Modelos de distribución de especies y estado de conservación de murciélagos amenazados en la región tumbesina de Ecuador y Perú / Species distribution models and conservation status of threatened bats in the Tumbesian region of Ecuador and Perú

Resumen Introducción: La biodiversidad se está perdiendo a un ritmo acelerado como resultado del cambio global. Herramientas como los modelos de distribución de especies (MDEs) han sido ampliamente usados para mejorar el conocimiento sobre el estado de conservación de las especies y ayudar a desarrollar estrategias de gestión para mitigar la pérdida de biodiversidad. Objetivo: Determinar cómo la distribución potencial predicha por los MDEs para ocho especies de murciélagos amenazados difiere de los mapas de distribución reportados por la UICN. También, inferir el área de distribución y estado de endemismo de cada especie, y evaluar la importancia de la región tumbesina para su conservación. Métodos: Basados en registros de presencia del rango global de las especies, usamos MDEs para evaluar el estado de conservación de estas ocho especies en la región tumbesina de Ecuador y Perú. Resultados: Las áreas estimadas por los MDEs eran 35-78 % más pequeñas para cuatro especies (Eptesicus innoxius, Lophostoma occidentale, Platalina genovensium y Lonchophylla hesperia) y 26-1 600 % más grandes para tres especies (Amorphochilus schnablii, Promops davisoni y Rhogeessa velilla) que aquellas reportadas por la UICN. Para Tomopeas ravus, el área estimada por el MDE y la UICN fue similar, pero difirió en la distribución espacial. Los MDEs coincidieron con áreas de endemismo informadas por autores previos para E. innoxius, R. velilla y T. ravus, pero fueron diferentes para A. schnablii, P. genovensium, P. davisoni y L. hesperia, debido en parte a las distribuciones proyectadas para estas últimas especies en valles secos interandinos según los MDEs. Conclusiones: La región tumbesina representa una porción significativa (40-96 %) de la distribución predicha de siete de las ocho especies estudiadas, subrayando la importancia de esta región para la conservación de murciélagos. Nuestros resultados muestran las probables distribuciones para estas especies y proporcionan una base importante para identificar vacíos de investigación y desarrollar medidas de conservación para murciélagos amenazados en el punto caliente de biodiversidad de Tumbes.

Abstract Introduction: Biodiversity is being lost at an accelerating rate because of global change. Tools such as species distribution models (SDMs) have been widely used to improve knowledge about species' conservation status and help develop management strategies to mitigate biodiversity loss. SDMs are especially important for species with restricted distributions, such as endemic species. Objective: To determine how potential distribution predicted by SDMs for eight threatened bat species differed from the distribution maps reported by the IUCN. Also, to infer the area of distribution and state of endemism of each specie, and to evaluate the importance of the Tumbesian region for their conservation. Methods: Based on presence records across the species' entire ranges, we used SDMs to assess the conservation status of these eight species in the Tumbesian region of Ecuador and Peru. Results: The areas estimated by SDMs were 35-78 % smaller for four species (Eptesicus innoxius, Lophostoma occidentale, Platalina genovensium and Lonchophylla hesperia) and 26-1 600 % larger for three species (Amorphochilus schnablii, Promops davisoni and Rhogeessa velilla) than those reported by the IUCN. For Tomopeas ravus, the area estimated by the SDM and IUCN was similar but differed in spatial distribution. SDMs coincided with areas of endemism reported by previous authors for E. innoxius, R. velilla, and T. ravus, but were different for A. schnablii, P. genovensium, P. davisoni, and L. hesperia, due in part to projected distributions for these latter species in dry inter-Andean valleys according to the SDMs. Conclusions: The Tumbesian region represents a significant portion (40-96 %) of the predicted distribution of seven of the eight species studied, underscoring the importance of this region for bat conservation. Our results show likely distributions for these species and provide an important basis for identifying research gaps and developing conservation measures for threatened bats in the Tumbes biodiversity hotspot.

Living lab modelling as a pilot study assessing the potential psychological health benefits of forest environment for cancer survivors.

