Topographic Variables Research Articles

Effects of land use types and environmental factors on woody species diversity in agricultural landscapes of Jimma Ganati District, Western Oromia, Ethiopia

Nowadays agricultural expansion and the concurrent climate variability jointly jeopardized global biodiversity in general and plant species diversity in particular. Assessing current diversity status of different land uses is a proxy for anticipating their individualistic resilience against future climatic variability. Here we explored woody species composition, diversity, distribution, and indicator species across five agricultural land uses. For this we employed a systematic sampling design to collect phytosociological attributes of woody plant species from 103 plots in four landscapes at a regular distance following transect lines. For comparative diversity analysis, we used linear mixed effects model using the lmer function and subsequently, post hoc pairwise comparisons with the emmeans function of R software. To visualize species composition dissimilarities between the land uses, we applied NMDS ordination based on Bray-Curtis dissimilarity index and subsequently, adonis2 () function (R software) was used for testing its significance and pair-wise PERMANOVA analysis. Linear regression model and multiple regression model by Envfit function was used to test the effect of environmental topographic variables on the species distribution. The result indicated that, 104 woody plant species belonging to 52 families were collected, in which significant differences in species richness and composition among the five land use types were showed. The result reveal that, forest is with the most diversified species assemblage followed by home gardens. However, grazing land is the least in species diversity. Among topographic variables, elevation gradient shows significant influence on diversity and species compositional dissimilarity among the five agricultural land uses in which 38 species were significantly contributed to the dissimilarity of species composition. Species richness and woody stem abundance declines with increasing elevation. However, slope and aspect does not show significant effect on these attributes analysis. Indicator species assessment (IndVal.) showed that, 20 species were significantly (P ≤ 0.001) associated with at least one of the five land use classes in agricultural landscape. In sum, this study highlights that, changes in woody species diversity, composition and spatial patterns are the responses to types of land uses of varying disturbance intensities and topographic elevation as a proxy to different environmental factors.

Spatial Confounding and Spatial+ for Nonlinear Covariate Effects

Regression models for spatially varying data use spatial random effects to reflect spatial correlation structure. Such random effects, however, may interfere with the covariate effect estimates and make them unreliable. This problem, known as spatial confounding, is complex and has only been studied for models with linear covariate effects. However, as illustrated by a forestry example in which we assess the effect of soil, climate, and topography variables on tree health, the covariate effects of interest are in practice often unknown and nonlinear. We consider, for the first time, spatial confounding in spatial models with nonlinear effects implemented in the generalised additive models (GAMs) framework. We show that spatial+, a recently developed method for alleviating confounding in the linear case, can be adapted to this setting. In practice, spatial+ can then be used both as a diagnostic tool for investigating whether covariate effect estimates are affected by spatial confounding and for correcting the estimates for the resulting bias when it is present. Supplementary materials accompanying this paper appear online.

Distribution and co-occurrence patterns of charophytes and angiosperms in the northern Baltic Sea

The distribution data of 11 soft substrate charophyte and angiosperm species were analyzed. Our study aimed to elucidate the co-occurrence patterns among these sympatric macrophyte species and quantify their distribution areas. The central hypothesis of this study proposed that the observed co-occurrence patterns among the studied species deviate from what would be expected by random chance. Macrophyte occurrence data was derived from an extensive field sampling database. Environmental variables available as georeferenced raster layers including topographical, hydrodynamic, geological, physical, chemical, and biological variables were used as predictor variables in the random forest models to predict the spatial distribution of the species. Permutation tests revealed statistically significant deviations from random co-occurrence patterns. The analysis demonstrated that species tended to co-occur more frequently within their taxonomic groups (i.e., within charophytes and within angiosperms) than between these groups. The most extensive distribution overlap was observed between Chara aspera Willd. and Chara canescens Loisel., while Zostera marina L. exhibited the least overlap with the other species. The mean number of co-occurring species was the highest in Chara baltica (Hartman) Bruzelius while Z. marina had the largest share of single-species occurrences. Based on the distribution models, Stuckenia pectinata (L.) Börner had the largest distribution area.

Ecological niche modelling in identifying habitats for effective species conservation: A study on Endemic aquatic plant Crinum malabaricum

Ecological niche modelling (ENM) aids in identifying new species populations and suitable habitats for species translocation, addressing the conservation challenge of endemic species with limited distribution. The current study aims to develop a habitat suitability map of Crinum malabaricum Lekhak & S. R. Yadav, an endemic species with a very limited geographical range, using Maxent-based ecological niche modelling to identify suitable receptor sites for conservation translocation. Out of 23 species occurrence records, 11 were used for this study. Seven predictive variables are selected based on Pearson’s correlation coefficient (r = ±0.8) and permutation importance (>3%) from a group of nineteen bioclimatic and two topographic variables. The study area encompasses the Malabar Plains of the Western Ghats. The accuracy of the model was assessed using the ROC curve method, and the contribution of variables was estimated using a Jackknife analysis. The results show that precipitation of the driest quarter and mean temperature of the driest quarter were the leading predictors of habitat suitability. The model shows high accuracy with a value of 1.6 for AUC ratios and values > 8 for AUC, partial AUC, Kappa, and TSS metrics. The model predicted a fundamental niche for this species with 2393 KM2 (3.6% of the study area) as a highly suitable area for C. malabaricum (where habitat suitability index ≥ 0.85), which is much higher than the calculated extend of occurrence (EOO = 283 KM2) of this species. The realized niche of this species was identified using field explorations, and ten suitable areas were selected for species translocation. These selected habitats are distributed across various regions of the southern Western Ghats. As a species with limited distribution and a lack of proper dispersal mechanisms, a successful species translocation to these sites will overcome the endemism in this species. For selecting appropriate sites for conservation translocations, this methodology will be reliable and effective, and it could be useful for other endemic species.

