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.

How do microtine rodent abundance, snow and landscape parameters influence pine marten Martes martes population dynamics?

The pine marten (Martes martes) occupies the northernmost extent of its distribution in Norway, where microtine rodents are an important food item. The relationship between microtine rodent abundance and pine marten population dynamics is not well understood. In this paper, we examined this relationship and tested if environmental factors (e.g. snow depth, elevation, mature spruce forest density and agricultural land density) modulate pine marten population dynamics. We calculated pine marten abundance indices using data collected from 593 unique snow transects surveyed between 2003 and 2014 in Hedmark, Norway. We employed a Partial Rate Correlation Function to identify potential cyclicity in pine marten populations. We did not observe any cyclical patterns in pine marten populations within our short time series. Instead, their population appeared to be directly density-dependent. Although the population growth rate of pine marten tended to increase with increasing elevation, it was not affected by individual variables including a microtine rodent abundance index and snow depth. However, the annual growth rate of pine marten populations was positively affected by the interaction between the microtine rodent abundance index and increasing elevation. Pine marten abundance increased with microtine rodent abundance, elevation, snow depth and density of mature spruce forest, but decreased with increasing agricultural land density. Pine martens are opportunistic diet generalists that can switch between prey and cache food for later consumption. They are also strongly territorial with delayed implantation and are slow to respond to environmental changes due to their relatively low reproductive potential. These life-history traits may mitigate the effects of fluctuating microtine rodent abundance on pine marten reproduction and survival. Nevertheless, our findings suggest that microtine rodents still serve as important prey which can influence the population dynamics of pine martens in higher elevation habitats where alternative prey may be less available.

A leaf age-dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests.

Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one-third of terrestrial gross primary productivity (GPP). However, the continental-scale leaf phenology-photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs. Leaf age is the crucial plant trait to link the dynamics of leaf phenology with GPP seasonality. Thus, here we incorporated the seasonal leaf area index of different leaf age cohorts into a widely used LUE model (i.e., EC-LUE) and proposed a novel leaf age-dependent LUE model (denoted as LA-LUE model). At the site level, the LA-LUE model (average R2 = .59, average root-mean-square error [RMSE] = 1.23 gC m-2 day-1) performs better than the EC-LUE model in simulating the GPP seasonality across the nine TEFs sites (average R2 = .18; average RMSE = 1.87 gC m-2 day-1). At the continental scale, the monthly GPP estimates from the LA-LUE model are consistent with FLUXCOM GPP data (R2 = .80; average RMSE = 1.74 gC m-2 day-1), and satellite-based GPP data retrieved from the global Orbiting Carbon Observatory-2 (OCO-2) based solar-induced chlorophyll fluorescence (SIF) product (GOSIF) (R2 = .64; average RMSE = 1.90 gC m-2 day-1) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R2 = .78; average RMSE = 1.88 gC m-2 day-1). Typically, the estimated monthly GPP not only successfully represents the unimodal GPP seasonality near the Tropics of Cancer and Capricorn, but also captures well the bimodal GPP seasonality near the Equator. Overall, this study for the first time integrates the leaf age information into the satellite-based LUE model and provides a feasible implementation for mapping the continental-scale GPP seasonality over the entire TEFs.

Radio Signal Modulation Recognition Method Based on Hybrid Feature and Ensemble Learning: For Radar and Jamming Signals.

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning.

Response to the detection of Rugonectria castaneicola and Rugonectria wingfieldii sp. nov. on Quercus in Australia.

Here we report on the detection and surveillance response to two Rugonectria species found in Sydney, Australia, in 2015. Both Rugonectria castaneicola and R. wingfieldii sp. nov. were found in association with cankers on Quercus robur (English oak). The fungi were initially found to be localised on amenity trees in northern Sydney, New South Wales, and as they were new detections for Australia, eradication was considered. Ongoing surveillance across the Sydney basin, regional New South Wales, and the Australian Capital Territory, however, indicated that they were already well established. Species identities were confirmed through morphological examination and molecular barcoding, with the subsequent analysis undertaken to classify R. wingfieldii sp. nov. This study provides the first records of Rugonectria found in association with canker on Oak trees in Australia. Citation: Trollip C, Carnegie AJ, Anderson C, Priest MJ, Gorrie B, Daly A (2024). Response to the detection of Rugonectria castaneicola and Rugonectria wingfieldii sp. nov. on Quercus in Australia. Fungal Systematics and Evolution 13: 123-130. doi: 10.3114/fuse.2024.13.06.

