Spatial Distribution Of Trees Research Articles

Determinants of spatial distribution of trees outside forests along urban-rural gradients: A review

Urbanization can create uncertainty for biodiversity. Understanding the spatial distribution of trees along urban-rural gradients is crucial for sustainable land management and the conservation of biological diversity. However, limited information is available on the factors influencing the distribution of trees outside forests along urban-rural transition gradients. This paper uses the Preferred Reporting items for Systematic Reviews and Meta-Analyses (PRISMA) to review how distance from urban centers, land use types, socio-economic disparities, and community attitudes and perceptions impact the spatial distribution of trees outside forests along urban-rural gradients. The review indicates that the species composition, diversity, density, and spatial arrangement of trees outside forests vary along the urban-rural gradient. The most commonly cited factors influencing this distribution are respondents' attitudes and perceptions of trees, socio-economic factors, and land use variations. Distance from the urban center was the least cited factor. However, there is significant variation in how different factors impact this distribution from study to study. Therefore, further research is needed to better understand the factors driving changes in the diversity of trees outside forests in various urban-rural contexts and to determine whether variations exist across different settings.

Individual tree detection in large-scale urban environments using high-resolution multispectral imagery

Systematic maps of urban forests are useful for regional planners and ecologists to understand the spatial distribution of trees in cities. However, manually-created urban forest inventories are expensive and time-consuming to create and typically do not provide coverage of private land. Toward the goal of automating urban forest inventory through machine learning techniques, we performed a comparative study of methods for automatically detecting and localizing trees in multispectral aerial imagery of urban environments, and introduce a novel method based on convolutional neural network regression. Our evaluation is supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. Our method outperforms previous methods, achieving 73.6% precision and 73.3% recall when trained and tested in Southern California, and 76.5% precision 72.0% recall when trained and tested across the entire state. To demonstrate the scalability of the technique, we produced the first map of trees across the entire urban forest of California. The map we produced provides important data for the planning and management of California’s urban forest, and establishes a proven methodology for potentially producing similar maps nationally and globally in the future.

Tree parameter extraction in Fokienia hodginsii plantation based on airborne LiDAR data.

Accurate and efficient extraction of tree parameters from plantations lay foundation for estimating individual wood volume and stand stocking. In this study, we proposed a method of extracting high-precision tree parameters based on airborne LiDAR data. The main process included data pre-processing, ground filtering, individual tree segmentation, and parameter extraction. We collected high-density airborne point cloud data from the large-diameter timber of Fokienia hodginsii plantation in Guanzhuang State Forestry Farm, Shaxian County, Fujian Province, and pre-processed the point cloud data by denoising, resampling and normalization. The vegetation point clouds and ground point clouds were separated by the Cloth Simulation Filter (CSF). The former data were interpolated using the Delaunay triangulation mesh method to generate a digital surface model (DSM), while the latter data were interpolated using the Inverse Distance Weighted to generate a digital elevation model (DEM). After that, we obtained the canopy height model (CHM) through the difference operation between the two, and analyzed the CHM with varying resolutions by the watershed algorithm on the accuracy of individual tree segmentation and parameter extraction. We used the point cloud distance clustering algorithm to segment the normalized vegetation point cloud into individual trees, and analyzed the effects of different distance thresholds on the accuracy of indivi-dual tree segmentation and parameter extraction. The results showed that the watershed algorithm for extracting tree height of 0.3 m resolution CHM had highest comprehensive evaluation index of 91.1% for individual tree segmentation and superior accuracy with R2 of 0.967 and RMSE of 0.890 m. When the spacing threshold of the point cloud segmentation algorithm was the average crown diameter, the highest comprehensive evaluation index of 91.3% for individual tree segmentation, the extraction accuracy of the crown diameter was superior, with R2 of 0.937 and RMSE of 0.418 m. Tree height, crown diameter, tree density, and spatial distribution of trees were estimated. There were 5994 F. hodginsii, with an average tree height of 16.63 m and crown diameter of 3.98 m. Trees with height of 15-20 m were the most numerous (a total of 2661), followed by those between 10-15 m. This method of forest parameter extraction was useful for monitoring and managing plantations.

Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data.

