Predicting Gross Primary Productivity under Future Climate Change for the Tibetan Plateau Based on Convolutional Neural Networks DOI Creative Commons
Meimei Li, Zhongzheng Zhu, Weiwei Ren

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(19), С. 3723 - 3723

Опубликована: Окт. 7, 2024

Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems’ responses to climate change. However, impact future changes on GPP in Tibetan Plateau, an ecologically important climatically region, remains underexplored. This study aimed develop data-driven approach predict seasonal annual variations Plateau up year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed investigate relationships between various environmental factors, including variables, CO2 concentrations, terrain attributes. analyzed projected from Coupled Model Intercomparison Project Phase 6 (CMIP6) four scenarios: SSP1–2.6, SSP2–4.5, SSP3–7.0, SSP5–8.5. The results suggest that expected significantly increase throughout 21st century all scenarios. By 2100, reach 1011.98 Tg C, 1032.67 1044.35 1055.50 C scenarios, representing 0.36%, 4.02%, 5.55%, 5.67% relative 2021. analysis indicates spring autumn shows more pronounced growth SSP3–7.0 SSP5–8.5 scenarios due extended growing season. Furthermore, identified elevation band 3000 4500 m particularly change terms response. Significant increases would occur east Qilian Mountains upper reaches Yellow Yangtze Rivers. These findings highlight pivotal role driving dynamics this region. insights not only bridge existing knowledge gaps regarding over coming decades but also provide valuable guidance formulation adaptation strategies at ecological conservation management.

Язык: Английский

Shifting microbial communities can enhance tree tolerance to changing climates DOI
Cassandra M. Allsup, Isabelle George, Richard A. Lankau

и другие.

Science, Год журнала: 2023, Номер 380(6647), С. 835 - 840

Опубликована: Май 25, 2023

Climate change is pushing species outside of their evolved tolerances. Plant populations must acclimate, adapt, or migrate to avoid extinction. However, because plants associate with diverse microbial communities that shape phenotypes, shifts in associations may provide an alternative source climate tolerance. Here, we show tree seedlings inoculated sourced from drier, warmer, colder sites displayed higher survival when faced drought, heat, cold stress, respectively. Microbially mediated drought tolerance was associated increased diversity arbuscular mycorrhizal fungi, whereas lower fungal richness, likely reflecting a reduced burden nonadapted taxa. Understanding microbially enhance our ability predict and manage the adaptability forest ecosystems changing climates.

Язык: Английский

Процитировано

125

Shifts in native tree species distributions in Europe under climate change DOI Creative Commons
Marcin K. Dyderski, Sonia Paź‐Dyderska, Andrzej M. Jagodziński

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 373, С. 123504 - 123504

Опубликована: Дек. 6, 2024

Язык: Английский

Процитировано

14

Functional Signatures of Surface Pollen and Vegetation Are Broadly Similar: Good News for Past Reconstructions of Vegetation DOI Creative Commons
Lucas Dugerdil, Odile Peyron, Cyrille Violle

и другие.

Journal of Biogeography, Год журнала: 2025, Номер unknown

Опубликована: Фев. 14, 2025

ABSTRACT Aim Pollen assemblages are widely used to infer paleoenvironment features, aiming at reconstructing both past climates and biomes. However, the functional link between environmental conditions pollen is not straightforward requires thorough testing be confidently. Here, we use a trait‐based approach assess consistency of signatures plant assemblages. Location Arid Central Asia (ACA). Taxon Spermatophytes (pollen‐producing plants). Methods We whether trait values distributions consistent for surface samples extant vegetation in biogeographic region. A working checklist was compiled ACA order assign types taxa. This done two methods aggregation schemes (coarse fine type depend on level identification). The were compared taxon community levels, using large‐scale databases, six traits global spectrum form function (i.e., height, seed mass, leaf area, specific nitrogen content per stem‐specific density). Results Trait bivariate relationships broadly similar taxa, which also case multivariate spaces function. At scale, weighted by abundance significantly differed among biomes, these differences extant. Main Conclusions scheme does impact organisation space function, compares well with that based species actually present plots. true scale. These findings very promising improving climate biome reconstructions from pave way “pollen biogeography”.

Язык: Английский

Процитировано

1

Climate change-related distributional range shifts of venomous snakes: a predictive modelling study of effects on public health and biodiversity DOI Creative Commons
Pablo A. Martínez,

Irene Barbosa da Fonseca Teixeira,

Tuany Siqueira‐Silva

и другие.

The Lancet Planetary Health, Год журнала: 2024, Номер 8(3), С. e163 - e171

Опубликована: Март 1, 2024

BackgroundClimate change is expected to have profound effects on the distribution of venomous snake species, including reductions in biodiversity and changes patterns envenomation humans domestic animals. We estimated effect future climate species potential knock-on public health.MethodsWe built models based geographical 209 medically relevant (WHO categories 1 2) present climatic variables, used these project 2070. incorporated different scenarios into model, which we estimate loss gain areas potentially suitable for each species. also assessed countries were likely new as a result crossing national borders. integrated with socioeconomic would become more vulnerable snakebites 2070.FindingsOur results suggest that substantial losses survival most will occur by However, some high risk health could climatically habitation. Countries such Niger, Namibia, China, Nepal, Myanmar several from neighbouring countries. Furthermore, combination an increase factors (including low-income rural populations) means southeast Asia Africa (and Uganda, Kenya, Bangladesh, India, Thailand particular) increased vulnerability future, human veterinary health.InterpretationLoss affect ecosystem functioning valuable genetic resources. Additionally, create challenges countries, particularly Africa. The international community needs its efforts counter coming decades.FundingGerman Research Foundation, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Coordenação Aperfeiçoamento Pessoal Nível Superior, German Centre Integrative Biodiversity Research, Ministerio Ciencia Innovación España, European Regional Development Fund.

