Forest Distribution Research Articles

Elevational distribution of forests and its spatiotemporal dynamics in subtropical China from 2000 to 2019

Elevational distribution of forests and its spatiotemporal dynamics in subtropical China from 2000 to 2019

A Big History of Land Clearance and Deforestation

The gathering and hunting humans who evolved from earlier manifestations of Homo changed the distribution of forests on the planet through their use of fire to direct biological productivity to their sustenance, and through their contribution to the elimination of much of the global terrestrial megafauna. Land clearance at any scale awaited the development of agriculture, the several independent origins of which may indicate that it is an emergent outcome from the combination of a social animal who can transmit knowledge through generations and who lives in environments that support high numbers of food plants. The transition from uncleared forest and treeless land to land cleared for agriculture was slow, often reversed, and limited by the necessity to produce more energy in food production than in the inputs that created comestibles. Increases in cleared land until the nineteenth century were largely a product of the displacement of gathering and hunting people by disease-ridden European agriculturalists and world trade imposed on non-Europeans by colonialists. The explosion in fossil fuel usage from the nineteenth century onwards enabled exponential growth in human populations and cleared land, with the consequence of a crash in forest cover. Ironically, attempts to mitigate global warming caused by increased fossil fuel use, deforestation and land clearance have resulted in more land clearance for biofuels. While settlements, roads, logging, plantation establishment and dam construction have all contributed to the decrease in the native terrestrial cover of the planet, their contribution has been minor compared to the massive impact of agricultural development.

Study on Factors Influencing Forest Distribution in Barcelona Metropolitan Region

As a precious natural resource, forests are being destroyed. In previous studies, there is a lack of an interactive assessment of their distribution that comprehensively considers multiple external disturbances. This paper takes the Barcelona Metropolitan Region as an example. Based on remote sensing, it analyzes the development process of the forest from 2006 to 2018 through multiple landscape indicators, and OLS models were established to analyze variables that have direct and indirect effects on forest distribution. In addition, the ecological structure of the forest was analyzed based on NDVI. It was found that the forest area is the largest area but has been decreasing, becoming more complex in distribution structure. Much of the forest was converted to agricultural land and grassland. The green quality of the forests has been increasing, and the broad-leaved forest, the second largest area, contributes the most. NDVI is the most important positively correlated variable, and daytime surface temperature is an important inverse factor related to NDVI. In addition, NDBI is also a negative condition that inhibits forest development. In conclusion: The BMR forest area is decreasing and becoming more fragmented. NDVI and daytime LST are the two most significant factors. Climate warming may lead to worse forest development.

GEOGRAPHIC DIVERSITY AND LANDSCAPE IN TRANSITION: ANALYZING THE PHYSICAL FEATURES OF GILGIT BALTISTAN REGION

This study presents multidimensional analysis of the Gilgit Baltistan region, focusing upon its geographical, environmental, and socio-economic aspects. Employing both qualitative and quantitative methodologies, the research defines the region's physical features, climatic conditions, and hydrological characteristics. It quantitatively assesses distribution of glaciers, rivers, and forests, utilizing statistical data to realize trends in hydrology and ecology. Qualitatively, study explores hazards and vulnerabilities inherent in region, including natural, man-made hazards, their intensity, and socio-economic implications. Also, it conducts a comparative analysis to examine strategic significance of mountain passes in facilitating the trade, travel and cultural exchange. Study reveals region's significant hydrological resources, with glaciers and snowmelt serving as principal water sources. Strategic status of mountain passes in facilitating trade and connectivity, with key passes like Khunjerab and Babusar serving as vital links between Pakistan to China, as well as connecting the region with other provinces of Pakistan. The analysis further reveals that among natural hazards the region is vulnerable to flash floods, landslides, and snow avalanches, intensified by its rugged terrain and climatic conditions.