Quantifying the relationship between light and stands or individual trees is of great significance in understanding tree competition, improving forest productivity, and comprehending ecological processes. However, accurately depicting the spatiotemporal variability of light under complex forest structural conditions poses a challenge, especially for precise forest management decisions that require a quantitative study of the relationship between fine-scale individual tree structure and light. 3D RTMs (3-dimensional radiative transfer models), which accurately characterize the interaction between solar radiation and detailed forest scenes, provide a reliable means for depicting such relationships. This study employs a 3D RTM and LiDAR (light detection and ranging) data to characterize the light environment of larch plantations at a fine spatiotemporal scale, further investigating the relationship between absorbed photosynthetically active radiation (APAR) and forest structures. The impact of specific tree structural parameters, such as crown diameter, crown area, crown length, crown ratio, crown volume, tree height, leaf area index, and a distance parameter assessing tree competition, on the daily-scale cumulative APAR per tree was investigated using a partial least squares regression (PLSR) model. Furthermore, variable importance in projection (VIP) was also calculated from the PLSR. The results indicate that among the individual tree structure parameters, crown volume is the most important one in explaining individual tree APAR (VIP = 4.19), while the competition from surrounding trees still plays a role in explaining individual tree APAR to some extent (VIP = 0.15), and crown ratio contributes the least (VIP = 0.03). Regarding the spatial distribution of trees, the average cumulative APAR per tree of larch plots does not increase with an increase in canopy gap fraction. Tree density and average cumulative APAR per tree were fitted using a natural exponential equation, with a coefficient of determination (R2 = 0.89), and a small mean absolute percentage error (MAPE = 0.03). This study demonstrates the potential of combining 3D RTM with LiDAR data to quantify fine-scale APAR in plantations, providing insights for optimizing forest structure, enhancing forest quality, and implementing precise forest management practices, such as selective breeding for superior tree species.

Tree growth and density enhanced, while diversity and spatial clustering reduced soil mycorrhizal C and N sequestration: Strong interaction with soil properties in northeastern China

In this paper, the effects of species diversity, tree growth, and spatial clustering on mycorrhizal carbon and nitrogen sequestration and the interaction of soil physicochemical properties in Northeast China were investigated. Based on 720 10 m ∗ 10 m plots in Harbin Experimental Forest Farm of Northeast Forestry University, we determined mycorrhizal biomarkers of easily extractable Glomalin-related soil protein (EEG) and total Glomalin-related soil protein (TG). Four plant diversity indices, seven structural metrics, and five soil properties were also measured. We found that: 1) The low tree diversity plots had 1.08–1.23 times higher TG, EEG, TG-N/TN (proportion of N in TG to TN), and TG-C/SOC (proportion of C in TG to SOC) than the high plots. 2) Tree diameter was negatively correlated with EEG and TG, but positively correlated with the EEG and TG contribution to soil TN and SOC. Soil EEG and TG were positively correlated with under-branch height and tree density. W (Uniform Angle Index, higher W indicates more clustering of tree distribution in the plot) was negatively correlated with the above four ratios and positively correlated with EEG/TG. 3) pH was the most powerful explainer for the GRSP variations (6.8 %, strongest negative association with GRSP/TN, R2 > 0.13), followed by soil electrical conductance (6.5 %, positive relation with TG, p < 0.05), AP (3.2 %). 4) Plant diversity mainly affected GRSP traits through the interaction with soils (0.07), tree growth and density directly increased TG, TG-N/TN, and TG-C/SOC, while tree spatial distribution directly reduced TG-N/TN. Our finding highlighted the important effects of tree diversity and forest structural traits on GRSP amount, carbon sequestration, and nutrient retentions, and could support glomalin-related forest soil management of temperate forests in the high-latitude northern hemisphere.