Land use and land cover changes, and woody vegetation status of the Tsimur Gebriel Monastery in Northern Ethiopia.

Ethiopian Orthodox churches are significant habitats for endemic and threatened plant species, yet their vegetation status and the land use systems impacting them, are little known. Therefore, this study assessed the land use and land cover changes (LULCC) within a 3 km buffer area and the woody vegetation status of the Tsimur Gebriel Monastery in the Tigray region, Northern Ethiopia. The United States Geological Survey's multi-dated (1986, 1999, and 2018) Landsat imagery was used for LULCC analysis. A supervised classification technique was employed for image classification using a maximum likelihood algorithm. Systematic sampling techniques were used to collect vegetation data (tree species, height, and DBH), using 20 sample plots (20 × 20 m) distanced 100 m apart. The results highlighted that among the five identified LULCC types in the buffer zone of the monastery, the farmland area has expanded from 56 to 78 % at the expense of shrublands between 1986 and 2018. At the monastery, 19 woody tree species from 13 families were identified, with an evenness of 0.5 and a Shannon diversity index of 2.4. The stem density was 336 stems per hectare, and the forest cover was approximately 65 %. Olea europaea was the dominant tree species, while Juniperus procera showed a lack of regeneration at the monastery. Despite the fair natural regeneration, the monastery exhibited lower species diversity, richness, and evenness. However, the monastery remains an important habitat for rare and threatened tree species and may supply seeds for the restoration of degraded lands. Therefore, establishing exclosures in the buffer zone, strengthening stone walls and enrichment planting of degraded tree species should be implemented to ensure the sustainable conservation of valuable tree species.

Interspecific competition shapes bird species' distributions along tropical precipitation gradients.

The hypothesis that species' ranges are limited by interspecific competition has motivated decades of debate, but a general answer remains elusive. Here we test this hypothesis for lowland tropical birds by examining species' precipitation niche breadths. We focus on precipitation because it-not temperature-is the dominant climate variable that shapes the biota of the lowland tropics. We used 3.6 million fine-scale citizen science records from eBird to measure species' precipitation niche breadths in 19 different regions across the globe. Consistent with the predictions of the interspecific competition hypothesis, multiple lines of evidence show that species have narrower precipitation niches in regions with more species. This means species inhabit more specialized precipitation niches in species-rich regions. We predict this niche specialization should make tropical species in high diversity regions disproportionately vulnerable to changes in precipitation regimes; preliminary empirical evidence is consistent with this prediction.

Understanding the impact of radiotherapy fractionation on overall survival in a large head and neck squamous cell carcinoma dataset: a comprehensive approach combining mechanistic and machine learning models.

Introduction: Treating head and neck squamous cell carcinomas (HNSCC), especially human papillomavirus negative (HPV-) and locally advanced cases, remains difficult. Our previous analyses of radiotherapy-only HNSCC clinical trials data using mechanistically-motivated models of tumor repopulation and killing by radiotherapy predicted that hyperfractionation with twice-daily fractions, or hypofractionation involving increased doses/fraction and reduced treatment durations, both improve tumor control and reduce late normal tissue toxicity, compared with standard protocols using 35×2 Gy. Here we further investigated the validity of these conclusions by analyzing a large modern dataset on 3,346 HNSCC radiotherapy patients from the University Health Network in Toronto, Canada, where 42.5% of patients were also treated with chemotherapy. Methods: We used a two-step approach that combines mechanistic modeling concepts with state-of-the-art machine learning, beginning with Random Survival Forests (RSF) for an exploratory analysis and followed by Causal Survival Forests (CSF) for a focused causal analysis. The mechanistic concept of biologically effective dose (BED) was implemented for the standard dose-independent (DI) tumor repopulation model, our alternative dose-dependent (DD) repopulation model, and a simple model with no repopulation (BEDsimp). These BED variants were included in the RSF model, along with age, stage, HPV status and other relevant variables, to predict patient overall survival (OS) and cause-specific mortality (deaths from the index cancer, other cancers or other causes). Results: Model interpretation using Shapley Additive Explanations (SHAP) values and correlation matrices showed that high values of BEDDD or BEDDI, but not BEDsimp, were associated with decreased patient mortality. Targeted causal inference analyses were then performed using CSF to estimate the causal effect of each BED variant on OS. They revealed that high BEDDD (>61.8 Gy) or BEDDI (>57.6 Gy), but not BEDsimp, increased patient restricted mean survival time (RMST) by 0.5-1.0 years and increased survival probability (SP) by 5-15% several years after treatment. In addition to population-level averages, CSF generated individual-level causal effect estimates for each patient, facilitating personalized medicine. Discussion: These findings are generally consistent with those of our previous mechanistic modeling, implying the potential benefits of altered radiotherapy fractionation schemes (e.g. 25×2.4 Gy, 20×2.75 Gy, 18×3.0 Gy) which increase BEDDD and BEDDI and counteract tumor repopulation more effectively than standard fractionation. Such regimens may represent potentially useful hypofractionated options for treating HNSCC.

