness. We then investigate the consequences of paleoenvironmental dynamics on the emergence of biodiversity gradients using a spatially explicit model of diversification coupled with paleoenvironmental and plate tectonic reconstructions. Contemporary climate is insufficient in explaining the PDD; instead, a simple model of diversification and temperature niche evolution coupled with paleoaridity constraints is successful in reproducing the variation in species richness and phylogenetic diversity seen repeatedly among plant and animal taxa, suggesting a prevalent role of paleoenvironmental dynamics in combination with niche conservatism. The model indicates that high biodiversity in Neotropical and Indomalayan moist forests is driven by complex macroevolutionary dynamics associated with mountain uplift. In contrast, lower diversity in Afrotropical forests is associated with lower speciation rates and higher extinction rates driven by sustained aridification over the Cenozoic. Our analyses provide a mechanistic understanding of the emergence of uneven diversity in tropical moist forests across 110 Ma of Earth’s history, highlighting the importance of deep-time paleoenvironmental legacies in determining biodiversity patterns.

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Section: Resultsmentioning

confidence: 76%

Earth history events shaped the evolution of uneven biodiversity across tropical moist forests

Hagen

Skeels

Onstein

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…The quality of the outputs of simulation models such as gen3sis hinges on accurate and relevant reconstructions of past environmental conditions [ 117 ]. Conditions during the Cenozoic (i.e., 65 Ma until the present) are considered key for the diversification of the current biota [ 118 ], and the Cenozoic is the period during which the modern LDG is expected to have been formed [ 119 ].…”

Section: Case Study: the Emergence Of The Ldg In The Cenozoicmentioning

confidence: 99%

gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth’s biodiversity

et al. 2021

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Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary, and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially explicit, eco-evolutionary engine with a modular implementation that enables the modeling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatiotemporally dynamic landscapes. Modeled processes can include species’ abiotic tolerances, biotic interactions, dispersal, speciation, and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, β, and γ diversity, species ranges, ecological traits, and phylogenies, emerge as simulations proceed. As an illustration, we examine alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth’s Cenozoic era. Our exploratory simulations simultaneously produce multiple realistic biodiversity patterns, such as the LDG, current species richness, and range size frequencies, as well as phylogenetic metrics. The model engine is open source and available as an R package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a key toward a numeric, interdisciplinary, and mechanistic understanding of the physical and biological processes that shape Earth’s biodiversity.

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“…Although these studies provide insight into Earth's biodiversity, they also involve biases regarding macroevolutionary estimations (Coiro et al 2019, Louca and Pennell 2020). For example, the absence of well‐sampled fossil data for most taxonomic groups restricts the possibilities of inferring and validating historical processes (Hagen et al 2018, Eyres et al 2021). Moreover, past environmental fluctuations are often spatially confounded with current environmental conditions, challenging correlative approaches (Hagen et al 2021a).…”

Section: Diverse Models Of Biodiversitymentioning

confidence: 99%

“…Deriving paleo‐indicators from climate, however, has major limitations and can result in inaccuracies. For example, the delimitation of categorical climate variables containing sharp borders might be arbitrary or circular if dependent on fossil data (Eyres et al 2021). Nevertheless, it is expected that the shape and position of temperature belts suffice to capture large‐scale temperature gradients, such as the LDG (Leprieur et al 2016, Donati et al 2019, Gaboriau et al 2019, Saupe et al 2019b, b, Hagen et al 2021a, b).…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

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

Hagen

2022

Ecography

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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.

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Climatic effects on niche evolution in a passerine bird clade depend on paleoclimate reconstruction method

Cited by 9 publications

References 93 publications

Earth history events shaped the evolution of uneven biodiversity across tropical moist forests

Earth history events shaped the evolution of uneven biodiversity across tropical moist forests

gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth’s biodiversity

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

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