Environmental Response of Tree Species Distribution in Northeast China with the Joint Species Distribution Model

The composition, distribution, and growth of native natural forests are important references for the restoration, structural adjustment, and close-to-nature transformation of artificial forests. The joint species distribution model is a powerful tool for analyzing community structure and interspecific relationships. It has been widely used in biogeography, community ecology, and animal ecology, but it has not been extended to natural forest conservation and restoration in China. Therefore, based on the 9th National Forest Inventory data in Jilin Province, combined with environmental factors and functional traits of tree species, this study adopted the joint species distribution model—including a model with all variables (model FULL), a model with environmental factors (model ENV), and a model with spatial factors (model SPACE)—to examine the distribution of multiple tree species. The results show that, in models FULL and ENV, the environmental factors explaining the model variation were ranked as follows, climate > site > soil. The explanatory power was as follows: model FULL (AUC = 0.8325, Tjur R2 = 0.2326) > model ENV (AUC = 0.7664, Tjur R2 = 0.1454) > model SPACE (AUC = 0.7297, Tjur R2 = 0.1346). Tree species niches in model ENV were similar to those in model FULL. Compared to predictive power, we found that the information transmitted by environmental and spatial predictors overlaps, so the choice between model FULL and ENV should be based on the purpose of the model, rather than the difference in predictive ability. Both models can be used to study the adaptive distribution of multiple tree species in northeast China.

Comparison of machine learning and statistical approaches for Digital Elevation Model (DEM) correction: interim results

Abstract. The correction of digital elevation models (DEMs) can be achieved using a variety of techniques. Machine learning and statistical methods are broadly applicable to a variety of DEM correction case studies in different landscapes. However, a literature survey did not reveal any research that compared the effectiveness or performance of both methods. In this study, we comparatively evaluate three gradient boosted decision trees (XGBoost, LightGBM and CatBoost) and multiple linear regression for the correction of two publicly available global DEMs: Copernicus GLO-30 and ALOS World 3D (AW3D) in Cape Town, South Africa. The training datasets are comprised of eleven predictor variables including elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector ruggedness measure, percentage bare ground, urban footprints and percentage forest cover as an indicator of the overland forest distribution. The target variable (elevation error) was derived with respect to highly accurate airborne LiDAR. The results presented in this study represent urban/industrial and grassland/shrubland/dense bush landscapes. Although the accuracy of the original DEMs had been degraded by several anomalies, the corrections improved the vertical accuracy across vast areas of the landscape. In the urban/industrial and grassland/shrubland landscapes, the reduction in the root mean square error (RMSE) of the original AW3D DEM was greater than 70%, after correction. The corrections improved the accuracy of Copernicus DEM, e.g., > 44% RMSE reduction in the urban area and >32% RMSE reduction in the grassland/shrubland landscape. Generally, the gradient boosted decision trees outperformed multiple linear regression in most of the tests.

The area and distribution changes of mangrove forests on the coast of Gresik Regency, East Java, 2012-2023

Abstract Environmental dynamics in coastal areas have resulted in changes in the area and distribution of mangrove forests in Gresik Regency. Mangroves have ecological and economic functions for living things. This study aims to analyse changes in the area and distribution of mangrove forests in Gresik Regency in 2012 and 2023. The data source is IKONOS imagery from Google Earth in 2012 and 2023, the coastal location of Gresik Regency, East Java Province. Interpretation and digitisation were done to determine the area and distribution of mangrove forests, and maps of the mangrove forests were produced in 2012 and 2023. Then, the two maps were overlayed to determine their area and distribution changes. Based on image interpretation and analysis, in 2012, the area with the largest mangrove forest was Ujung Pangkah District (802.75 hectares) or 63.21% of the total mangrove forest area of Gresik Regency. The largest mangrove forest in 2023 is still in Ujung Pangkah District, with an area of 1261.50 hectares or 64.26%. For ten years (from 2012 to 2023), Gresik Regency’s mangrove forests increased by 590.41 hectares, the most significant increase in Ujung Pangkah District (increased by 458; 76 hectares).

Environmental correlates of the forest carbon distribution in the Central Himalayas.