Accurate space-group prediction from composition.

Predicting crystal symmetry simply from chemical composition has remained challenging. Several machine-learning approaches can be employed, but the predictive value of popular crystallographic databases is relatively modest due to the paucity of data and uneven distribution across the 230 space groups. In this work, virtually all crystallographic information available to science has been compiled and used to train and test multiple machine-learning models. Composition-driven random-forest classification relying on a large set of descriptors showed the best performance. The predictive models for crystal system, Bravais lattice, point group and space group of inorganic compounds are made publicly available as easy-to-use software downloadable from https://gitlab.com/vishsoft/cosy.

Isolated, neglected, and likely threatened: a new species of Magoniella (Polygonaceae) from the seasonally dry tropical forests of Northern Colombia and Venezuela revealed from nuclear, plastid, and morphological data.

Seasonally tropical dry forests (SDTFs) in the American tropics are a highly diverse yet poorly understood and endangered ecosystem scattered from Northern Mexico to Southern Argentina. One floristic element of the STDFs is the genus Magoniella (Polygonaceae), which includes two liana species, M. laurifolia and M. obidensis, which have winged fruits and are distributed from Costa Rica to Southern Brazil. In a field expedition to the SDTFs of the Colombian Caribbean in 2015, morphologically distinctive individuals of Magoniella were found. In this study, we investigated the species boundaries within Magoniella and determined the phylogenetic position of these morphologically distinctive individuals in the tribe Triplaridae. We compiled morphological trait data across 19 specimens of both species and produced newly sequenced nuclear-plastid DNA data for M. obidensis. Morphometric analyses revealed significant differences in fruit length and perianth size among individuals from the Colombian Caribbean compared to M. obidensis and bract length when compared to M. laurifolia. Maximum likelihood analysis of non-conflicting nuclear and plastid datasets placed the Colombian Caribbean individuals as sister to M. obidensis with maximum statistical support. Additionally, pairwise sequence comparisons of the nuclear ribosomal ITS and the lfy2i loci consistently showed 15-point mutations (10 transitions, five transversions) and six 2 bp-long substitutions that differ between M. obidensis and the Colombian Caribbean individuals. Our morphological and molecular evidence thus suggests that the Colombian Caribbean individuals of Magoniella represent a divergent population from M. laurifolia and M. obidensis, which we describe and illustrate as a new species, M. chersina. Additionally, we provide nomenclatural updates for M. laurifolia and M. obidensis. This study highlights the power of combining morphological and molecular evidence in documenting and naming plant diversity.

The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.

Although methods in diagnosis and therapy of hepatocellular carcinoma (HCC) have made significant progress in the past decades, the overall survival (OS) of liver cancer is still disappointing. Machine learning models have several advantages over traditional cox models in prognostic prediction. This study aimed at designing an optimal panel and constructing an optimal machine learning model in predicting prognosis for HCC. A total of 941 HCC patients with completed survival data and preoperative clinical chemistry and immunology indicators from two medical centers were included. The OCC panel was designed by univariate and multivariate cox regression analysis. Subsequently, cox model and machine-learning models were established and assessed for predicting OS and PFS in discovery cohort and internal validation cohort. The best OCC model was validated in the external validation cohort and analyzed in different subgroups. In discovery, internal and external validation cohort, C-indexes of our optimal OCC model were 0.871 (95% CI, 0.863-0.878), 0.692 (95% CI, 0.667-0.717) and 0.648 (95% CI, 0.630-0.667), respectively; the 2-year AUCs of OCC model were 0.939 (95% CI, 0.920-0.959), 0.738 (95% CI, 0.667-0.809) and 0.725 (95% CI, 0.643-0.808), respectively. For subgroup analysis of HCC patients with HBV, aged less than 65, cirrhosis or resection as first therapy, C-indexes of our optimal OCC model were 0.772 (95% CI, 0.752-0.792), 0.769 (95% CI, 0.750-0.789), 0.855 (95% CI, 0.846-0.864) and 0.760 (95% CI, 0.741-0.778), respectively. In general, the optimal OCC model based on RSF algorithm shows prognostic guidance value in HCC patients undergoing individualized treatment.

