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Record W4224913921 · doi:10.1002/ecs2.4016

Integrating historical observations alters projections of eastern North American spruce–fir habitat under climate change

2022· article· en· W4224913921 on OpenAlexaboutno aff
Caitlin M. Andrews, Jane R. Foster, Aaron R. Weiskittel, Anthony W. D’Amato, Erin Simons‐Legaard

Bibliographic record

VenueEcosphere · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersNational Science Foundation of Sri LankaNational Aeronautics and Space Administration
KeywordsClimate changeEcologyAbundance (ecology)Species distributionRange (aeronautics)HabitatPrecipitationGeographyDisturbance (geology)Environmental sciencePhysical geographyEcosystemClimatologyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Spruce–fir ( Picea–Abies ) forests of the North American Acadian Forest Region are at risk of disappearing from the northeastern United States and Canada due to climate change. Species distribution models (SDMs) have been used to predict changes in this critical transitional ecosystem in the past, but none have addressed how seasonal patterns of temperature and precipitation interact to influence tree species abundance. Inferences have also been limited by contemporary inventory data that could not fully characterize species ranges because they either, (1) only sampled species occurrence after large‐scale human disturbance and settlement, or (2) did not span critical geopolitical boundaries (e.g., the US–Canadian border) that intersect the focal species' range(s). Here, we built new SDM models to better assess the bioclimatic distribution of four spruce–fir species and to test the importance of seasonal climate interactions. We compiled an extensive database of tree occurrence and abundance from recent (~1955–2012) and historical time periods (1623–1869) to model current species distributions and to predict how these might change under future climate. We found that including historical tree data in our SDMs revealed previously unrecognized suitable habitat along the southern edge of species' contemporary ranges. Random forest models predicted occurrence with high accuracy (area under receiver operator curve >0.98), and the seasonal climate variables that emerged as most important for these cold‐adapted species all included interactions that reflected sensitivity to colder temperatures, and preferences for wet weather concentrated in the winter months. Under moderate climate warming (representative concentration pathway 6.0), the northeastern United States retained additional suitable habitat when historical data were included through 2060 for three of the four species: red spruce ( Picea rubens ), black spruce ( Picea mariana ), and balsam fir ( Abies balsamea ), while white spruce ( Picea glauca ) habitat contracted into Canada. In contrast, future predictions from models that used contemporary data alone forecast extirpation for all four species from the northeastern United States. Overall, these findings highlight that prediction of species ranges in transitional ecosystems that span geopolitical boundaries and gradients of intense land use are improved when historical data and seasonal climate interactions of both temperature and precipitation variables are incorporated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0350.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.061
GPT teacher head0.252
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2022
Admission routes1
Has abstractyes

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