Objective: To evaluate the physiological and psychological changes in cancer survivors who engage in repeated forest therapy in a living environment. Methods: This study included stay-based forest therapy for female cancer survivors aged ≥40 years. The program was conducted in two cycles, each spanning 3 weeks and consisting of a 2-night, 3-day stay, followed by daily life integration. The cycles were repeated from July 2, 2022, to August 18, 2022. Participant assessment included standard physical health parameters and a questionnaire on general characteristics, lifestyle habits, stress levels, and health status. Results: Thirty-seven female cancer survivors participated in the forest healing program, 56.8% of whom had a history of breast cancer. The median body mass index (BMI) was 23.80 kg/m2 (range, 21.00-25.60). More than half of the patients reported mild-to-moderate fatigue, chronic pain, and mild-to-moderate depression (81%, 65%, and 73%, respectively). After two cycles of forest therapy, no significant differences were observed in terms of fatigue, pain, or BMI levels. However, significant improvements were found in quality of life measures, particularly the psychological quality of life (mean score 12.54 at baseline vs. 13.48 after cycle 2; P=0.007). Positive improvements were also observed in terms of stress (mean score 17.03 vs. 13.76; P=0.002) and depression (mean score 8.35 vs. 6.11; P=0.002) levels. Conclusion: Our forest-healing program demonstrated that nature-based therapies improve the mental health and quality of life of female cancer survivors, suggesting the need for further research on nature-based interventions to better support cancer survivors.

Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality.

Understanding the combined effects of risk factors on all-cause mortality is crucial for implementing effective risk stratification and designing targeted interventions, but such combined effects are understudied. We aim to use survival-tree based machine learning models as more flexible nonparametric techniques to examine the combined effects of multiple physiological risk factors on mortality. More specifically, we (1) study the combined effects between multiple physiological factors and all-cause mortality, (2) identify the five most influential factors and visualize their combined influence on all-cause mortality, and (3) compare the mortality cut-offs with the current clinical thresholds. Data from the 1999-2014 NHANES Survey were linked to National Death Index data with follow-up through 2015 for 17,790 adults. We observed that the five most influential factors affecting mortality are the tobacco smoking biomarker cotinine, glomerular filtration rate (GFR), plasma glucose, sex, and white blood cell count. Specifically, high mortality risk is associated with being male, active smoking, low GFR, elevated plasma glucose levels, and high white blood cell count. The identified mortality-based cutoffs for these factors are mostly consistent with relevant studies and current clinical thresholds. This approach enabled us to identify important cutoffs and provide enhanced risk prediction as an important basis to inform clinical practice and develop new strategies for precision medicine.

Fire protection priorities in the oak forests of Iran with an emphasis on vertebrate habitat preservation.

This study examines the impact of fire incidents on wildlife and habitats in the western oak forests of Iran (Zagros region). These forests are globally recognized for their exceptional biodiversity but are frequently threatened by wildfires. To achieve this, the study uses the space-time scan statistics permutation (STSSP) model to identify areas with a higher frequency of fires. The study also analyzes the effects of fires on the Zagros forests from 2000 to 2021 using remote-sensing MODIS data. Also, to understand the elements at risk of fire, burned areas were assessed based on the richness of vertebrate species, determined by the distribution of 88 vertebrate species. The results show that the annual fire rate in the Zagros forests is 76.2 (fire occurrences per year), calculated using the Poisson distribution. Findings show the highest fire rates are found in the northwest and a part of the south of the Zagros. The northwest of the Zagros also has the largest number of single fires and clusters, indicating a wide spatial distribution of fire in these regions. On the other side, it was unexpectedly found that these regions have the richest number of species and higher habitat value. The results demonstrate a significant correlation between the value of the habitat and the extent of burned areas (p < 0.05). The study also reveals that the greatest impact of fires is on small vertebrates. The overlap of frequent fire spots with the richest regions of Zagros oak forests in terms of vertebrate diversity emphasizes the need for strategic forest risk reduction planning, especially in these priority zones.

Identifying early language predictors: A replication of Gasparini et al. (2023) confirming applicability in a general population cohort.

