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Abstract Tropical forest diversity governs forest structures, compositions, and influences the ecosystem response to environmental changes. Better representation of forest diversity in ecosystem demography (ED) models within Earth system models is thus necessary to accurately capture and predict how tropical forests affect Earth system dynamics subject to climate changes. However, achieving forest coexistence in ED models is challenging due to their computational expense and limited understanding of the mechanisms governing forest functional diversity. This study applies the advanced Multi‐Objective Population‐based Parallel Local Surrogate‐assisted search (MOPLS) optimization algorithm to simultaneously calibrate ecosystem fluxes and coexistence of two physiologically distinct tropical forest species in a size‐ and age‐structured ED model with realistic representation of wood harvest. MOPLS exhibits satisfactory model performance, capturing hydrological and biogeochemical dynamics observed in Barro Colorado Island, Panama, and robustly achieving coexistence for the two representative forest species. This demonstrates its effectiveness in calibrating tropical forest coexistence. The optimal solution is applied to investigate the recovery trajectories of forest biomass after various intensities of clear‐cut deforestation. We find that a 20% selective logging can take approximately 40 years for aboveground biomass to return to the initial level. This is due to the slow recovery rate of late successional trees, which only increases by 4% over the 40‐year period. This study lays the foundation to calibrate coexistence in ED models. MOPLS can be an effective tool to help better represent tropical forest diversity in Earth system models and inform forest management practices. 

Extending and safeguarding tropical forest ecosystems is critical for combating climate change and biodiversity loss. One of its constituents, lianas, is spreading and increasing in abundance on a global scale. This is particularly concerning as lianas negatively impact forests’ carbon fluxes, dynamics, and overall resilience, potentially exacerbating both crises. While possibly linked to climate-change-induced atmospheric CO₂elevation and drought intensification, the reasons behind their increasing abundance remain elusive. Prior research shows distinct physiological differences between lianas and trees, but it is unclear whether these differences confer a demographic advantage to lianas with climate change. Guided by extensive datasets collected in Panamanian tropical forests, we developed a tractable model integrating physiology, demography, and epidemiology. Our findings suggest that CO₂fertilization, a climate change factor promoting forest productivity, gives lianas a demographic advantage. Conversely, factors such as extreme drought generally cause a decrease in liana prevalence. Such a decline in liana prevalence is expected from a physiological point of view because lianas have drought-sensitive traits. However, our analysis underscores the importance of not exclusively relying on physiological processes, as interactions with demographic mechanisms (i.e., the forest structure) can contrast these expectations, causing an increase in lianas with drought. Similarly, our results emphasize that identical physiological responses between lianas and trees still lead to liana increase. Even if lianas exhibit collinear but weaker responses in their performance compared to trees, a temporary liana prevalence increase might manifest driven by the faster response time of lianas imposed by their distinct life-history strategies than trees. 

Abstract Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals—reflectance across wavelengths—from liana‐infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive database, including leaf reflectance spectra from 5424 leaves, fine‐scale airborne reflectance data from 999 liana‐infested canopies, and coarse‐scale satellite reflectance data covering 775 ha of liana‐infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we corroborate with field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and short‐wave infrared regions, linked to lianas' less costly leaf construction compared with trees on average. The existence of a consistent global spectral signal for lianas suggests that large‐scale quantification of liana infestation is feasible. However, because the traits responsible for the liana canopy‐reflectance signal are not exclusive to lianas, accurate large‐scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross‐site patterns also prompt ecological questions about lianas' adaptive similarities in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits. 

Synopsis Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth’s ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community-scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of forest CO2 and H2O gas exchange and productivity: leaf water potential (Ψcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought, including growth cessation, mortality, and flammability. We unite these concepts in a novel framework to predict Ψcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how the extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate and provide new mechanistic understanding and prediction of forest water use and productivity. 

Summary The spring phenology has advanced significantly over recent decades with climate change, impacting large‐scale biogeochemical cycles, climate feedback, and other essential ecosystem processes. Although numerous prognostic models have been developed for spring phenology, regional analyses of the optimality (OPT) strategy model that incorporate environmental variables beyond temperature and photoperiod remain lacking.We investigated the roles of solar radiation (SR) and three water stress factors (precipitation (P), soil moisture, and vapor pressure deficit (VPD)) on spring phenology from 1982 to 2015 using the OPT model with Global Inventory Modeling and Mapping Studies NDVI3g dataset and environmental data from TerraClimate, CRU_TS, and Global Land Data Assimilation System across the Northern Hemisphere (> 30°N).Our results show that SR and water stress factors significantly impacted intrasite decadal spring phenology variability, with water stress factors dominant in grassland ecosystems while SR dominated in the rest of the ecosystem types. Enhanced models incorporating SR (OPT‐S) and VPD (OPT‐VPD) outperformed the original OPT model, likely due to improved representation of the adaptive strategy of spring phenology to optimize photosynthetic carbon gain while minimizing frost risk.Our research enhances the understanding of the key environmental drivers influencing decadal spring phenology variation in the Northern Hemisphere and contributes to more accurate forecasts of ecological responses to global environmental change. 

ABSTRACT Conspecific density dependence (CDD) in plant populations is widespread, most likely caused by local‐scale biotic interactions, and has potentially important implications for biodiversity, community composition, and ecosystem processes. However, progress in this important area of ecology has been hindered by differing viewpoints on CDD across subfields in ecology, lack of synthesis across CDD‐related frameworks, and misunderstandings about how empirical measurements of local CDD fit within the context of broader ecological theories on community assembly and diversity maintenance. Here, we propose a conceptual synthesis of local‐scale CDD and its causes, including species‐specific antagonistic and mutualistic interactions. First, we compare and clarify different uses of CDD and related concepts across subfields within ecology. We suggest the use of local stabilizing/destabilizing CDD to refer to the scenario where local conspecific density effects are more negative/positive than heterospecific effects. Second, we discuss different mechanisms for local stabilizing and destabilizing CDD, how those mechanisms are interrelated, and how they cut across several fields of study within ecology. Third, we place local stabilizing/destabilizing CDD within the context of broader ecological theories and discuss implications and challenges related to scaling up the effects of local CDD on populations, communities, and metacommunities. The ultimate goal of this synthesis is to provide a conceptual roadmap for researchers studying local CDD and its implications for population and community dynamics.