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.

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.

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.

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.

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.

Disentangling the veil line for Brazilian biodiversity: An overview from two long-term research programs reveals huge gaps in ecological data reporting.

The lack of synthesized information regarding biodiversity is a major problem among researchers, leading to a pervasive cycle where ecologists make field campaigns to collect information that already exists and yet has not been made available for a broader audience. This problem leads to long-lasting effects in public policies such as spending money multiple times to conduct similar studies in the same area. We aim to identify this knowledge gap by synthesizing information available regarding two Brazilian long-term biodiversity programs and the metadata generated by them. Using a unique dataset containing 1904 metadata, we identified patterns of metadata distribution and intensity of research conducted in Brazil, as well as where we should concentrate research efforts in the next decades. We found that the majority of metadata were about vertebrates, followed by plants, invertebrates, and fungi. Caating was the biome with least metadata, and that there's still a lack of information regarding all biomes in Brazil, with none of them being sufficiently sampled. We hope that these results will have implications for broader conservation and management guiding, as well as to funding allocation programs.

Tropical root responses to global changes: A synthesis.

Tropical ecosystems face escalating global change. These shifts can disrupt tropical forests' carbon (C) balance and impact root dynamics. Since roots perform essential functions such as resource acquisition and tissue protection, root responses can inform about the strategies and vulnerabilities of ecosystems facing present and future global changes. However, root trait dynamics are poorly understood, especially in tropical ecosystems. We analyzed existing research on tropical root responses to key global change drivers: warming, drought, flooding, cyclones, nitrogen (N) deposition, elevated (e) CO2, and fires. Based on tree species- and community-level literature, we obtained 266 root trait observations from 93 studies across 24 tropical countries. We found differences in the proportion of root responsiveness to global change among different global change drivers but not among root categories. In particular, we observed that tropical root systems responded to warming and eCO2 by increasing root biomass in species-scale studies. Drought increased the root: shoot ratio with no change in root biomass, indicating a decline in aboveground biomass. Despite N deposition being the most studied global change driver, it had some of the most variable effects on root characteristics, with few predictable responses. Episodic disturbances such as cyclones, fires, and flooding consistently resulted in a change in root trait expressions, with cyclones and fires increasing root production, potentially due to shifts in plant community and nutrient inputs, while flooding changed plant regulatory metabolisms due to low oxygen conditions. The data available to date clearly show that tropical forest root characteristics and dynamics are responding to global change, although in ways that are not always predictable. This synthesis indicates the need for replicated studies across root characteristics at species and community scales under different global change factors.

Detection of Changes in Soil Microbial Community Physiological Profiles in Relation to Forest Types and Presence of Antibiotics Using BIOLOG EcoPlate.

Soil is home to microbiota with diverse metabolic activities. These microorganisms play vital roles in many ecological processes. Thus, the assessment of microbial functional diversity is an important quality indicator of soil ecosystems. In this study, we collected soil samples from three distinct forest habitats, i.e., an agroforest, a primary forest (PF), and a secondary forest, within the Angat Watershed Reservation in Bulacan, Northern Philippines. Community-level physiological profiling (CLPP) was done with the BIOLOG EcoPlate™ to analyze the responses of the soil microbial communities from the three forest habitats in the absence or presence of antibiotics. The BIOLOG EcoPlate represents 31 utilizable carbon sources. Based on the CLPP analysis, soil samples from the PF showed significantly higher utilization of most carbon sources than the other forest types (p < 0.05). Thus, less disturbed forest types constitute more functionally diverse microbial communities. The presence of antibiotics significantly decreased the carbon utilization patterns of the soil microbial communities (p < 0.05), indicating the possible use of CLPP in monitoring contamination in soil.

Developing the Additive Systems of Stand Basal Area Model for Broad-Leaved Mixed Forests.