BACKGROUND: Identifying language disorders earlier can help children receive the support needed to improve developmental outcomes and quality of life. Despite the prevalence and impacts of persistent language disorder, there are surprisingly no robust predictor tools available. This makes it difficult for researchers to recruit young children into early intervention trials, which in turn impedes advances in providing effective early interventions to children who need it. AIMS: To validate externally a predictor set of six variables previously identified to be predictive of language at 11 years of age, using data from the Longitudinal Study of Australian Children (LSAC) birth cohort. Also, to examine whether additional LSAC variables arose as predictive of language outcome. METHODS & PROCEDURES: A total of 5107 children were recruited to LSAC with developmental measures collected from 0 to 3 years. At 11-12 years, children completed the Clinical Evaluation of Language Fundamentals, 4th Edition, Recalling Sentences subtest. We used SuperLearner to estimate the accuracy of six previously identified parent-reported variables from ages 2-3 years in predicting low language (sentence recall score ≥ 1.5 SD below the mean) at 11-12 years. Random forests were used to identify any additional variables predictive of language outcome. OUTCOMES & RESULTS: Complete data were available for 523 participants (52.20% girls), 27 (5.16%) of whom had a low language score. The six predictors yielded fair accuracy: 78% sensitivity (95% confidence interval (CI) = [58, 91]) and 71% specificity (95% CI = [67, 75]). These predictors relate to sentence complexity, vocabulary and behaviour. The random forests analysis identified similar predictors. CONCLUSIONS & IMPLICATIONS: We identified an ultra-short set of variables that predicts 11-12-year language outcome with 'fair' accuracy. In one of few replication studies of this scale in the field, these methods have now been conducted across two population-based cohorts, with consistent results. An imminent practical implication of these findings is using these predictors to aid recruitment into early language intervention studies. Future research can continue to refine the accuracy of early predictors to work towards earlier identification in a clinical context. WHAT THIS PAPER ADDS: What is already known on the subject There are no robust predictor sets of child language disorder despite its prevalence and far-reaching impacts. A previous study identified six variables collected at age 2-3 years that predicted 11-12-year language with 75% sensitivity and 81% specificity, which warranted replication in a separate cohort. What this study adds to the existing knowledge We used machine learning methods to identify a set of six questions asked at age 2-3 years with ≥ 71% sensitivity and specificity for predicting low language outcome at 11-12 years, now showing consistent results across two large-scale population-based cohort studies. What are the potential or clinical implications of this work? This predictor set is more accurate than existing feasible methods and can be translated into a low-resource and time-efficient recruitment tool for early language intervention studies, leading to improved clinical service provision for young children likely to have persisting language difficulties.

Long-term Trends of Dissolved Organic Carbon Dynamics in a Subtropical Forest Responding to Environmental Changes.

Dissolved organic carbon (DOC) dynamics are critical to carbon cycling in forest ecosystems and sensitive to global change. Our study, spanning from 2001 to 2020 in a headwater catchment in subtropical China, analyzed DOC and water chemistry of throughfall, litter leachate, soil waters at various depths, and streamwater. We focused on DOC transport through hydrological pathways and assessed the long-term trends in DOC dynamics amidst environmental and climatic changes. Our results showed that the annual DOC deposition via throughfall and stream outflow was 14.2 ± 2.2 and 1.87 ± 0.83 g C m-2 year-1, respectively. Notably, there was a long-term declining trend in DOC deposition via throughfall (-0.195 mg C L-1 year-1), attributed to reduced organic carbon emissions from clean air actions. Conversely, DOC concentrations in soil waters and stream waters showed increasing trends, primarily due to mitigated acid deposition. Moreover, elevated temperature and precipitation could partly explain the long-term rise in DOC leaching. These trends in DOC dynamics have significant implications for the stability of carbon sink in terrestrial, aquatic, and even oceanic ecosystems at regional scales.

Limited drought tolerance in the neotropical seasonally dry forest plants impairs future species richness.