Random forests for the analysis of matched case-control studies.

BACKGROUND: Conditional logistic regression trees have been proposed as a flexible alternative to the standard method of conditional logistic regression for the analysis of matched case-control studies. While they allow to avoid the strict assumption of linearity and automatically incorporate interactions, conditional logistic regression trees may suffer from a relatively high variability. Further machine learning methods for the analysis of matched case-control studies are missing because conventional machine learning methods cannot handle the matched structure of the data. RESULTS: A random forest method for the analysis of matched case-control studies based on conditional logistic regression trees is proposed, which overcomes the issue of high variability. It provides an accurate estimation of exposure effects while being more flexible in the functional form of covariate effects. The efficacy of the method is illustrated in a simulation study and within an application to real-world data from a matched case-control study on the effect of regular participation in cervical cancer screening on the development of cervical cancer. CONCLUSIONS: The proposed random forest method is a promising add-on to the toolbox for the analysis of matched case-control studies and addresses the need for machine-learning methods in this field. It provides a more flexible approach compared to the standard method of conditional logistic regression, but also compared to conditional logistic regression trees. It allows for non-linearity and the automatic inclusion of interaction effects and is suitable both for exploratory and explanatory analyses.

Identification of Dementia & Mild Cognitive Impairment in Chinese Elderly Using Machine Learning.

OBJECTIVE: To assess the role of Machine Learning (ML) in identification critical factors of dementia and mild cognitive impairment. METHODS: 371 elderly individuals were ultimately included in the ML analysis. Demographic information (including gender, age, parity, visual acuity, auditory function, mobility, and medication history) and 35 features from 10 assessment scales were used for modeling. Five machine learning classifiers were used for evaluation, employing a procedure involving feature extraction, selection, model training, and performance assessment to identify key indicative factors. RESULTS: The Random Forest model, after data preprocessing, Information Gain, and Meta-analysis, utilized three training features and four meta-features, achieving an area under the curve of 0.961 and a accuracy of 0.894, showcasing exceptional accuracy for the identification of dementia and mild cognitive impairment. CONCLUSIONS: ML serves as a identification tool for dementia and mild cognitive impairment. Using Information Gain and Meta-feature analysis, Clinical Dementia Rating (CDR) and Neuropsychiatric Inventory (NPI) scale information emerged as crucial for training the Random Forest model.

Impact of transforming karst mountainous forests into urban parks on plant diversity patterns.

The conversion of urban montane forest resources into urban parks requires careful assessment to understand its impacts on plant diversity over time. This study aims to enhance urban biodiversity conservation strategies by analyzing how habitat type and park age affect woody plant diversity. We surveyed woody plant species in 60 sample plots across two different habitats (remnant forest vs. artificial green space) within three mountain parks in Guiyang, China, established at different times. The alpha diversity of saplings/seedlings was significantly higher in remnant forests than in artificial green spaces. Artificial green spaces exhibited more homogenous woody plant composition compared with remnant forests. Newer parks showed greater variation in plant composition between the two habitats than older parks. Indicative species in remnant forests were predominantly native, whereas those in artificial green spaces were mainly ornamental species. The transformation of karst mountainous forests into urban parks leads to the homogenization of woody plant composition and impedes the regeneration of saplings/seedlings. Therefore, it is crucial to manage these conversions carefully and strive to preserve as many native species as possible to support urban plant diversity conservation.

Seasonal timing of fluorescence and photosynthetic yields at needle and canopy scales in evergreen needleleaf forests.

The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive "greenness" of these systems. We combine continuous needle-scale chlorophyll fluorescence measurements with tower-based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar-induced chlorophyll fluorescence (SIF) and solar intensity-normalized SIF (SIFrelative) under snow-covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near-infrared vegetation index [NIRv] under 50% snow cover). Needle-scale fluorescence and photochemistry strongly correlated (r2 = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower-based estimates of LUEP and SIFrelative which were well correlated across all sites (r2 = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data-driven and process-based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems.