Inventory and Spatial Distribution of Trees in Eleme Local Government Area, Port Harcourt, Nigeria

Urban trees provide a wide range of environmental, economic and social benefits to those that live and work within and outside the community. Urban forest assessments are essential in supporting urban forest management and planning to improve environmental quality and human health in cities. The study investigated woody trees distributions in Eleme Local Government Area (Apkajo) Port Harcourt Nigeria, The study area was divided into 3 categories (Academic, Residential and Religious). Total woody tree species enumerations were carried out using tree identification and measurement while remote sensing technique was used to determine spatial distribution of trees and their land surface temperature. Morphological parameters assessed included total height (THT), crown diameter (CD), crown projection area (CPA), slenderness coefficient (SC) Merchantable area (MCH). Findings indicated total number of twenty seven (27) different woody tree species was identified from a sample population of one hundred and eighty five (185) trees. Species identified includes Mangifera indica, Dacryodes edulis,Terminalia mantaly, Azadirchta indica, Newbouldia laevis, Persea americana, Polyalthia longifolia, Terminalia catappa..Beneficial roles included medicinal, food security, income generation, beautification and micro-climate purposes. Among the species, Mangifera indica was the most abundant with a frequency of occurrence of 47. The result revealed that trees found in academic areas consistently gave higher mean tree growth variable values. Biodiversity was highest in residential areas with a Shannon index of 2.54 and Simpsons index of 0.88. Spatial distribution pattern showed that most tree species are clustered around the residential areas than the religious and academic areas. It was also observed that the trees are active in moderation of the land surface temperature by creating a micro-climate beneath it. The high species richness and diversity within the residential areas, despite its high infrastructural development, showed the importance people placed on urban greening and climate change.

Habitat associations of woody plant species in evergreen–deciduous broadleaf karst forests in southwest China

The effects of habitat filtering on community assembly have been extensively researched, and topography has been identified as a critical factor influencing the spatial distribution of trees. In this study, a 25-ha plot was established in karst evergreen–deciduous broadleaf forests in southwestern China. Eight topographical factors were used to divide plots into four habitat types, i.e., hilltop, steep slope, gentle slope, and depression, using a multivariate regression tree. A total of 85 evergreen and deciduous tree species were recorded in these four habitats and classified into three life stages, the differentiation of which was assessed using torus-translation tests. A total of 65 species significantly positively associated with at least one habitat and 79 species significantly negatively associated with at least one habitat were identified. Most species, whether evergreen or deciduous, exhibited a positive correlation with steep slopes, whereas relatively few species were adapted to depressions. Moreover, the percentage of evergreen species positively associated with hilltops and steep slopes was higher than that of deciduous species. Both evergreen and deciduous species showed an increasing percentage of positive correlation with hilltops from the sapling stage to the mature stage. However, more evergreen species grew on steep slopes in the sapling stage, whereas deciduous species grew in the mature stage. Canonical correspondence was used to analyze the relationship between species and the eight topographical factors. Regardless of life form or life stage, results showed that species distribution was significantly affected by topography. Furthermore, the distribution of evergreen species on sapling-stage trees was found to be more influenced by topography, whereas deciduous species were more influenced by topography in the mature stage. Finally, elevation was identified as the most crucial topographical factor affecting species distribution.

Prediction of Urban Trees Planting Base on Guided Cellular Automata to Enhance the Connection of Green Infrastructure

Urbanization and climate change pose significant challenges to urban ecosystems, underscoring the necessity for innovative strategies to enhance urban green infrastructure. Tree planting, a crucial aspect of green infrastructure, has been analyzed for optimized positioning using data metrics, priority scoring, and GIS. However, due to the dynamic nature of environmental information, the accuracy of current approaches is compromised. This study aims to present a novel approach integrating deep learning and cellular automata to prioritize urban tree planting locations to anticipate the optimal urban tree network. Initially, GIS data were collated and visualized to identify a suitable study site within London. CycleGAN models were trained using cellular automata outputs and forest mycorrhizal network samples. The comparison validated cellular automata’s applicability, enabled observing spatial feature information in the outputs and guiding the parameter design of our 3D cellular automata system for predicting tree planting locations. The locations were optimized by simulating the network connectivity of urban trees after planting, following the spatial-behavioral pattern of the forest mycorrhizal network. The results highlight the role of robust tree networks in fostering ecological stability and cushioning climate change impacts in urban contexts. The proposed approach addresses existing methodological and practical limitations, providing innovative strategies for optimal tree planting and prioritization of urban green infrastructure, thereby informing sustainable urban planning and design. Our findings illustrate the symbiotic relationship between urban trees and future cities and offer insights into street tree density planning, optimizing the spatial distribution of trees within urban landscapes for sustainable urban development.