Neotropical seasonal dry forest (NSDF) is one of the most threatened ecosystems according to global climate change predictions. Nonetheless, few studies have evaluated the global climate change impacts on diversity patterns of NSDF plants. The lack of whole biome-scale approaches restricts our understanding of global climate change consequences in the high beta-diverse NSDF. We analysed the impact of global climate change on species distribution ranges, species richness, and assemblage composition (beta diversity) for 1,178 NSDF species. We used five representative plant families (in terms of abundance, dominance, and endemism) within the NSDF: Cactaceae, Capparaceae, Fabaceae, Malvaceae, and Zygophyllaceae. We reconstructed potential species distributions in the present and future (2040-2080), considering an intermediate Shared Socioeconomic Pathway and two dispersal ability assumptions on the taxa. Using a resource use scores index, we related climate-induced range contractions with species' water stress tolerance. Even under a favourable dispersal scenario, species distribution and richness showed future significant declines across those sites where mean temperature and precipitation seasonality are expected to increase. Further, changes in species range distribution in the future correlated positively with potential use of resources in Fabaceae. Results suggest that biotic heterogenization will likely be the short-term outcome at biome scale under dispersal limitations. Nonetheless, by 2080, the prevailing effect under both dispersal assumptions will be homogenization, even within floristic nuclei. This information is critical for further defining new areas worth protecting and future planning of mitigation actions for both species and the whole biome.

Responses of particulate and mineral-associated organic carbon to temperature changes and their mineral protection mechanisms: A soil translocation experiment.

Mineral protection mechanisms are important in determining the response of particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) to temperature changes. However, the underlying mechanisms for how POC and MAOC respond to temperature changes are remain unclear. By translocating soils across 1304 m, 1425 m and 2202 m elevation gradient in a temperate forest, simulate nine months of warming (with soil temperature change of +1.41 °C and +3.91 °C) and cooling (with soil temperature change of -1.86 °C and -4.20 °C), we found that warming translocation significantly decreased POC by an average of 10.84 %, but increased MAOC by an average of 4.25 %. Conversely, cooling translocation led to an average increase of 8.64 % in POC and 13.48 % in MAOC. Exchangeable calcium (Caexe) had a significant positive correlation with POC and MAOC during temperature changes, and Fe/Al-(hydr)oxides had no significant correlation or a significant negative correlation with POC and MAOC. Our results showed that POC was more sensitive than MAOC to temperature changes. Caexe mediated the stability of POC and MAOC under temperature changes, and Fe/Al-(hydr)oxides had no obvious protective effect on POC and MAOC. Our results support the role of mineral protection in the stabilization mechanism of POC and MAOC in response to climate change and are critical for understanding the consequences of global change on soil organic carbon (SOC) dynamics.

Exploratory subgroup identification in the heterogeneous Cox model: A relatively simple procedure.

For survival analysis applications we propose a novel procedure for identifying subgroups with large treatment effects, with focus on subgroups where treatment is potentially detrimental. The approach, termed forest search, is relatively simple and flexible. All-possible subgroups are screened and selected based on hazard ratio thresholds indicative of harm with assessment according to the standard Cox model. By reversing the role of treatment one can seek to identify substantial benefit. We apply a splitting consistency criteria to identify a subgroup considered "maximally consistent with harm." The type-1 error and power for subgroup identification can be quickly approximated by numerical integration. To aid inference we describe a bootstrap bias-corrected Cox model estimator with variance estimated by a Jacknife approximation. We provide a detailed evaluation of operating characteristics in simulations and compare to virtual twins and generalized random forests where we find the proposal to have favorable performance. In particular, in our simulation setting, we find the proposed approach favorably controls the type-1 error for falsely identifying heterogeneity with higher power and classification accuracy for substantial heterogeneous effects. Two real data applications are provided for publicly available datasets from a clinical trial in oncology, and HIV.

The impact of grazing on biodiversity and forest succession in the Brazilian dry forest region is constrained by non-equilibrium dynamics.