Understanding the biophysical limitations on forest carbon across diverse ecological regions is crucial for accurately assessing and managing forest carbon stocks. This study investigates the role of climate and disturbance on the spatial variation of two key forest carbon pools: aboveground carbon (AGC) and soil organic carbon (SOC). Using plot-level carbon pool estimates from Nepal's national forest inventory and structural equation modelling, we explore the relationship of forest carbon stocks to broad-scale climatic water and energy availability and fine-scale terrain and disturbance. The forest AGC and SOC models explained 25% and 59% of the observed spatial variation in forest AGC and SOC, respectively. Among the evaluated variables, disturbance exhibited the strongest negative correlation with AGC, while the availability of climatic energy demonstrated the strongest negative correlation with SOC. Disturbances such as selective logging and firewood collection result in immediate forest carbon loss, while soil carbon changes take longer to respond. The lower decomposition rates in the high-elevation region, due to lower temperatures, preserve organic matter and contribute to the high SOC stocks observed there. These results highlight the critical role of climate and disturbance regimes in shaping landscape patterns of forest carbon stocks. Understanding the underlying drivers of these patterns is crucial for forest carbon management and conservation across diverse ecological zones including the Central Himalayas.

Linking Post-fire Tree Density to Carbon Storage in High-Latitude Cajander Larch (Larix cajanderi) Forests of Far Northeastern Siberia

AbstractWith climate warming and drying, fire activity is increasing in Cajander larch (Larix cajanderi Mayr.) forests underlain by continuous permafrost in northeastern Siberia, and initial post-fire tree demographic processes could unfold to determine long-term forest carbon (C) dynamics through impacts on tree density. Here, we evaluated above- and belowground C pools across 25 even-aged larch stands of varying tree densities that established following a wildfire in ~ 1940 near Cherskiy, Russia. Total C pools increased with increased larch tree density, from ~ 9,000 g C m−2 in low-density stands to ~ 11,000 g C m−2 in high and very high-density stands, with increases most pronounced at tree densities < 1 stem m−2 and driven by increased above- and belowground (that is, coarse roots) and live and dead (that is, woody debris and snags) larch biomass. Total understory vegetation and non-larch coarse root C pools declined with increased tree density due to decreased shrub C pools, but these pools were relatively small compared to larch biomass. Fine root, soil organic matter (OM), and near surface (0–30 cm) mineral soil (MS) C pools varied little with tree density, although soil C pools held most (18–28% in OM and 44–51% in MS) C stored in these stands. Thus, if changing fire regimes promote denser stands, C storage will likely increase, but whether this increase offsets C lost during fires remains unknown. Our findings highlight how post-fire tree demographic processes impact C pool distribution and stability in larch forests of Siberian permafrost regions.

Biogeography of larches in eastern Siberia – using single nucleotide polymorphisms derived by genotyping by sequencing

The present distribution of Siberian boreal forests that are dominated by larches (Larix spp.) is influenced, to an unknown extent, by glacial history. Knowing the past treeline dynamics can improve our understanding of future treeline shifts under changing climate. Here, we study patterns in the genetic variability of Siberian Larix to help unravel biogeographic migration routes since the Last Glacial Maximum (LGM).We infer the spatial distribution and the postglacial demographic history of Larix using genome‐wide single nucleotide polymorphisms (SNPs) derived through genotyping by sequencing (GBS) from 130 individuals sampled across eastern Siberia.Our analysis gives statistical support for two or three clusters, spanning from western to eastern Siberia. These clusters reveal a genetic structure influenced by isolation resulting from geographical distance, barriers imposed by geographic features, and distinct glacial histories. Assuming three clusters, our demographic inference indicates that the common ancestor of the current Larix populations existed in northeast Siberia well before the LGM. This suggests that Larix persisted in the northern region throughout previous glacials.Our genetic studies suggest that Larix likely survived the cold LGM in northern refugia, enabling a fast colonization of Siberia. Instead of complete repopulation from southern areas postglacially, the northernmost Larix expansion during the Holocene seems to have benefitted from refugial populations ahead of the treeline. Present‐day migration is expected to be slow initially, due to the absence of current refugial populations in the far north, in contrast to the early‐Holocene situation.

Soil properties constrain forest understory plant distributions along an elevation gradient.