Spatial distribution of tree and grassland fine roots in an alley cropping system

Alley cropping systems are known as more sustainable land use alternatives compared to monoculture cropland. In addition to the improvement of above-ground structures and creation of biotopes relevant to nature conservation, the improvement of resource development through various root systems plays a major role. We studied the interaction of the root systems in an alley cropping system combining permanent grassland and willows and at a reference grassland site. The system was established 12 years prior to our study on a site with a shallow groundwater table at 130 cm depth. We measured carbon stocks in the topsoil and determined the share of root-bound carbon relative to the total carbon pool and extracted soil cores up to a depth of 150 cm along a distance gradient to the trees and at a reference grassland site with no tree influence. The maximal rooting depth of the grassland roots increased with increasing distance to the tree lines and total root biomass was higher than under the grassland reference up to a distance of 2.5 m from the tree line. Tree roots extended up to a distance of 5.5 m from the trees and we could distinguish zones of tree root dominance very close to the trees, zones of grassland root dominance at distances ≥ 8.5 m and an interaction zone in between those two extremes. We conclude that alley cropping increases belowground biomass as compared to grassland and has therefore a higher potential to store carbon in the subsoil.

Distribution of trees in medieval areas of East-Central European cities ‒ regularities and peculiarities

Trees in the densely built-up historical cores of cities increase the aesthetic values of the cityscape as well as lower the effect of the urban heat island. The research aimed to determine the spatial distribution of tree populations in the medieval parts of cities. The investigation included three cities in Central and Eastern Europe: Poznań and Lublin in Poland and Lviv in Ukraine. The oldest parts of these cities share similar origins, the same continental biogeographical region, and comparable climatic conditions. This study considered indicators of occurrence, density, species composition, and spatial distribution of trees. The study identified different types of management concerning trees. In Poznań, trees are actively introduced in public spaces. In Lublin, there is the largest share of trees in the backyards and self-seeding is common. In Lviv, there are new plantings in private and public spaces. Lviv is distinguished by its large share of native trees, and in Poznan and Lublin, new plantings include artificially shaped, small spherical crowns.

Interactions between mammals and fruit of an endemic tree (Amphitecna montana, Bignoniaceae) with megafauna traits

ABSTRACT Several features in the fruit of some neotropical trees likely evolved in response to the interaction with the mammalian megafauna extinct in the late Pleistocene. However, few studies have assessed the interactions between these plants and mammals in the absence of megafauna. We conducted a camera-trapping study in the Reserve of El Triunfo, Chiapas, Southern Mexico, to record the fauna consuming the fruit of the endemic tree Amphitecna montana which has megafauna traits. We surveyed the abundance and the spatial distribution of trees, saplings, and seedlings in 10 transects totalizing 0.1 ha, analyzed the chemical composition of their fruit, and assessed the impact of seed manipulation on germination success and seedling establishment. We recorded 12 species of medium-large mammals but the most frequent consumers of fruit were squirrels. The relationship among trees, saplings, and seedlings did not indicate the existence of a regeneration limitation. All the plant stages showed a clustered spatial pattern, which is in agreement with squirrels being the main seed dispersers. Free seeds were the more successful to germinate and establish as seedlings. Despite the current lack of megafauna in our study area, the A. montana population does not show signs of declining.

Effects of functional traits on the spatial distribution and hyperdominance of tree species in the Cerrado biome

The ecological influence of functional traits on species persistence as well as on their role over the organization of forest communities in the Brazilian Cerrado biome have not been fully understood yet. In this study, we assessed the effects of six functional groups, characterized by three seed dispersal syndromes (i.e., anemochory, autochory, and zoochory) and three wood density classes (i.e., hardwood, lightwood, and softwood), on tree spatial distribution patterns, habitat occupancy, and ecosystem services (biomass hyper dominance and abundance) provided by a forest community located in the “Parque do Lajeado”, state of Tocantins, Brazil. The similarity among study sites was characterized by applying the tree dominant height approach and the environmental and soil variables as input. The floristic similarity was assessed by applying the Bray-Curtis index. The zoochoric species showed more aggregated spatial pattern at local scale, which indicates that it is more sensitive to environmental gradients than other dispersal syndromes. Meanwhile, hardwood density species were more established in the community, being more persistent to environmental filters. We observed that a small number of species contributed with about 50% of the abundance and biomass of the community, whose functional traits (wood density and dispersal syndrome) indirectly affect the relationship among the community species richness and their ecosystem functions. We observed that the functional traits related to seed dispersal and wood density functional groups resulted in different spatial distribution patterns of those tree species. Therefore, functional traits and environmental factors combined have substantially affected the structure and composition of forest communities at local scale.