The impacts of grazing on rangelands have historically been studied within the framework of the equilibrium model, which predicts significant impacts of grazing on ecosystems. However, in recent decades, studies have observed a non-equilibrium pattern, suggesting that abiotic factors play a primary role compared to grazing. These studies are primarily focused on rangelands, despite animal husbandry occurring in other biomes, such as seasonally dry tropical forests. Our study examines the influence of goat grazing on biodiversity and forest succession in the Brazilian dry forest (Caatinga). Considering its high interannual precipitation variability, we hypothesize a response that aligns with the non-equilibrium paradigm. We established a gradient of grazing intensity and history in areas at different stages of vegetation succession. A survey of tree - shrub and herbaceous species was conducted at each site and the biomass of both strata was quantified. Linear mixed models and Permanova were employed to assess differences in richness, composition, structure, and biomass among the areas. Our results suggest that grazing (history and intensity) and forest fallow age did not affect species richness, but only species composition. Low and high grazing intensity drive ecosystems toward similar compositions, which align with the non-equilibrium model predictions. Biomass in the herbaceous layer remained unaffected by grazing history, intensity, or forest fallow age, whereas woody biomass was influenced by grazing intensity in older forest fallows. Although trees in low-intensity grazing sites were significantly taller compared to those in other levels, overall, grazing did not disrupt the natural succession process. Older forest fallows exhibited greater diversity and higher basal area compared to new forest fallows, irrespective of grazing intensity. Our findings suggest that: a) grazing has minimal effects on biodiversity and biomass due to non-equilibrium dynamics, and b) with appropriate management, grazing can coexist with the conservation of the Caatinga.

[Prediction Model of Groundwater Sulphate Based on Combined Multi-source Spatio-temporal Data].

The accurate prediction of spatial variation trends in groundwater SO42- is of great significance for improving groundwater quality and regional groundwater management level. The multi-source spatio-temporal data such as land cover data, soil parameter data, digital elevation data, and groundwater pH value in the plain area of the Yarkant River Basin in 2011, 2014, 2017, and 2020 were used as characteristic variables to analyze their correlation with groundwater SO42- concentration. To enhance the prediction accuracy, the Bayesian optimization algorithm (BOA) was used to optimize the random forest regression (RFR). Based on the BOA-RFR model, the importance of the characteristic variables was analyzed, the prediction accuracy of the model was evaluated, and the groundwater SO42- prediction map was generated. The results showed that pH value, ground elevation (GE), and percentage of bare land (BAR) in the contribution area were important parameters influencing groundwater hydrochemical composition, which were significantly negatively correlated with groundwater SO42- concentration, and the importance of impact factors for predicting groundwater SO42- concentration exceeded 25 %. The geostatistical interpolation method was used as an auxiliary tool for the predictive modeling of spatial distribution. After adding auxiliary samples, the R2 of groundwater SO42- concentration prediction of the BOA-RFR model was greater than 0.96, and the maximum values of RMSE and MAE were reduced by 4.7 % and 23.8 %, respectively, compared with the minimum values of the model with fewer samples. The SO42- concentration prediction map showed that high SO42- groundwater was enriched in the northeast of the plain area of the Yarkand River Basin, an area that was expanding.

Comparative study of urea-15N fate in pure bamboo and bamboo-broadleaf mixed forests.

Objectives: Bamboo is a globally significant plant with ecological, environmental, and economic bene-fits. Choosing suitable native tree species for mixed planting in bamboo forests is an effective measure for achieving both ecological and economic benefits of bamboo forests. However, little is currently known about the impact of bamboo forests on nitrogen cycling and utilization efficiency after mixing with other tree species. Therefore, our study aims to compare the nitrogen cycling in pure bamboo forests with that in mixed forests. Methods: Through field experiments, we investigated pure Qiongzhuea tumidinoda forests and Q. tumidinoda-Phellodendron chinense mixed forests, and utilized 15N tracing technology to explore the fertilization effects and fate of urea-15N in different forest stands. Results: The results demonstrated the following: 1) in both forest stands, bamboo culms account for the highest biomass percentage (42.99%-51.86%), while the leaves exhibited the highest nitrogen concentration and total nitrogen uptake (39.25%-44.52%/29.51%-33.21%, respectively) Additionally, the average nitrogen uptake rate of one-year-old bamboo is higher (0.25 mg kg-1 a-1) compared to other age groups. 2) the urea-15N absorption in mixed forests (1066.51-1141.61 g ha-1, including 949.65-1000.07 g ha-1 for bamboo and 116.86-141.54 g ha-1 for trees) was significantly higher than that in pure forests (663.93-727.62 g ha-1, P<0.05). Additionally, the 15N recovery efficiency of culms, branches, leaves, stumps, and stump roots in mixed forests was significantly higher than that in pure forests, with increases of 43.14%, 69.09%, 36.84%, 51.63%, 69.18%, 34.60%, and 26.89%, respectively. 3) the recovery efficiency of urea-15N in mixed forests (45.81%, comprising 40.43% for bamboo and 5.38% for trees) and the residual urea-15N recovery rate in the 0-60 cm soil layer (23.46%) are significantly higher compared to those in pure forests (28.61%/18.89%). This could be attributed to the nitrogen losses in mixed forests (30.73%, including losses from ammonia volatilization, runoff, leaching, and nitrification-denitrification) being significantly lower than those in pure forests (52.50%). Conclusion: These findings suggest that compared to pure bamboo forests, bamboo in mixed forests exhibits higher nitrogen recovery efficiency, particularly with one-year-old bamboo playing a crucial role.