Язык: Английский

Процитировано

9

Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches DOI Creative Commons
Benjamin Dechant, Jens Kattge, Ryan Pavlick

и другие.

Remote Sensing of Environment, Год журнала: 2024, Номер 311, С. 114276 - 114276

Опубликована: Июнь 27, 2024

Foliar traits such as specific leaf area (SLA), nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies ecosystem functioning.Various global maps of these foliar have been generated using statistical upscaling approaches based on in-situ trait observations.Here, we intercompare upscaled at 0.5 • spatial resolution (six for SLA, five N, three P), categorize the used to generate them, evaluate with estimates from a database vegetation plots (sPlotOpen).We disentangled contributions different functional types (PFTs) quantified impacts plot-level metrics evaluation sPlotOpen: community weighted mean (CWM) top-of-canopy (TWM).We found that SLA N differ drastically fall into two groups are almost uncorrelated (for P only one group were available).The primary factor explaining differences between is use PFT information combined remote sensing-derived land cover products while other mostly relied environmental predictors alone.The corresponding exhibit considerable similarities patterns strongly driven by cover.The not PFTs show lower level similarity tend be individual variables.Upscaled both moderately correlated sPlotOpen data aggregated grid-cell (R = 0.2-0.6)when processing way consistent respective approaches, including metric (CWM or TWM) scaling grid cells without accounting fractional impact TWM CWM was relevant, but considerably smaller than information.The better reproduce between-PFT data, performed similarly capturing within-PFT variation.Our findings highlight importance explicitly within-grid-cell variation, which has implications applications existing future efforts.Remote sensing great potential reduce uncertainties related observations regression-based mapping steps involved upscaling.

Язык: Английский

Процитировано

9

Conservation Letter: Effects of Global Climate Change on Raptors1 DOI Open Access
Marisela Martínez‐Ruiz, Cheryl R. Dykstra, Travis L. Booms

и другие.

Journal of Raptor Research, Год журнала: 2023, Номер 57(1)

Опубликована: Фев. 23, 2023

Язык: Английский

Процитировано

16

Global prediction of gross primary productivity under future climate change DOI
Qikai Lu, Hui Liu, Lifei Wei

и другие.

The Science of The Total Environment, Год журнала: 2023, Номер 912, С. 169239 - 169239

Опубликована: Дек. 10, 2023

Язык: Английский

Процитировано

16

Tree aboveground biomass increment and mortality in temperate mountain forests: Tracing dynamic changes along 25-year monitoring period DOI
Marcin K. Dyderski, Łukasz Pawlik,

K. Chwistek

и другие.

Forest Ecology and Management, Год журнала: 2023, Номер 540, С. 121054 - 121054

Опубликована: Апрель 28, 2023

Язык: Английский

Процитировано

14

Global leaf‐trait mapping based on optimality theory DOI Creative Commons
Ning Dong, Benjamin Dechant, Han Wang

и другие.

Global Ecology and Biogeography, Год журнала: 2023, Номер 32(7), С. 1152 - 1162

Опубликована: Апрель 14, 2023

Abstract Aim Leaf traits are central to plant function, and key variables in ecosystem models. However recently published global trait maps, made by applying statistical or machine‐learning techniques large compilations of environmental data, differ substantially from one another. This paper aims demonstrate the potential an alternative approach, based on eco‐evolutionary optimality theory, yield predictions spatio‐temporal patterns leaf that can be independently evaluated. Innovation Global community‐mean specific area (SLA) photosynthetic capacity ( V cmax ) predicted climate via existing Then nitrogen per unit N mass inferred using their (previously derived) empirical relationships SLA . Trait data thus reserved for testing model across sites. Temporal trends also predicted, as consequences change, compared those leaf‐level measurements and/or remote‐sensing methods, which increasingly important source information variation traits. Main conclusions Model evaluated against site‐mean > 2,000 sites Plant database yielded R 2 = 73% SLA, 38% 28% Declining species‐level , increasing community‐level have both been reported were correctly predicted. Leaf‐trait mapping theory holds promise macroecological applications, including improved understanding community leaf‐trait responses change.

Язык: Английский

Процитировано

12

Biome classification influences current and projected future biome distributions DOI Creative Commons
Simon Scheiter, Dushyant Kumar, Mirjam Pfeiffer

и другие.

Global Ecology and Biogeography, Год журнала: 2023, Номер 33(2), С. 259 - 271

Опубликована: Ноя. 21, 2023

Abstract Aim Biome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence biome patterns, and models can be assess the susceptibility biomes experience transitions. However, is not unique, various maps exist. Here, we aimed how choice influences current projected future patterns. Location Africa, Australia, Tropical Asia. Time period 2000–2099. Major taxa studied vegetation. Methods We adaptive dynamic global model version 2 (aDGVM2) simulate in study region. classified into using (1) a scheme based cover functional types, (2) cluster analysis types (3) trait simulated by aDGVM2. compared resulting multiple observation‐based quantified differences changes under RCP8.5 scenario for different schemes. Results As expected, were strongly related classification. The highest data‐model agreement was derived 21 traits. Traits size most important Considering all schemes, area undergo transitions varied between 16.5% 32.1%. Despite this variability, consistently showed that grassland savanna areas susceptible change, whereas tropical forests deserts stable. Our results demonstrate traits aDGVM2 appropriate delimit biomes. Main conclusions Studies projecting should consider applying avoid biases such projections caused

Язык: Английский

Процитировано

12