Compatible ecological niche signals between biological and archaeological datasets for late‐surviving Neandertals

Bible, Rachael C; Peterson, A. Townsend

doi:10.1002/ajpa.23482

Cited by 4 publications

(6 citation statements)

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“…The BAM framework provides a way to operationalize the niche concept in the geographical space, making it appealing for inferring the distribution of a species through SDMs. Since its introduction in 2005, the BAM framework has become a mainstay in correlative SDMs exercises and has been applied in multiple scientific fields (e.g., Bible & Peterson, 2018; Escobar & Craft, 2016; Franklin, 2023).…”

Section: Incorporating Hypothesized Causal Relationships Into Sdmsmentioning

confidence: 99%

Towards causal relationships for modelling species distribution

Da Re,

Tordoni,

Lenoir

et al. 2023

Journal of Biogeography

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AimUnderstanding the processes underlying the distribution of species through space and time is fundamental in several research fields spanning from ecology to spatial epidemiology. Correlative species distribution models rely on the niche concept to infer or explain the distribution of species, though often focusing only on the abiotic component of the niche (e.g. temperature, precipitation), without clear causal links to the biology of the species under investigation. This might result in an oversimplification of the complex niche hypervolume, resulting in a single model formula whose estimates and predictions lack ecological realism.LocationNot applicable.Time PeriodNot applicable.Major Taxa StudiedVirtual species.Materials and MethodsWe believe that a causal perspective associated with a finer definition of the modelling target is necessary to develop more ecologically realistic outputs. Here, we propose to infer the geographical distribution of a species by applying the modelling relation approach, a causal conceptual framework developed by the theoretical biologist Robert Rosen, which can be formalized through structural equation modelling.ResultsOur findings suggest that building a model relying on a strong conceptual basis improves the stability of the estimated model's coefficients, without necessarily increasing the predictive accuracy metrics of the model.Main ConclusionsIncluding causal processes underlying the spatial distribution of species into an inferential formal system highlights the methodological steps where uncertainty can arise and results in model outputs which are tightly linked to the ecology of the target species.

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Section: Incorporating Hypothesized Causal Relationships Into Sdmsmentioning

confidence: 99%

Towards causal relationships for modelling species distribution

Da Re,

Tordoni,

Lenoir

et al. 2023

Journal of Biogeography

View full text Add to dashboard Cite

show abstract

“…In fact, the BAM framework provides a way to operationalize the niche concept in the geographical space, making it appealing for inferring the distribution of a species through SDMs. Since its introduction in 2005, the BAM framework has become a mainstay in correlative SDMs exercises and has been applied in multiple scientific fields (e.g., Escobar and Craft, 2016;Bible and Peterson, 2018;Franklin 2023).…”

Section: Incorporating Hypothesized Causal Relationships Into Sdmsmentioning

confidence: 99%

Towards causal relationships for modelling species distribution

Da Re,

Tordoni,

Lenoir

et al. 2023

Preprint

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1. Understanding the processes underlying the distribution of species through space and time is fundamental in several research fields spanning from ecology to spatial epidemiology. Correlative species distribution models (SDMs) involve popular statistical tools to infer species geographical distribution thanks to spatiotemporally explicit observations of species occurrences coupled with a set of environmental predictors. 2. So-called SDMs rely on the niche concept to infer or explain the distribution of species, though often focusing only on the abiotic component of the niche (e.g., temperature, precipitation), without clear causal links to the biology of species under investigation. This might result in an over-simplification of the complex niche hypervolume, resulting in a single model formula whose estimates and predictions lack ecological realism. 3. We believe that a causal perspective associated with a finer definition of the modelling target is necessary to develop ecologically more realistic outputs. Here, we propose to infer the geographical distribution of a species by applying the modelling relation approach, a causal conceptual framework developed by the theoretical biologist Robert Rosen, which can be formalized through structural equation modelling (SEM). 4. Implementing the modelling relation into SDMs would improve the inclusion of the causal processes underlying the spatial distribution of species into an inferential formal system, potentially highlighting the methodological steps where uncertainty arises and eventually resulting in model outputs which are tightly linked to the ecology of the target species.

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“…1, Table 3). Known human "land preference" sites for model validation purposefully included all potential indicators (fossil or archaeological) to maintain the most comprehensive indicators of human presence on the landscape (Bible and Peterson, 2018).…”

Section: Tablementioning

confidence: 99%

“…Recently, however, ENM has been applied to the study of human evolution to create predictions of hominin land preference, site use during periods of intense climate fluctuations, and dispersal pathways in relatively extreme climates. Studies on Neandertals in Europe (Bible, 2016;Bible and Peterson, 2018) and early modern humans in the foothills of the Altai mountains (Beeton et al, 2014;Glantz et al, 2018) have provided evidence that some of the most important variables for predicting hominin niches and dispersal patterns are variations in temperature and precipitation (often the primary abiotic variables used in ENM analyses).…”

Section: Introductionmentioning

confidence: 99%

“…However, the long-standing temperate environment of Australasia fundamentally differs from other regions where ENM has been applied in human evolutionary studies. Without extreme variation in temperature and precipitation, the most important variables in predicting human land preference and dispersal patterns in these models, (Beeton et al, 2014;Bible, 2016;Bible and Peterson, 2018;Glantz et al, 2018) it is unclear how individual abiotic variables (e.g., temperature, rainfall, landform data, aridity, etc.) or biotic factors (competition, distribution of vegetation and prey species, etc.)…”

Section: Introductionmentioning

confidence: 99%

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