Intermediate-severity disturbance impacts in a mixedwood forest: A multi-scale analysis

Mixed Quercus-Pinus stands are increasingly desired by forest managers to achieve a range of objectives, including biodiversity enhancement and resilience to global change. To create and perpetuate desired mixed Quercus-Pinus stand composition and structure, quantitative information on natural disturbance impacts on stand development and succession is necessary. Wind is the most common natural canopy disturbance in eastern North America, and impacts vary in severity and spatiotemporal scales. Intermediate-severity disturbance (ISD; events that occur along a classification gradient between frequent gap-scale and infrequent, stand-replacing events) is hypothesized to be an important driver of mixed Quercus-Pinus creation and maintenance. Our overarching goal was to quantify patterns of intermediate-severity wind disturbance in a mixed Quercus-Pinus echinata Mill. forest on the Cumberland Plateau in Tennessee, USA. Specifically, our objectives were to: 1) quantify post-disturbance species composition, stand structure, and residual tree spatial patterns, 2) infer individual-tree, neighborhood, and site-specific impacts on tree mortality, and 3) describe frequency, size, shape, and spatial distribution of ISD-created canopy openings. We inventoried plots in Pinus echinata-dominated, disturbance-impacted portions of the forest. To infer individual-tree and spatially-explicit neighborhood characteristics that influenced survival probability, we applied a random forest classification algorithm. To document changes in tree spatial distribution before and after disturbance, we used spatial point pattern analysis. To characterize forest-scale disturbance patterns, we classified canopy gaps from high-resolution orthoimagery. The most important predictors of survival were basal area, taxonomic group, and distance to nearest neighbor. Pre-disturbance trees and residuals were spatially randomly distributed. Most detected openings were 50–150 m2, and the opening size-frequency distribution exhibited a reverse J-shape. Openings were spatially clustered within the forest at distances < 200 m and complex in shape. We provide quantitative recommendations on the frequency, size, shape, and spatial distribution of silvicultural entries patterned after natural disturbance. To emulate natural disturbance patterns, we recommend patch seedtree harvests with reserves or patch clearcuts with reserves, and these harvest-created openings should be concentrated (i.e., clustered) in portions of the stand in which P. echinata is most dominant and/or most competitive. Site preparation with prescribed fire, herbicide, and/or mechanical thinning may also be necessary to prepare the seedbed and reduce hardwood competition to favor Pinus establishment in openings.

Estimating potential tree height in Pinus radiata plantations using airborne laser scanning data

Representing the spatial distribution of trees and competition interactions in growth models improves growth prediction and provides insights into spatially explicit forecasts for precise silvicultural interventions. However, this information is rarely taken into account over large areas because obtaining the spatial distribution of individual trees and estimating their competition is both expensive and time consuming. Airborne laser scanning enables rapid estimation of tree height and other attributes over large areas. In this study, we implemented an individual tree detection approach to first extract tree attributes of Pinus radiata D. Don plantations, and second to use this spatially explicit information on tree location and competition to forecast potential tree height, defined as a maximum projected tree height at rotation age. To do so, using a chronosequence of tree heights, we developed a tree height growth model using a Chapman–Richards function, utilizing the effect of inter-tree competition and stand-level top height (TH) on the tree height growth. The results showed that using chronosequence of heights, competition, and TH resulted in accurate predictions of potential tree height (root mean square error = 2.9 m; mean absolute percentage error = 0.154%). We concluded that individual tree height growth is significantly influenced by competition, with increased competition values associated with reductions in potential height growth by 22.2% at 30 years.