High-Resolution Spatiotemporal Modeling for PM2.5 Major Components in the Pearl River Delta and Its Implications for Epidemiological Studies.

Distinguishing the effects of different fine particulate matter components (PMCs) is crucial for mitigating their effects on human health. However, the sparse distribution of locations where PM is collected for component analysis makes it challenging to investigate the relevant health effects. This study aimed to investigate the agreement between data-fusion-enhanced exposure assessment and site monitoring data in estimating the effects of PMCs on gestational diabetes mellitus (GDM). We first improved the spatial resolution and accuracy of exposure assessment for five major PMCs (EC, OM, NO3-, NH4+, and SO42-) in the Pearl River Delta region by a data fusion model that combined inputs from multiple sources using a random forest model (10-fold cross-validation R2: 0.52 to 0.61; root mean square error: 0.55 to 2.26 µg/m3). Next, we compared the associations between exposures to PMCs during pregnancy and GDM in a hospital-based cohort of 1148 pregnant women in Heshan, China, using both site monitoring data and data-fusion model estimates. The comparative analysis showed that the data-fusion-based exposure generated stronger estimates of identifying statistical disparities. This study suggests that data-fusion-enhanced estimates can improve exposure assessment and potentially mitigate the misclassification of population exposure arising from the utilization of site monitoring data.

Tree inventory data from permanent plots in French forest reserves.

We present a data set resulting from the first round of a national monitoring program of forest reserves. It contains 9538 permanent plots, distributed across 111 study sites in mainland France (including Corsica). Notably focusing on dead wood measurement, this protocol has primarily been applied in strict forest reserves and special nature reserves (sensu Bollmann & Braunisch 2013), with 68% (6494) of the plots being currently located in strict forest reserves (unmanaged) and 24.7% (2363 plots) in forests unmanaged for at least 50 years. Sites cover a large variety of ecological conditions, from lowland to subalpine forests, but with an underrepresentation of Mediterranean forests (Table 1). The protocol assesses all the stages of a tree's life cycle, from seedling to decomposed lying dead wood. On each plot, a combination of three sampling techniques was used: (1) fixed-area inventory for regeneration, standing dead trees, living trees, and coarse woody debris (CWD) with diameter over 30 cm; (2) transect lines for CWD with diameter <30 cm; and (3) fixed-angle plot method for living trees with diameter at breast height (DBH) >30 cm (using a relascopic angle of 3%). Measurements include exact tree location (azimuth, distance), species, diameter(s), tree-related microhabitats, decay stage and bark cover, and seedling cover. With ongoing climate change, the program network can also provide important information to monitor changes in forest ecosystems. It can also be used as forest management monitoring or conservation status assessment. These data are freely available for noncommercial scientific use (Creative Commons Attribution 4.0 CC BY SA 4.0) with attribution, and this paper must be cited if this material is reused.

Long-term annual estimation of forest above ground biomass, canopy cover, and height from airborne and spaceborne sensors synergies in the Iberian Peninsula.