Projections of spatial biodiversity dynamics under climate warming are often based on models including only climate variables, and when non-climatic factors (e.g. soil) are included, data are often at much coarser spatial resolutions than those experienced by plants. Field studies along elevation gradients permit the gathering of detailed soil data, while still covering a wide climatic gradient. Here, an intensive field survey of four spring forest herbs along an elevation gradient showed that soil properties had substantial impacts on the occurrence/abundance of all species, and that soil effects were more pronounced at higher elevations. For Trillium erectum and Claytonia caroliniana, very infrequent occurrences at high elevation were strongly associated with rare microsites with high pH or nutrients. In a seven-year transplant experiment with T. erectum, we found that individuals grew to much smaller sizes at high than low elevation, suggesting that environmental factors rather than dispersal limitation constrain the species' upper range limit, despite substantial warming in recent decades. Our study demonstrates that soil factors interact strongly with climate to determine plant range limits along climatic gradients. Unsuitable soils for plants at high elevations or latitudes may represent an important constraint on future plant migration and biodiversity change. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Leveraging climate and remote sensing metrics for predicting forest carbon stock using Bayesian geostatistical modelling under a projected climate warming in Zimbabwe

Climate change, driven by escalating carbon dioxide (CO2) emissions, poses a significant threat to forest ecosystems and the livelihoods of communities reliant on them, especially for the global south countries and regions like the eastern highlands of Zimbabwe. The 2000 land redistribution programme reduced buffer zones between ecologically sensitive forests and land reform beneficiaries near major carbon reservoirs. In light of these challenges, this study aimed to assess the potential effects of climate change on a strategically important plantation forest ecosystem in Zimbabwe's eastern highlands. Using data from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) of the Intergovernmental Panel on Climate Change (IPCC), we modelled and predicted changes in forest carbon (C) stock density under different climate scenarios: current (1970–2000), SSP5–4.5, and SSP5–8.5. Employing a hierarchical Bayesian geostatistical approach, we compared the baseline scenario (1970–2000) with projected scenarios (RCP4.5 and RCP8.5) for 2075 to estimate changes in forest carbon stock distribution. Our results indicated a decline in carbon stock concentration under future climate scenarios, reflecting the adverse impact of greenhouse gas emissions on forest growth. We found that the projected range of forest carbon stock under the RCP8.5 scenario for 2075 is notably lower (2≤MgCha−1≤44.9) than that of the baseline period (1970–2000) (1≤MgCha−1≤97), suggesting a substantial reduction in carbon storage. As the difference in posterior mean C stock (μ̅1−μ̅2), 52.1 MgCha-1 is well above zero, we deduce that the posterior mean C stock distribution of the projected future RCP8.5 2075 climate projection is indeed credibly different from the current (1970–2000) climate scenario. Additionally, there is a high probability (>90%) that forest plantations will be adversely affected by the business-as-usual climate warming projection. Overall, our findings highlight the urgent need for climate change mitigation strategies, such as reforestation programs and careful selection of tree species for plantations, to safeguard forest ecosystems and the communities dependent on them. These insights are crucial for informing effective adaptation measures in the face of future climate uncertainties.

Exploring the Differences in Tree Species Classification between Typical Forest Regions in Northern and Southern China