Using Airborne LiDAR to Monitor Spatial Patterns in South Central Oregon Dry Mixed-Conifer Forest

Abstract A common forest restoration goal is to achieve a spatial distribution of trees consistent with historical forest structure, which can be characterized by the distribution of individuals, clumps, and openings (ICO). With the stated goal of restoring historical spatial patterns comes a need for effectiveness monitoring at appropriate spatial scales. Airborne light detection and ranging (LiDAR) can be used to identify individual tree locations and collect data at landscape scales, offering a method of analyzing tree spatial distributions over the scales at which forest restoration is conducted. In this study, we investigated whether tree locations identified by airborne LiDAR data can be used with existing spatial analysis methods to quantify ICO distributions for use in restoration effectiveness monitoring. Results showed fewer large clumps and large openings, and more small clumps and small openings relative to historical spatial patterns, suggesting that the methods investigated in this study can be used to monitor whether restoration efforts are successful at achieving desired tree spatial patterns. Study Implications: Achieving a desired spatial pattern is often a goal of forest restoration. Monitoring for spatial pattern, however, can be complex and time-consuming in the field. LiDAR technology offers the ability to analyze spatial pattern at landscape scales. Preexisting methods for evaluation of the distribution of individuals, clumps, and openings were used in this study along with LiDAR individual tree detection methodology to assess whether a forest restoration project implemented in a Southern Oregon landscape achieved desired spatial patterns.

Changes in tree layer and altitudinal distribution of herbaceous species in temperate old-growth forests over 30 years

Temperate forest ecosystems, including old-growth fragments, are subject to increasing pressures, both from biotic and abiotic factors. Frequent disturbance events, rising mean annual temperatures and longer-lasting droughts are causing changes in tree species composition, probably shifting the altitudinal distribution of herbaceous species as well. Our goal was to examine whether such shifts can be observed even in old-growth temperate forests, and if the changes in the species composition and spatial distribution of trees is reflected in the herbaceous layer. Our study was based on a survey of several old-growth forests from the 1970s that was repeated after 30 years. Using spatial point pattern methods and generalized linear mixed effect models, repeated measurements of mapped phytosociological relevés and detailed maps of tree positions from two survey periods allowed us to examine how the species composition of the herb layer and the spatial distribution of trees ≥ 10 cm DBH (diameter at breast height) changed over 30 years. On most of the studied sites, the total number of trees declined and the proportion of broadleaves increased between the two surveys. Analyses of tree spatial distribution showed a general shift from a regular spatial distribution in the 1970s to a clustered spatial distribution of trees in the 2000s. In the 2000s, herbaceous species showed an upwards shift in their distribution compared to the 1970s, even after accounting for the effect of changing tree spatial distributions in both survey periods. These effects could be an outcome of warmer and drier weather conditions during the past decades. Further investigation is needed to examine whether this trend is related to changes in climatic conditions.

Density effect on decline of Cedrus atlantica Manetti Ex Carriere in national park of Theniet El Had, Algeria

This study focuses on the study of the causal factors of Atlas cedar decline in the Theniet el Had national park, once classified as the first park in Algeria and the most beautiful cedar forest in northern Africa. Although severalstudies have been done on the causative agent of decline, none of them have confirmed the predisposing factors, thus weakening its vigor. Therefore, in this study, the search for possible relationship between dieback and ecological, dendrometric characteristics as well as the spatial distribution of trees were taken into consideration. 40 concentric circular plots of 200, 400, 500 and 1,000 m² were installed in the northern part of the park for dendrometric and edaphic surveys, in addition to rectangular plots of 200 m2 installed in the center of the concentric plots were measured of distance between trees and of the four radii of the crown. The results obtained showed that the circumference in the reduced areas (the circular plots of 200m²) declined significantly. The spatial distribution of the trees, schematized by the AutoCAD software, was chaotic and the crowns showed deterioration influenced by their overlapping. In 76% of the rectangular plots, the maximum area of the crowns had an overlap of more than 90%. The application of Schütz's inter-individual competition index also indicated that competition between trees was very important. This situation only explains the chaotic distribution of the trees, due to the lack of forestry work, imposes a competition between the trees causing, thus, the weakening of their vigor. Therefore, the establishment of a short- and longterm Atlas cedar decline management plan is of high recommended importance, in which adjusting the spatial distribution can promote the future strength and vitality of Atlas cedars.