The Mediterranean Basin has experienced substantial land use changes as traditional agriculture decreased and population migrated from rural to urban areas, which have resulted in a large forest cover increase. The combination of Landsat time series, providing spectral information, with lidar, offering three-dimensional insights, has emerged as a viable option for the large-scale cartography of forest structural attributes across large time spans. Here we develop and test a comprehensive framework to map forest above ground biomass, canopy cover and forest height in two regions spanning the most representative biomes in the peninsular Spain, Mediterranean (Madrid region) and temperate (Basque Country). As reference, we used lidar-based direct estimates of stand height and forest canopy cover. The reference biomass and volume were predicted from lidar metrics. Landsat time series predictors included annual temporal profiles of band reflectance and vegetation indices for the 1985-2023 period. Additional predictor variables including synthetic aperture radar, disturbance history, topography and forest type were also evaluated to optimize forest structural attributes retrieval. The estimates were independently validated at two temporal scales, i) the year of model calibration and ii) the year of the second lidar survey. The final models used as predictor variables only Landsat based metrics and topographic information, as the available SAR time-series were relatively short (1991-2011) and disturbance information did not decrease the estimation error. Model accuracies were higher in the Mediterranean forests when compared to the temperate forests (R2 = 0.6-0.8 vs. 0.4-0.5). Between the first (1985-1989) and the last (2020-2023) decades of the monitoring period the average forest cover increased from 21 ± 2% to 32 ± 1%, mean height increased from 6.6 ± 0.43 m to 7.9 ± 0.18 m and the mean biomass from 31.9 ± 3.6 t ha-1 to 50.4 ± 1 t ha-1 for the Mediterranean forests. In temperate forests, the average canopy cover increased from 55 ± 4% to 59 ± 3%, mean height increased from 15.8 ± 0.77 m to 17.3 ± 0.21m, while the growing stock volume increased from 137.8 ± 8.2 to 151.5 ± 3.8 m3 ha-1. Our results suggest that multispectral data can be successfully linked with lidar to provide continuous information on forest height, cover, and biomass trends.

Coordination of water use strategies and leaf economic traits in coexisting exotic and native woody species from evergreen and deciduous broadleaf forests.

The leaf economics spectrum (LES) describes the covariation of traits relevant for carbon and nutrient economy in different plant species. However, much less is known about the correlation of LES with leaf water economy, not only because some woody species do not follow the rules, but also because they are rarely tested on the widespread, non-native, fast-growing trees. We hypothesized that fast-growing exotic species that spread on the fast side of the LES coordinate their water-use strategies (WUS) to maintain rapid growth, and that the pattern of coordination differs between evergreen and deciduous forests. Using 4 exotic and 4 native species from evergreen and deciduous broadleaf forests in China, we measured 17 traits of LES and WUS and analyzed their functional roles in different species groups. Our results suggest that LES plays a more important role in the coexistence of species within a community, while WUS contributes more to the distribution of species across different regions. The multidimensional coordination of LES and WUS could better explain the growth and distribution of different plant species and shed light on the coexistence of species from different forest types, especially fast-growing woody exotics.

Aridity affects soil organic carbon concentration and chemical stability by different forest types and soil processes across Chinese natural forests.

Forest soils play a critical role in carbon (C) reservoirs and climate change mitigation globally. Exploring the driving factors of soil organic carbon (SOC) concentration and stability in forests on a large spatial scale can help us evaluate the role of forest soils in regulating C sequestration. Based on SOC quantification and solid-state 13C nuclear magnetic resonance spectroscopy, we investigated the SOC concentration and SOC chemical stability (indicated by alkyl-to-O-alkyl ratio and hydrophobic-to-hydrophilic ratio) in top 0-5 and 5-10 cm soils from 65 Chinese natural forest sites and explored their driving factors. Results showed that SOC concentration in 0-5 cm soils were highest in mixed forests but SOC chemical stability in 0-5 cm soils were highest in coniferous forests, while SOC concentration and chemical stability in 5-10 cm soil layers did not differ across forest types. SOC concentration in 0-5 cm was directly related to soil pH and soil bacterial diversity. Structural equation models showed that aridity indirectly affected SOC concentration in 0-5 cm by directly affecting soil pH. While SOC chemical stability in 0-5 cm soils was higher with increased aridity. According to the correlations, the potential mechanisms could be attributed to higher proportion of coniferous forests in more arid forest sites, lower relative abundance of O-alkyl C, higher MgO and CaO contents, and higher bacterial diversity in soils from more arid forest sites. Our study reveals the important role of aridity in mediating SOC concentration and chemical stability in top 0-5 cm soils in Chinese natural forests on a large-scale field investigation. These results will help us better understand the different mechanisms underlying SOC concentration and stability in forests and assess the feedback of forest SOC to future climate change.