Focusing on the trend of continuously seeking high-precision tree species classification results in small areas from the perspectives of sensors and classification algorithms. This study aimed to explore the effects of data sources, classifiers, and seasons on classification accuracy in regions with significant environmental variation, examining patterns of tree species classification to enhance the transferability of classification. Considering two typical forest distribution regions in the north and south of China, this study utilized the revisitation cycle and open-source advantages of Sentinel-2 and Landsat-8. Leveraging the Google Earth Engine (GEE) platform, this study captured spectral features, vegetation indices, and texture features for single seasonal and seasonal combination images. With the assistance of Sentinel-1A and SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model), backscattering coefficient features and topographical features were extracted and input with features captured from Sentinel-2 and Landsat-8 into three types of classifiers: random forest (RF), support vector machine (SVM), and gradient tree boosting (GTB) for major tree species classification. In this research, we discovered that the best classification for single season in the northern study area was spring, whereas, for the southern study area, it was winter. Seasonal combination images effectively improved the classification accuracy of single seasonal images, with Sentinel-2 imagery displaying better classification performance compared to Landsat-8, and the optimal classifier differing between the north and the south. The inclusion of topographical or backscattering coefficient features in the four-season combination imagery contributed to improvements in classification accuracy, with topographical features significantly enhancing the classification performance in the topographically varied southern study area. The evaluation of feature importance indicated that elevation was the most critical feature for classification, while spectral features and vegetation indices were also significant. In the southern study area with large topographical discrepancies, subdividing into different terrain units led to improved tree species classification accuracy in medium-altitude, gentle slope areas. These findings provide insights into the regularity of enhancing tree species classification accuracy in environmentally diverse areas through the use of multi-source remote sensing data and multi-seasonal imagery. Consequently, the results offer a reference for the identification of tree species across large areas and the creation of spatial distribution maps.

Utilizing Multi-Source Data and Cloud Computing Platform to Map Short-Rotation Eucalyptus Plantations Distribution and Stand Age in Hainan Island

Short-rotation eucalyptus plantations play a key positive role in the forestry economy due to their fast-growing and high-yielding properties. However, some studies have suggested that eucalyptus plantations introductions may have negative impacts on biodiversity and ecosystems’ stability. In order to precisely and promptly determine the influence of eucalyptus plantations on soil characteristics and hydrological processes, based on the rotation change rules of eucalyptus plantations, this study combined the continuous change detection and classification and spectral mixture analysis (CCDC-SMA) algorithm and the random forest (RF) algorithm to map the distribution and stand age of short-rotation eucalyptus plantations in Hainan Island. First, the forest distribution map was used to mask out the rubber plantations, and forest disturbances were extracted through the CCDC-SMA algorithm to determine the potential short-rotation eucalyptus plantations distribution. Second, using CCDC-SMA algorithm fitting coefficients, field surveys, original spectral bands, vegetation indices, and digital elevation models (DEM) as inputs to the RF algorithm, short-rotation eucalyptus plantations distribution maps were created and evaluated based on Google Earth images. Finally, the stand age of the newly mapped short-rotation eucalyptus plantations was estimated based on the breakpoints of the CCDC-SMA algorithm. The results showed that the producer, user, and overall accuracies of the 2022 short-rotation eucalyptus plantations map were estimated at 0.95, 0.95, and 0.94, respectively, and the validation R2 of the estimated stand ages was at 0.97. The eucalyptus plantations in Hainan Island had a total area of roughly 9.93 × 104 ha in 2022. Danzhou City had the highest planting area of eucalyptus plantations, followed by Changjiang County, Chengmai County, and Lingao County. It was worth noting that the eucalyptus plantations were mostly located in places with low altitudes (<200 m) and flat slopes (<10°). Approximately 43.91% of eucalyptus plantations were located in the three major watersheds. In addition, the 1-year-old eucalyptus plantations accounted for the highest areal percentage of 30.58%. These datasets are valuable tools to aid sustainable production, ecological assessment, and conservation of eucalyptus plantations.

When species don't move together: Non-concurrent range shifts in Eastern Pacific kelp forest communities.

Species range shifts due to changing ocean conditions are occurring around the world. As species move, they build new interaction networks as they shift from or into new ecological communities. Typically, species ranges are modeled individually, but biotic interactions have been shown to be important to creating more realistic modeling outputs for species. To understand the importance of consumer interactions in Eastern Pacific kelp forest species distributions, we used a Maxent framework to model a key foundation species, giant kelp (Macrocystis pyrifera), and a dominant herbivore, purple sea urchins (Strongylocentrotus purpuratus). With neither species having previously been modeled in the Eastern Pacific, we found evidence for M. pyrifera expansion in the northern section of its range, with no projected contraction at the southern range edge. Despite its known co-occurrence with M. pyrifera, models of S. purpuratus showed a non-concurrent southern range contraction and a co-occurring northern range expansion. While the co-occurring shifts may lead to increased spatial competition for suitable substrate, this non-concurrent contraction could result in community wide impacts such as herbivore release, tropicalization, or ecosystem restructuring.