Analyzing the Spatial Correspondence between Different Date Fruit Cultivars and Farms’ Cultivated Areas, Case Study: Al-Ahsa Oasis, Kingdom of Saudi Arabia

Diversity in date palm (DP) cultivars plays a crucial role in the agroecosystems of several countries, such as the Kingdom of Saudi Arabia (KSA). This study aims to map and analyze the spatial distribution of the most grown DP cultivars (Khlas, Ruziz, and Shishi) in the Al-Ahsa oasis in the KSA and to highlight their spatial correlation with the corresponding cultivated patches within farms. Descriptive and spatial data on 288 farms were analyzed using GIS, data curation, cross-TAB statistics, clustering maps, and spatial autocorrelation techniques. The obtained results revealed that most of the oasis’s DP farms are within a cultivated area of &lt;500 m2. The larger cultivated areas are mostly in the oasis’s northern and central subregions, agreeing with the spatial distribution of trees. In total, 56.9% of the studied farms grew the cultivars together within the least rank (&lt;500 m2) of cultivated area, having the greatest tendency for DP cultivation. Khlas was the most dominant cultivar being the least absent from cultivation with 3.1% compared to Ruziz (31.9%) and Shishi (37.8%). The spatial distribution of DP plantations in the oasis was also consistent with the spatial variation in soils and irrigation water salinity, necessitating the need for special agricultural extension programs. In conclusion, these outcomes indicate that this study is essential for DP sustainability, growers, authorities, and policy makers.

Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape.

Predicting forest recovery at landscape scales will aid forest restoration efforts. The first step in successful forest recovery is tree recruitment. Forecasts of tree recruit abundance, derived from the landscape-scale distribution of seed sources (i.e., adult trees), could assist efforts to identify sites with high potential for natural regeneration. However, previous work revealed wide variation in the effect of seed sources on seedling abundance, from positive to no effect. We quantified the relationship between adult tree seed sources and tree recruits and predicted where natural recruitment would occur in a fragmented, tropical, agricultural landscape. We integrated species-specific tree crown maps generated from hyperspectral imagery and property ownership data with field data on the spatial distribution of tree recruits from five species. We then developed hierarchical Bayesian models to predict landscape-scale recruit abundance. Our models revealed that species-specific maps of tree crowns improved recruit abundance predictions. Conspecific crown area had a much stronger impact on recruitment abundance (8.00% increase in recruit abundance when conspecific tree density increases from zero to one tree; 95% credible interval (CI): 0.80% to 11.57%) than heterospecific crown area (0.03% increase with the addition of a single heterospecific tree, 95% CI: -0.60% to 0.68%). Individual property ownership was also an important predictor of recruit abundance: The best performing model had varying effects of conspecific and heterospecific crown area on recruit abundance, depending on individual property ownership. We demonstrate how novel remote sensing approaches and cadastral data can be used to generate high-resolution and landscape-level maps of tree recruit abundance. Spatial models parameterized with field, cadastral, and remote sensing data are poised to assist decision support for forest landscape restoration.

Spatial patterns of a savanna palm treeBorassus aethiopumand its temporal variability

AbstractThe spatial distributions and associations of plant species in a stand can provide essential information about their dynamics. However, since tree spatial distribution and association depend on factors that operate at different scales, disentangling the effects of environmental heterogeneity and plant–plant interactions requires the choice of a suitable null model for spatial analysis. We analyzed the spatial distributions of the same savanna palm tree populations that were studied, the underlying hypothesis being that these distributions have changed in 20 years. To remove the effect of large-scale environmental heterogeneity, the inhomogeneous L-function under the heterogeneous Poisson null model was used. We showed: (i) unlike 20 years ago, adults had a regular distribution instead of an aggregated distribution in the grass savanna; (ii) although the spatial distribution of seedlings and juveniles was always aggregated, we observed a decrease in the size of the aggregates (intensities); (iii) except for juveniles, no other stages were associated with nutrient-rich patches, which was also different 20 years ago; (iv) we did not observe any particular difference in structure between two study sites, only that female palm trees were spatially associated with entire-leafed seedlings in the reserve while they were spatially independent in the rural area. Our study supports the hypothesis that the spatial distributions have partially changed, and that the management of spatial heterogeneity has improved and given more precision in the results.