Stand basal area (SBA) is an important variable in the prediction of forest growth and harvest yield. However, achieving the additivity of SBA models for multiple tree species in the complex structure of broad-leaved mixed forests is an urgent scientific issue in the study of accurately predicting the SBA of mixed forests. This study used data from 58 sample plots (30 m × 30 m) for Populus davidiana × Betula platyphylla broad-leaved mixed forests to construct the SBA basic model based on nonlinear least squares regression (NLS). Adjustment in proportion (AP) and nonlinear seemingly unrelated regression (NSUR) were used to construct a multi-species additive basal area prediction model. The results identified the Richards model (M6) and Korf model (M1) as optimal for predicting the SBA of P. davidiana and B. platyphylla, respectively. The SBA models incorporate site quality, stand density index, and age at 1.3 m above ground level, which improves the prediction accuracy of basal area. Compared to AP, NSUR is an effective method for addressing the additivity of basal area in multi-species mixed forests. The results of this study can provide a scientific basis for optimizing stand structure and accurately predicting SBA in multi-species mixed forests.

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics.

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR's footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran's I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran's I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C = 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis.

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.

Explaining the emergence of land-use frontiers.

Land-use expansion is linked to major sustainability concerns including climate change, food security and biodiversity loss. This expansion is largely concentrated in so-called 'frontiers', defined here as places experiencing marked transformations owing to rapid resource exploitation. Understanding the mechanisms shaping these frontiers is crucial for sustainability. Previous work focused mainly on explaining how active frontiers advance, in particular, into tropical forests. Comparatively, our understanding of how frontiers emerge in territories considered marginal in terms of agricultural productivity and global market integration remains weak. We synthesize conceptual tools explaining resource and land-use frontiers, including theories of land rent and agglomeration economies, of frontiers as successive waves, spaces of territorialization, friction and opportunities, anticipation and expectation. We then propose a new theory of frontier emergence, which identifies exogenous pushes, legacies of past waves and actors' anticipations as key mechanisms by which frontiers emerge. Processes of differential rent creation and capture and the built-up of agglomeration economies then constitute key mechanisms sustaining active frontiers. Finally, we discuss five implications for the governance of frontiers for sustainability. Our theory focuses on agriculture and deforestation frontiers in the tropics but can be inspirational for other frontier processes including for extractive resources, such as minerals.

Lithology and elevated temperature impact phoD-harboring bacteria on soil available P enhancing in subtropical forests.

Plants are generally limited by soil phosphorus (P) deficiency in forest ecosystems. Soil available P is influenced by lithology, temperature, and soil microbes. However, the interactive effects of these factors on soil P availability in subtropical forests remain unclear. To assess their impacts, we measured soil inorganic and available P fractions and the diversity, composition, and co-occurrence network of phoD-harboring bacteria in two contrasting forest soils (lithosols in karst forests and ferralsols in non-karst forests) in the subtropical regions of southwestern China across six temperature gradients. The present results showed that the complexities in composition and network and the diversity indices of phoD-harboring bacteria were higher in the karst forest soils than those in the non-karst forest soils, with marked differences in composition. In both types of forest soils, the complexities of composition and networks and the diversity indices were higher in the high-temperature regions (mean annual temperature (MAT) > 16 °C) compared to the low-temperature regions (MAT <16 °C). Soil total inorganic and available P contents were lower in the karst forest soils compared to the non-karst forest soils. Soil total available P contents were lower in the high temperature regions than those in the low temperature regions in both forest soils, whereas soil total inorganic P contents were contrary. Variance partitioning analysis showed that soil inorganic and available P fractions were predominantly explained by lithology and its interaction with soil microbes and climate. The present findings demonstrate that soil P availability in subtropical forests of southwestern China is influenced by lithology and temperature, which regulate the diversity, composition, and network connectivity of phoD-harboring bacteria. Furthermore, this study highlights the significance of controlling the composition of phoD-harboring bacteria for mitigating plant P deficiency in karst ecosystems.