Optimistic growth of marginal region plantations under climate warming: Assessing divergent drought resilience.

Given the context of significant global warming and the intensification of extreme climate events in the last century, large-scale reforestation and afforestation have been recognized as effective strategies to mitigate the climate crisis. Since the 1970s, China has launched several afforestation programs aimed at regional ecological protection, playing an important role in reaching carbon neutrality by 2060. This study provided a detailed analysis of the growth suitability of the main planted conifers (Pinus sylvestris var. mongolica and Pinus tabulaeformis) and broadleaves (Populus spp., Robinia pseudoacacia) in the semi-arid northern China. We compared the radial growth trends of plantations and their responses to extreme droughts from 1980 to 2018. Growth of most plantations has significantly increased over time, but broadleaves showed recent growth reductions in the past decade, which may be related to tree age and reduced soil moisture. Nevertheless, under warmer climate scenarios, the growth of plantations is forecasted to continue increasing. Broadleaves showed a better post-drought recovery, probably linked to their anisohydric behavior, than conifers, which presented a better resistance to drought. Growth of conifers depended more on warmer temperature and better precipitation conditions during the growing season, whereas broadleaves mainly reacted to warm temperature. Additionally, pre-drought growth levels weakened resilience components, while post-drought precipitation compensated for drought-induced growth deficit. Growth and resilience were negatively related to tree age, while higher stand density reduced growth. This assessment and projections of growth and drought resilience indicate the sustainability of most plantations in semi-arid regions, but future warmer and drier conditions may lead to an uncertain future regarding forest health and reduce their carbon sink potential.

Poor compliance and exemptions facilitate ongoing deforestation.

Many nations are struggling to reduce deforestation, despite having extensive environmental protection laws in place and commitments to international agreements that address the biodiversity and climate crises. We developed a novel framework to quantify the extent to which contemporary deforestation is being captured under national and subnational laws. We then applied this framework to northern Australia as a case study, a development and deforestation hotspot with ecosystems of global significance. First, deforestation may be compliant under all relevant legislation, either through assessment and approval or because of exemptions in the legislation. Second, deforestation may be compliant under at least one relevant law, but not all. Third, there may be no evidence of deforestation assessment or exemption from assessment, despite their apparent requirement, which could mean the deforestation is potentially noncompliant. Finally, deforestation may occur in an area or under circumstances that are beyond the intended scope of any relevant legislation. All deforestation that we analyzed was hypothetically covered by one or more laws. However, 65% of deforestation was potentially noncompliant with at least one law. Because multiple laws could be relevant to a given clearing event, the majority of clearing was still compliant with at least one law, but of these events, only a small proportion was explicitly approved (19%). The remaining were permitted under various exemptions. Of all the legislation we analyzed, most of the exempt clearing occurred under one subnational law and most potentially noncompliant clearing occurred under one national law. Our results showed that even a nation with a suite of mature environmental protection laws is falling well short of achieving international commitments regarding deforestation. Our framework can be used to pinpoint the pathways of policy change required for nations to align local laws with these international accords.

Cumplimiento deficiente y exenciones que facilitan la deforestación Resumen Muchos países luchan por reducir la deforestación, a pesar de contar con amplias leyes de protección del medio ambiente y de sus compromisos con los acuerdos internacionales que abordan la crisis de la biodiversidad y el clima. Por ello desarrollamos un novedoso marco para cuantificar hasta qué punto la deforestación actual se recopila en las leyes nacionales y subnacionales. Después aplicamos este marco al norte de Australia como estudio de caso, un punto caliente de desarrollo y deforestación con ecosistemas de importancia mundial. En primer lugar, la deforestación puede ser compatible con toda la legislación pertinente, ya sea mediante evaluación y aprobación o debido a exenciones en la legislación. En segundo lugar, la deforestación puede ser compatible con al menos una ley pertinente, pero no con todas. En tercer lugar, puede que no haya pruebas de evaluación de la deforestación o de exención de la evaluación, a pesar de su aparente requisito, lo que podría significar que la deforestación es potencialmente no conforme. Por último, la deforestación puede producirse en una zona o en circunstancias que quedan fuera del ámbito de aplicación de la legislación pertinente. Toda la deforestación analizada era hipotéticamente legal según una o más leyes. Sin embargo, el 65% de la deforestación no cumplía potencialmente al menos una ley. Dado que varias leyes podían ser pertinentes para un determinado caso de deforestación, la mayoría de las deforestaciones seguían cumpliendo al menos una ley, pero de estos casos, sólo una pequeña proporción estaba explícitamente aprobada (19%). El resto estaba permitido en virtud de diversas exenciones. De toda la legislación que analizamos, la mayor parte de la compensación exenta se produjo en virtud de una ley subnacional y la mayor parte de la compensación potencialmente no conforme se produjo en virtud de una ley nacional. Nuestros resultados muestran que incluso un país con un conjunto de leyes maduras de protección del medio ambiente está muy lejos de cumplir los compromisos internacionales en materia de deforestación. Nuestro marco puede utilizarse para determinar las vías de cambio político necesarias para que los países adapten su legislación local a los acuerdos internacionales.

