Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Reviews of Geophysics, Journal Year: 2024, Volume and Issue: 62(1)

Published: Feb. 11, 2024

Abstract Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the impacts of future change lake and related processes, use mathematical models is essential. In this study, we provide a comprehensive review water temperature modeling. We begin discussing physical concepts that regulate in lakes, which serve as primer for description process‐based models. then an overview different sources observational data, including situ monitoring satellite Earth observations, used field classify various available, discuss model performance, commonly performance metrics optimization methods. Finally, analyze emerging modeling approaches, forecasting, digital twins, combining deep learning, evaluating structural differences through ensemble modeling, adapted management, coupling This aimed at diverse group professionals working fields limnology hydrology, ecologists, biologists, physicists, engineers, remote sensing researchers from private public sectors who are interested understanding its applications.

Language: Английский

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Recent advancement in energy storage technologies and their applications

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 92, P. 112112 - 112112

Published: May 25, 2024

Language: Английский

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Trophic Reorganisation of Animal Communities Under Climate Change

Journal of Biogeography, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

ABSTRACT Aim This study employs a novel modelling approach to analyse and project global transformations in trophic structures driven by 21st‐century climate change. The objective is assess the impacts of these changes on dynamics, providing insights inform future research biodiversity conservation strategies. Location A total 14,520 terrestrial grid cells 1° × globally. Time Period uses 1990 as baseline reference projects current (2018) conditions for 2040, 2060, 2080 2100 under three Shared Socioeconomic Pathways (SSPs). Major Taxa Studied Trophic were assessed 15,265 species, including 9993 non‐marine birds 5272 mammals, across 9 predefined guilds. Methods spatially explicit community structure model was implemented using extreme gradient boosting algorithm (Xgboost). trained climatic data subset 6610 continental partially or fully overlapping with protected areas. It subsequently used Pathways: SSP2‐45, SSP3‐70 SSP5‐85. Results Xgboost showed high predictive accuracy (86%, kappa = 0.91). Projections reveal extinction pressures concentrated tropical subtropical regions, disproportionately affecting specialised guilds such frugivores invertivores, while colonisation predominantly occur boreal, temperate high‐altitude Andes Himalayas, favouring plant‐invertivores granivores. By end century, significant reorganisations are projected, potentially leading homogenisation structures. Main Conclusions Climate change driving communities globally, uneven effects regions These highlight vulnerability potential expansion more generalist ones. Integrating models (CTSMs) into strategies essential complement species distribution models, comprehensive framework that integrates both dynamics individual responses their environment. reinforces importance biogeography key subdiscipline within biogeography, offering actionable mitigating guiding efforts.

Language: Английский

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The NEON Ecological Forecasting Challenge

Frontiers in Ecology and the Environment, Journal Year: 2023, Volume and Issue: 21(3), P. 112 - 113