Future climate doubles the risk of hydraulic failure in a wet tropical forest.

Future climate presents conflicting implications for forest biomass. We evaluate how plant hydraulic traits, elevated CO2 levels, warming, and changes in precipitation affect forest primary productivity, evapotranspiration, and the risk of hydraulic failure. We used a dynamic vegetation model with plant hydrodynamics (FATES-HYDRO) to simulate the stand-level responses to future climate changes in a wet tropical forest in Barro Colorado Island, Panama. We calibrated the model by selecting plant trait assemblages that performed well against observations. These assemblages were run with temperature and precipitation changes for two greenhouse gas emission scenarios (2086-2100: SSP2-45, SSP5-85) and two CO2 levels (contemporary, anticipated). The risk of hydraulic failure is projected to increase from a contemporary rate of 5.7% to 10.1-11.3% under future climate scenarios, and, crucially, elevated CO2 provided only slight amelioration. By contrast, elevated CO2 mitigated GPP reductions. We attribute a greater variation in hydraulic failure risk to trait assemblages than to either CO2 or climate. Our results project forests with both faster growth (through productivity increases) and higher mortality rates (through increasing rates of hydraulic failure) in the neo-tropics accompanied by certain trait plant assemblages becoming nonviable.

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.

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.

The Complex Genetic Legacy of Hybridization and Introgression between the Rare Ocotea loxensis van der Werff and the Widespread O. infrafoveolata van der Werff (Lauraceae).

Hybridization and introgression are complex evolutionary mechanisms that can increase species diversity and lead to speciation, but may also lead to species extinction. In this study, we tested the presence and genetic consequences of hybridization between the rare and Ecuadorian endemic O. loxensis van der Werff and the widespread O. infrafoveolata van der Werff (Lauraceae). Phenotypically, some trees are difficult to identify, and we expect that some might in fact be cryptic hybrids. Thus, we developed nuclear microsatellites to assess the existence of hybrids, as well as the patterns of genetic diversity and population structure in allopatric and sympatric populations. The results revealed high levels of genetic diversity, even in the rare O. loxensis, being usually significantly higher in sympatric than in allopatric populations. The Bayesian assignment of individuals into different genetic classes revealed a complex scenario with different hybrid generations occurring in all sympatric populations, but also in allopatric ones. The absence of some backcrossed hybrids suggests the existence of asymmetric gene flow, and that some hybrids might be more fitted than others might. The existence of current and past interspecific gene flow also explains the blurring of species boundaries in these species and could be linked to the high rates of species found in Ocotea.

Neighborhood Diversity Promotes Tree Growth in a Secondary Forest: The Interplay of Intraspecific Competition, Interspecific Competition, and Spatial Scale.

Understanding the biodiversity-productivity relationship (BPR) is crucial for biodiversity conservation and ecosystem management. While it is known that diversity enhances forest productivity, the underlying mechanisms at the local neighborhood level remain poorly understood. We established a 9.6 ha dynamic forest plot to study how neighborhood diversity, intraspecific competition, and interspecific competition influence tree growth across spatial scales using linear mixed-effects models. Our analysis reveals a significant positive correlation between neighborhood species richness (NSR) and relative growth rate (RGR). Notably, intraspecific competition, measured by conspecific neighborhood density and resource competition, negatively impacts RGR at finer scales, indicating intense competition among conspecifics for limited resources. In contrast, interspecific competition, measured by heterospecific density and resource competition, has a negligible impact on RGR. The relative importance of diversity and intra/interspecific competition in influencing tree growth varies with scale. At fine scales, intraspecific competition dominates negatively, while at larger scales, the positive effect of NSR on RGR increases, contributing to a positive BPR. These findings highlight the intricate interplay between local interactions and spatial scale in modulating tree growth, emphasizing the importance of considering biotic interactions and spatial variability in studying BPR.

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.