Dispersal limitation and environmental filtering effects: The taxonomic and functional beta diversity of ground beetles along the altitudinal gradient in Chinese warm-temperature forests.

Beta diversity patterns along environmental gradients and underlying mechanisms constitute key research inquiries in biogeography. However, ecological processes often also influence the functional traits of biological communities, making the assessment of functional ß-diversity crucial. Ground beetles (Coleoptera: Carabidae) are one of the most species-rich groups in the insect community, displaying strong habitat specificity and morphological differences. In this study, we explored the patterns of taxonomic and functional beta diversity in ground beetle communities along the altitudinal gradient of warm-temperature forests. By partitioning beta diversity into turnover and nestedness components, we evaluated their relationship with spatial distance. Our findings indicate a decline in species and functional trait similarity with increasing elevation and geographic distance. Further analysis attributed both types of beta diversity in carabids to a combination of dispersal limitation and environmental filtering, with elevation and geographic distance emerging as significant factors. Interestingly, forest-type variations were found to have no impact on the beta diversity of these communities. Our study reveals the impact of environmental filtering and dispersal limitation on both taxonomic and functional beta-diversity, shedding light on carabid community assembly in localized warm-temperature forest areas in eastern China.

Adaptive forest management improves stand-level resilience of temperate forests under multiple stressors.

Forests are expected to be strongly affected by modifications in climate and disturbance regimes, threatening their ability to sustain the provision of essential services. Promoting drought-tolerant species or functionally diverse stands have recently emerged as management options to cope with global change. Our study aimed at evaluating the impact of contrasting stand-level management scenarios on the resilience of temperate forests in eastern North America and central-western Europe using the individual process-based model HETEROFOR. We simulated the evolution of eight stands over 100 years under a future extreme climate according to four management scenarios (business as usual - BAU; climate change adaptation - CC; functional diversity approach - FD; no management - NM) while facing multiple disturbances, resulting in a total of 160 simulations. We found that FD demonstrated the greatest resilience regarding transpiration and tree biomass, followed by CC and then BAU, while these three scenarios were equivalent concerning the net primary production. These results were however dependent on forest type: increasing functional diversity was a powerful option to increase the resilience of coniferous plantations whereas no clear differences between BAU and adaptive management scenarios were detected in broadleaved and mixed stands. The FD promoted a higher level of tree species diversity than any other scenario, and all scenarios of management were similar regarding the amount of harvested wood. The NM always showed the lowest resilience, demonstrating that forest management could be an important tool to mitigate adverse effects of global change. Our study highlighted that tree-level process-based models are a relevant tool to identify suitable management options for adapting forests to global change provided that model limitations are considered, and that alternative management options, particularly those based on functional diversity, are promising and should be promoted from now on.

A common ericoid shrub modulates the diversity and structure of fungal communities across an arbuscular to ectomycorrhizal tree dominance gradient.

Differences between arbuscular (AM) and ectomycorrhizal (EcM) trees strongly influence forest ecosystem processes, in part through their impact on saprotrophic fungal communities. Ericoid mycorrhizal (ErM) shrubs likely also impact saprotrophic communities given that they can shape nutrient cycling by slowing decomposition rates and intensifying nitrogen limitation. We investigated the depth distributions of saprotrophic and EcM fungal communities in paired subplots with and without a common understory ErM shrub, mountain laurel (Kalmia latifolia L.), across an AM to EcM tree dominance gradient in a temperate forest by analyzing soils from the organic, upper mineral (0-10 cm), and lower mineral (cumulative depth of 30 cm) horizons. The presence of K. latifolia was strongly associated with the taxonomic and functional composition of saprotrophic and ectomycorrhizal communities. Saprotrophic richness was consistently lower in the Oa horizon when this ErM shrub species was present. However, in AM tree dominated plots, the presence of the ErM shrub was associated with a higher relative abundance of saprotrophs. Given that EcM trees suppress both the diversity and relative abundance of saprotrophic communities, our results suggest that separate consideration of ErM shrubs and EcM trees may be necessary when assessing the impacts of plant mycorrhizal associations on belowground communities.