Precipitation masks the effect of temperature on Birch airborne pollen start, and previous summer temperature affects pollen intensity; A 31-year study at its southwestern distribution boundary

We studied the relationship between the occurrence of airborne pollen of Betula spp. and climate, during the main pollination season, as well as before the pollen season and in the preceding year. Our aim was to understand how environmental conditions could influence the main pollen season and the intensity of airborne pollen presence at the meridional boundary of the Betula forest distribution in Europe.For this purpose, pollen monitoring was conducted using Lanzoni VPPS 2000 volumetric trap and weather data from the nearest meteorological station. The study area was located in the northwest of the Iberian Peninsula, specifically in three areas with diverse mesoclimate: Vigo (Atlantic), Ourense (Mediterranean) and Lugo (Cantabrian), which had a distinct oceanic influence. Aerobiological analysis was conducted based on the Spanish aerobiology network protocol. Data on precipitation, maximum, minimum and mean temperature were selected for three time windows: 1st the main pollen season period, 2nd from 5 to 90 days before the onset of pollen presence, with a cumulative five-day interval, and 3rd the average monthly climate of the previous year.Precipitation delays the onset of the main pollination season as well as the end of the season, increasing pollen counts in the post-peak interval at all three sites. Temperature during the pollen season affects duration and pollen integral differently among sites. The pollen integral shows an increasing trend over the last decade's at all three areas. The rise trend detected in the minimum temperature of the previous summer explains these increases in the three sites.In conclusion, the effect of climate prior to and during the main pollen season have varied effects on date-related data across mesoclimates, while temperature during bud floral primordia development can be considered as a timeframe for predicting pollen intensity in the following year through the entire area.

The largest European forest carbon stocks are in the Dinaric Alps old-growth forests: comparison of direct measurements and standardised approaches

BackgroundCarbon (C) sink and stock are among the most important ecosystem services provided by forests in climate change mitigation policies. In this context, old-growth forests constitute an essential reference point for the development of close-to-nature silviculture, including C management techniques. Despite their small extent in Europe, temperate old-growth forests are assumed to be among the most prominent in terms of biomass and C stored. However, monitoring and reporting of C stocks is still poorly understood. To better understand the C stock amount and distribution in temperate old-growth forests, we estimated the C stock of two old-growth stands in the Dinaric Alps applying different assessment methods, including direct and indirect approaches (e.g., field measurements and allometric equations vs. IPCC standard methods). This paper presents the quantification and the distribution of C across the five main forest C pools (i.e., aboveground, belowground, deadwood, litter and soil) in the study areas and the differences between the applied methods.ResultsWe report a very prominent C stock in both study areas (507 Mg C ha− 1), concentrated in a few large trees (36% of C in 5% of trees). Moreover, we found significant differences in C stock estimation between direct and indirect methods. Indeed, the latter tended to underestimate or overestimate depending on the pool considered.ConclusionsComparison of our results with previous studies and data collected in European forests highlights the prominence of temperate forests, among which the Dinaric Alps old-growth forests are the largest. These findings provide an important benchmark for the development of future approaches to the management of the European temperate forests. However, further and deeper research on C stock and fluxes in old-growth stands is of prime importance to understand the potential and limits of the climate mitigation role of forests.

Effect of invasive species on forest composition in tropical dry forest: a case study of invasion by Gliricidia sepium