Published: April 1, 2023

The 21st century continues to be characterized by major changes the environment and ecosystem services upon which society depends. Anticipating responding these requires that scientists explicitly forecast future conditions in real time (Dietze et al. 2018). Ecological forecasting, like weather epidemiological involves integrating data models generate quantitative predictions of state ecological systems before observations are collected. iterative cycle creating forecasts, evaluating them with new observations, updating models, then making forecasts has potential accelerate learning across many subdisciplines. This builds on openly available data, often published soon after collection, as is increasingly common observatory networks, such National Observatory Network (NEON). To improvements we designed launched NEON Forecasting Challenge (hereafter, "Challenge") (Figure 1), an open platform for science communities they forecasting community interested using advance theory (Lewis 2023) translating natural resource management (Enquist 2017). By analyzing a catalog developed range systems, spatiotemporal scales, environmental gradients, can begin address fundamental questions ecology. Initiative Research Coordination (EFI-RCN) – funded US Science Foundation (NSF) invites broad ecology help build this data. powerful support challenge because it provides standardized reported uncertainties span levels biological organization terrestrial freshwater US. was input from academic, government, private sectors through workshops working groups. We call "Challenge" because, despite its similarities competitions (Makridakis 2021), empowering do more than just submit also collaboratively developing software, training materials, best practices. In May 2020, Challenge's design at virtual conference over 200 attendees (Peters Thomas 2021). Attendees prioritized five "themes" draw questions, have decision management: (1) temperature, dissolved oxygen, chlorophyll-a; (2) carbon fluxes evapotranspiration; (3) plant canopy phenology; (4) tick populations; (5) beetle communities. Themes were identified meeting, smaller teams detailed theme-specific protocols. protocols defined timing submissions (when how due) horizons (how far extend into future). With place, team participants code convert products time-series ready modeling evaluation. Simultaneously, EFI-RCN standards group assembled define format metadata themes 2023). Likewise, steering committee worked each ensure consistent (eg all quantify uncertainty predictions). Challenge, created software workflows provisioning model inputs processing outputs leverage modern cloud storage computing 1). improve efficiency downloading while facilitating analysis exceed computer memory (Boettiger Other end-user tools easy-to-use time-series, process submitted score probabilistic visualize submissions. Every day automatically downloading, processing, sharing NOAA numerical ensemble sites, eliminating need users so themselves. All technologies source generalized applicable beyond Challenge. hope everyone who participating feels empowered individuals or teams. reduce barriers, curated resources (documentation, workflow examples, videos) train computational skills needed development submission. Participants contribute any site theme, type framework empirical, process-based, machine learning). set simple serve benchmarks foundation forecasting. Teaching undergraduate classrooms improves students' systems-level thinking literacy (Carey 2020). Similarly, expands understanding complex concepts (Moore 2022). ideal project graduate students courses workshops. rapid, feedback inherent inspires student engagement improvement evaluated daily accepted become available. transform predictive providing generation delivery. part NEON's mission, empowers lead charge accomplishing mission. However, extends well NEON, engaging researchers not previously considered seeking approaches. It testing ground novel techniques rapidly applied outcomes conservation. fosters creation workforce inspiration blueprint other networks globe. 2021, beta round resulted 2516 contributed 54 different teams, ranging composition companies. At stage contributions critical refining identifying educational materials. Today, fully operational actively contributions. If you becoming involved more, see www.neon4cast.org (Thomas aim further enable innovations, provide valuable training, spark among ecologists. supported NSF (DEB-1926388) provided NSF-funded Jetstream2 (OAC-2005506). program sponsored operated under cooperative agreement Battelle. material based work NEON. Any use trade, firm, product names descriptive purposes only does imply endorsement Government. WebPanel 1 Please note: publisher responsible content functionality supporting information supplied authors. queries (other missing content) should directed corresponding author article.

Language: Английский

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Using plant functional types to predict the influence of fire on species relative abundance

Biological Conservation, Journal Year: 2024, Volume and Issue: 292, P. 110555 - 110555

Published: March 25, 2024

Fire influences plant survival, reproduction, and establishment. Consequently, plants exhibit fire-related traits. Grouping species with similar traits into Plant Functional Types (PFTs) enables predictions of fire–related change based on ecological mechanisms. However, if PFTs are to advance conservation decision-making, we must know robust. We developed a PFT approach predict how relative abundance changes as function time since fire, tested empirically. First, used trait databases knowledge assign Second, graphical in abundance. Third, collected data at 57 sites, across an 81–year post–fire chronosequence. Finally, using non–linear regression models. Predictions the direction (increase or decrease from 0 81 years fire) were correct for 18 24 modelled. shape not accurate, but still useful: 13 out showed 'excellent' conformity predictions, 7 'good' conformity, 4 'poor'. Broader functional groupings commonly ecology, such facultative resprouter, inadequately captured An this study is that trajectory can be predicted deductive represent population processes. This suggests generalize fire responses share traits, thus inform biodiversity management.