Tree richness increased biomass carbon sequestration and ecosystem stability of temperate forests in China: Interacted factors and implications.

Biodiversity loss and forest degradation have received increasing attention worldwide, and their effects on forest biomass carbon storage and stability have not yet been well defined. This study examined 1275 tree plots using the field survey method to quantify the effects of tree diversity, tree sizes, and mycorrhizal symbiont abundance on biomass carbon storages (Cs) and NDVI (Normalized Difference Vegetation Index)-based ecosystem stability (standard deviation/mean NDVI = NDVI_S) during the field survey period from 2008 to 2018. Our data showed Cs and NDVI_S averaged at 31-108 t ha-1 and 32.04-49.28, respectively, and positive relations between Cs and NDVI_S were observed (p < 0.05). Large forest-type and regional variations were found in these two parameters. Broadleaf forests had 74% of Cs (p < 0.05) of the conifer forests, but no differences were in NDVI_S. Cold regions at high latitudes had 71% of NDVI_S in the warm regions at low latitudes, while no differences were in Cs. Moist regions at high longitudes had 2.04 and 1.28-fold higher Cs and NDVI_S (p < 0.05). The >700 m a.s.l. regions had 1.24-fold higher Cs (p < 0.01) than the <700 m a.s.l. regions, but similar NDVI_S (p > 0.05). Nature Reserves had 1.94-fold higher Cs but 30% lower NDVI_S than outside Reserves (p < 0.001). > 40-year-old forests had 1.3- and 2-fold higher Cs and NDVI_S than the young forests. Structural equation modeling and hierarchical partitioning revealed the driving paths responsible for these variations. Tree richness was positively associated with Cs and ecosystem stability, contributing 21.6%-30.6% to the total effects on them; tree sizes significantly promoted the Cs, but had negligible impacts on NDVI_S. MAT's total effects on NDVI_S of conifer forests were 40% higher than that of broadleaf forests, MAP's total effects on Cs varied with forest types; arbuscular mycorrhizal tree dominance exhibited a smaller positive impact on Cs and ecosystem stability in comparison to other factors. Our findings underscore that the significance of climatic-adapted forest management, diversity conservation, and big-sized tree protections can support the achievement of carbon neutrality in China from biomass carbon sequestration and ecosystem stability.

Introduction of broadleaf tree species can promote the resource use efficiency and gross primary productivity of pure forests.

Long-term pure forest (PF) management and successive planting has result resulted in "low-efficiency artificial forests" in large areas. However, controversy persists over the promoting effect of introduction of broadleaf tree species on production efficiency of PF. This study hypothesised that introduced broadleaf tree species can significantly promote both water-nutrient use efficiency and gross primary productivity (GPP)of PF. Tree ring chronologies, water source, water use efficiency and GPP were analysed in coniferous Cunninghamia lanceolata and broadleaved Phoebe zhennan growing over the past three decades. The introduction of P. zhennan into C. lanceolata plantations resulted in inter-specific competition for water, probably because of the similarity of the main water source of these two tree species. However, C. lanceolata absorbed more water with a higher nutrient level from the 40-60-cm soil layer in mixed forests (MF). Although the co-existing tree species limited the basal area increment and growth rates of C. lanceolata in MF plots, the acquisition of dissolved nutrients from the fertile topsoil layer were enhanced; this increased the water use efficiency and GPP of MF plots. To achieve better ecological benefits and GPP, MFs should be constructed in southern China.