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Enregistrement W4410825948 · doi:10.3897/aca.8.e147461

How ‘long term’ is long term enough? Bridging neo- and paleo-ecological timescales

2025· article· en· W4410825948 sur OpenAlexaffabout
Richard Bindler, Carsten Meyer‐Jacob

Notice bibliographique

RevueARPHA Conference Abstracts · 2025
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueGeology and Paleoclimatology Research
Établissements canadiensUniversité du Québec en Abitibi-Témiscamingue
Organismes subventionnairesnon disponible
Mots-clésTerm (time)Bridging (networking)EcologyEnvironmental sciencePhysicsBiologyComputer scienceAstronomy

Résumé

récupéré en direct d'OpenAlex

A challenge in assessing the impacts of ongoing human-driven environmental and climate changes is the limited long-term data from monitoring programs and field studies, which rarely span more than a few decades (Nevalainen et al. 2020, Dodds et al. 2012). In line with common usage of ‘long term’ in ecology, Dodds et al. (2012) fined a “long-term ecological data set as data that are measured systematically through time using standardized methods that allow for the elucidation of ecological system responses (e.g., linear, lag, threshold, regime shift) to drivers, disturbances (e.g., presses or pulses), recovery from disturbances, and relevant interactions for a given hypothesis.” However, while experimental, field, and monitoring data are essential to develop insights into mechanisms and possibly reveal emerging trends, these time series provide limited insights into long-term – i.e., decadal–centennial–millennial – dynamics that enable defining reference conditions and possible underlying trajectories, which are required for identifying ongoing super-imposed changes. Since national monitoring programs in Sweden began in the early-1980’s, lake-water organic carbon (LW-TOC; Fig. 1) has been increasing in many lakes (de Wit et al. 2016), which affects light penetration and freshwater ecology (Horppila et al. 2024). Increasing LW-TOC has been attributed to decreasing S deposition, increasing temperature and wetness (de Wit et al. 2016, Monteith et al. 2007), and increasing forest cover (Finstad et al. 2016). Since precipitation monitoring began in 1978–1980, S deposition has decreased and precipitation pH increased by ~0.75–1 pH units. Temperatures have been increasing over the timeframe available from the longest continuous time series: in Lund, southern Sweden, June temperatures have increased 2°C since 1859, and in Mora, central Sweden, show a similar ~2°C increase since 1941. Finally, the Swedish National Forest Inventory indicates forests in southwestern Sweden have increased 2–4 fold and in central Sweden by ~60% since 1923. Because these long-term data for S deposition, climate, and forest growth have been changing at the same time as LW-TOC increases (Fig. 1), disentangling the relative importance of these factors. Furthermore, none of these time series provide information on reference conditions or the full timeframe of current trajectories. Using monitoring data from Sweden and specifically LW-TOC, we demonstrate the importance of integrating contemporary instrumental data with sedimentary records; that is, bridging instrumental and paleolimnological timescales (Fig. 2). This research has entailed developing calibrations between instrumental data and sediment proxies (VNIRS-inferred LW-TOC; (Meyer-Jacob et al. 2015, Meyer-Jacob et al. 2017, Rosén 2005), demonstrating the proxy signal is preserved in sediments, and assessing the trends from monitoring in relation to longer-term patterns (Meyer-Jacob et al. 2015, Meyer-Jacob et al. 2019, Meyer-Jacob et al. 2017, Myrstener et al. 2021). Our research has shown the importance of acidification/recovery, climate changes, and/or land-use changes has varied within Sweden and in other northern areas. Focusing on data from Sweden (Fig. 3), LW-TOC increased with post-glacial landscape development and thereafter remained high and stable through most of the Holocene (Meyer-Jacob et al. 2015, Myrstener et al. 2021). In northern Finland LW-TOC was also associated climate (). However, in Sweden, centuries-long traditional use led to declining LW-TOC, which began c. 700 CE in southwestern Sweden and c. 1400 in central Sweden – declining by ~50% by the 1800’s. The association with human impacts is evidenced by declining arboreal pollen, increasing pollen of plants favored by human disturbance, and occurrence of pollen from cultivated plants. In southwestern Sweden, where 20 th -century impacts of acidification were greatest, LW-TOC decreased even further. The current levels of LW-TOC in these lakes are still far below the ‘natural’ levels preceding human impacts. In Canada, where historical land uses did not have the same impact as in Sweden, it has been possible to tease apart the influence of acidification and climate on recent increases in LW-TOC (Meyer-Jacob et al. 2019). During the 20 th century acidification in the highest S deposition areas has been the most important driver of LW-TOC decreases – similar to the 20 th -century decline in southwestern Sweden. Thus, increasing LW-TOC is mainly a response to acidification recovery. In contrast, outside of high S deposition areas LW-TOC decreased less during the mid-20 th century due to S deposition, the increasing LW-TOC has in some cases exceeded pre-acidification values, indicating a climate contribution to the trend. Taken together, these centennial–millennial long records of past environmental changes have been able to identify important long-term patterns and levels in LW-TOC, and to disentangle the importance of key processes identified through field and experimental studies. The paleolimnological studies indicate there are regionally different drivers underlying the observed increases in LW-TOC in northern lakes.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,038
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,266
Écart entre enseignants0,237 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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