Language: Английский

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A community convention for ecological forecasting: Output files and metadata version 1.0

Ecosphere, Journal Year: 2023, Volume and Issue: 14(11)

Published: Nov. 1, 2023

Abstract This paper summarizes the open community conventions developed by Ecological Forecasting Initiative (EFI) for common formatting and archiving of ecological forecasts metadata associated with these forecasts. Such standards are intended to promote interoperability facilitate forecast communication, distribution, validation, synthesis. For output files, we first describe convention conceptually in terms global attributes, dimensions, forecasted variables, ancillary indicator variables. We then illustrate application this two file formats that currently preferred EFI, netCDF (network data form), comma‐separated values (CSV), but note is extensible future formats. metadata, EFI's identifies a subset conventional variables required (e.g., temporal resolution variables) focuses on developing framework storing information about uncertainty propagation, assimilation, model complexity, which aims cross‐forecast The initial expands upon Metadata Language (EML), commonly used standard ecology. To adoption, also provide Github repository containing validator tool several vignettes R Python how both write read EFI standard. Lastly, guidance archiving, making an important distinction between short‐term dissemination long‐term while touching code workflows. Overall, living document can continue evolve over time through process.

Language: Английский

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Forecasting seed production in perennial plants: identifying challenges and charting a path forward

New Phytologist, Journal Year: 2023, Volume and Issue: 239(2), P. 466 - 476

Published: May 18, 2023

Summary Interannual variability of seed production, known as masting, has far‐reaching ecological impacts including effects on forest regeneration and the population dynamics consumers. Because relative timing management conservation efforts in ecosystems dominated by masting species often determines their success, there is a need to study mechanisms develop forecasting tools for production. Here, we aim establish production new branch discipline. We evaluate predictive capabilities three models – foreMast, Δ T , sequential model designed predict trees using pan‐European dataset Fagus sylvatica The are moderately successful recreating dynamics. availability high‐quality data prior improved model's power, suggesting that effective monitoring methods crucial creating tools. In terms extreme events, better at predicting crop failures than bumper crops, likely because factors preventing understood processes leading large reproductive events. summarize current challenges provide roadmap help advance discipline encourage further development mast forecasting.

Language: Английский

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Ecological forecasts of insect range dynamics: a broad range of taxa includes winners and losers under future climate

Current Opinion in Insect Science, Journal Year: 2024, Volume and Issue: 62, P. 101159 - 101159

Published: Jan. 9, 2024

Language: Английский

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Informing policy via dynamic models: Cholera in Haiti

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(4), P. e1012032 - e1012032

Published: April 29, 2024

Public health decisions must be made about when and how to implement interventions control an infectious disease epidemic. These should informed by data on the epidemic as well current understanding transmission dynamics. Such can posed statistical questions scientifically motivated dynamic models. Thus, we encounter methodological task of building credible, data-informed based stochastic, partially observed, nonlinear This necessitates addressing tradeoff between biological fidelity model simplicity, reality misspecification for models at all levels complexity. We assess approaches these issues via a case study 2010-2019 cholera in Haiti. consider three developed expert teams advise vaccination policies. evaluate previous methods used fitting models, demonstrate modified analysis strategies leading improved fit. Specifically, present diagnosing consequent development Additionally, utility recent advances likelihood maximization high-dimensional enabling likelihood-based inference spatiotemporal incidence using this class Our workflow is reproducible extendable, facilitating future investigations system.

Language: Английский

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A Continuum From Positive to Negative Interactions Drives Plant Species' Performance in a Diverse Community

Ecology Letters, Journal Year: 2025, Volume and Issue: 28(1)

Published: Jan. 1, 2025

ABSTRACT With many species interacting in nature, determining which interactions describe community dynamics is nontrivial. By applying a computational modeling approach to an extensive field survey, we assessed the importance of from plants (both inter‐ and intra‐specific), pollinators insect herbivores on plant performance (i.e., viable seed production). We compared inclusion interaction effects as aggregate guild‐level terms versus specific taxonomic groups. found that continuum positive negative interactions, containing mostly few strong taxonomic‐specific effects, was sufficient performance. While with intraspecific varied weakly positive, heterospecific mainly promoted competition facilitated plants. The consistency these empirical findings over 3 years suggests including groups rather than all pairwise high‐order can be for accurately describing variation across natural communities.

Language: Английский

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