Ecological concerns caused by biological invasion have been considered an inevitable threat to global sustainability. The superior competitive ability of invasive plant species (IPS) has an impact on vegetation composition and loss of native flora. Recognising IPS at an early stage would significantly contribute to controlling invasive plants, protecting biodiversity and formulating appropriate strategies. Most of the studies available on invasive plants are descriptive and largely confined to non-woody species. The present study focused on the alien Gliricidia sepium, a tree species, to determine the invasive potential of its distribution in tropical dry deciduous forests of Central India. Forty square plots (25 × 25 m) were laid out at each uninvaded and invaded forest to enumerate phytosociological attributes to determine the impact of G. sepium on vegetation composition and community structure. Higher density (37.2 individuals ha−1), basal area (6.32 m2 ha−1) and importance value index (95.8) contributed by G. sepium clearly showed the suppression of seedlings, saplings and trees of native forest species at the invaded sites in terms of species richness, cover and other attributes as compared to at the uninvaded sites. Further, other invasive attributes such as higher seed production, dieback phenomenon, higher coppicing power and higher growth rate as observed during the study clearly affirmed the early speculation that the species might be invasive. The species has a strong potential to spread vigorously and may gradually become invasive throughout the country as already reported from various parts of India. In the present study area, G. sepium was introduced due to restoration and plantation programmes and our findings indicate its negative impact on the native vegetation. Thus, we strongly recommend the avoidance of this species for both plantation and restoration programmes, especially for Indian forests. The study helps in early recognition and rapid monitoring of G. sepium to control its spread throughout the country.

Lethal combination for seedlings: extreme heat drives mortality of drought-exposed high-elevation pine seedlings.

Hotter drought- and biotically-driven tree mortality are expected to increase with climate change in much of the western United States, and species persistence will depend upon ongoing establishment under novel conditions or migration to track ecological niche requirements. High-elevation tree species may be particularly vulnerable to increasing water stress as snowpack declines, increasing the potential for adult mortality and simultaneous regeneration failures. Seedling survival will be determined by ecophysiological limitations in response to changing water availability and temperature. We exposed seedlings from populations of Pinus longaeva, Pinus flexilis, and Pinus albicaulis to severe drought and concurrent temperature stress in common gardens testing timing of drought onset under two different temperature regimes. We monitored seedling functional traits, physiological function, and survival. The combined stressors of water limitation and extreme heat led to conservative water use strategies and declines in physiological function, with these joint stressors ultimately exceeding species' tolerances and leading to complete episodic mortality across all species. Growing conditions were the primary determinant of seedling trait expression, with seedlings exhibiting more drought-resistant traits such as lower specific leaf area in the hottest, driest treatment conditions. Water stress-induced stomatal closure was also widely apparent. Under adequate soil moisture, seedlings endured prolonged exposure to high air and surface temperatures, suggesting broad margins for survival. The critical interaction between soil moisture and temperature suggests that rising temperatures will exacerbate growing season moisture stress. Our results highlight the importance of local conditions over population- and species-level influences in shaping strategies for stress tolerance and resistance to desiccation at this early life stage. By quantifying some of the physiological consequences of drought and heat that lead to seedling mortality, we can better understand the future effects of global change on the composition and distribution of high-elevation conifer forests.

Bayesian approach for modeling diameter distributions of patula pine (Pinus patula Schiede ex Schltdl. & Cham.) stands in Southern Mexico

Characterization of the diameter distribution of forest stands managed for timber production provides accurate estimates of existing wood volume by diameter class and area unit. It provides the basis for determining the stand's ecological and economic value, its structure, the potential for ecosystem services, and appropriate management practices. Patula pine is one of the most important conifers in southern Mexico and requires biometric tools to improve management strategies. This study aimed to use the Bayesian approach to estimate the parameters of Johnson's SB (JSB) and Weibull (W) probability density functions (pdf) and to model the diameter distribution of patula pine stands in southern Mexico. We used data from 462 diameter distributions derived from 66 permanent 400 m2 sampling plots. The results indicated that the Bayesian approach was appropriate for estimating the JSB and W pdf parameters. Due to the similarity between the nonparametric tests and the goodness-of-fit statistics of the functions, the graphical analysis helped to select the W function as the better option to adequately model the diameter distribution of the analyzed stands. The equations developed for parameter prediction of the W function with stand variables allowed us to characterize and project the theoretical distribution of diameter classes in a simple accurate way with variables easily obtained from forest inventories. The Bayesian approach proved to be an effective method for estimating the pdf parameters used. This allowed for the correct characterization of the observed diameter distribution, and its implementation was easy for executing the procedure. Diameter distribution models are a powerful tool for forest inventory and management of patula pine stands analyzed in southern Mexico.