Ecological niche modelling of two water-dependant birds informs the conservation needs of riverine ecosystems outside protected area network in the Eastern Himalaya, India.

Common species often play vital roles in ecosystem functions and processes. Globally, conservation strategies are mostly focused on threatened species and rarely explored the potential of using common species as indicators of critical ecosystems. The Himalayan mountains have unique riverine ecosystems harbouring high diversity of specialist river birds. Ecological niche modelling provides effective tools to predict suitable habitats of a species and identify habitats for conservation. We used two common water-dependent bird species, Blue Whistling Thrush and White-capped Water Redstart as indicators of riverine ecosystems within the Sikkim Himalayan region and predicted their suitable habitats using an ensemble modelling approach. We selected six predictor variables for the final model including three bioclimatic and three topographic variables. For both species, bioclimatic variables such as mean annual temperature and precipitation were the most important factors compared to topographic variables. At least 70 percent of the most suitable habitats are distributed below 2000 m elevation alongside major drainages. Also, most of their potential habitats are distributed outside the protected area networks in the region. This habitat suitability pattern may be applied to other sympatric species in the region. Since major water bodies in Sikkim are largely affected by developmental activities and climate change, these riverine birds might face threats of losing suitable habitats. We recommend a dynamic approach to evaluate the habitat quality of riverine birds, especially outside protected area networks in the region to plan conservation strategies. This approach will ensure habitat conservation of many water-dependent birds and other taxa associated with the riverine ecosystems of the Eastern Himalaya.

Short-term recovery of post-fire vegetation is primarily limited by drought in Mediterranean forest ecosystems

BackgroundClimate change is altering the fire regime and compromising the post-fire recovery of vegetation worldwide. To understand the factors influencing post-fire vegetation cover restoration, we calculated the recovery of vegetation in 200,000 hectares of western Mediterranean forest burned by 268 wildfires over a 27-year period (1988–2015). We used time series of the Tasseled Cap Transformation Brightness (TCTB) spectral transformation over Landsat imagery to calculate vegetation recovery. Then, we quantified the importance of the main drivers of post-fire vegetation recovery (climate, fire severity, and topography) along an aridity gradient (semi-arid, sub-humid, and humid) using Random Forest models.ResultsIn most models (99.7%), drought duration was the most important factor, negatively affecting post-fire recovery especially in the extremes of the aridity gradient. Fire severity was the second most important factor for vegetation cover recovery, with its effect varying along the aridity gradient: there was a positive relationship between fire severity and recovery in sub-humid and humid areas, while semi-arid areas showed the opposite pattern. Topographic variables were the least important driver and had a marginal effect on post-fire recovery. Additionally, semi-arid areas exhibited a low mean recovery rate, indicating limitations in the short-term recovery after a fire.ConclusionsOur study highlights the key role that drought duration plays in the recovery of vegetation after wildfires in the Mediterranean basin and, particularly, in forests located in climatically extreme areas. The results suggest that the predicted increase in drought duration coupled with a higher frequency and intensity of large fires may modify the structure and composition of Mediterranean forest ecosystems. Our analysis provides relevant information to evaluate and design adaptive management strategies in post-fire recovery hotspots of Mediterranean forest ecosystems.

Climate change impacts: Vegetation shift of broad-leaved and coniferous forests

Climate change is a variation in temperature and precipitation for longer periods due to global warming. It has an impact on tree species distribution, composition and diversity of the forests. Our study aims to answer how future climate change is likely to have an impact on the vegetation shift of broad-leaved and coniferous forests. The study used forest resource assessment data (2010–2014) of Nepal to assess vegetation shift from the perspective of climate change scenario. We collected altogether 392 presence points (observations) for broad-leaved forests and 99 for coniferous forests. These occurrence points accompanied by bioclimatic variables and topographical variables (Elevation, Slope and Aspect) were used as input data in a MaxEnt model to predict the distribution of the coniferous and broad-leaved forests. We found a potential area of the near current (1970–2000) coniferous forest replaced by a broad-leaved forest under a climate change scenario (SSP2 4.5 for 2041–2060) and vice versa. The total projected vegetation shift area of Nepal was found to be approximately 1800 km2 (i.e. over 3 % of the total forest area). Out of the total vegetation shift area, almost 90 % percent of the area was found to be replaced by broad-leaved forest while the remaining 10 % area was found to be replaced by a coniferous forest. The climate change impact has been noticed in the vegetation shift, particularly the presence of broad-leaved forest is more dominant. The study provides better insights into the impact of climate change on the existing vegetation under the future climate change scenario.

A locally explained heterogeneity model for examining wetland disparity

ABSTRACTIdentifying the factors influencing wetland variations is crucial for understanding the relationship of climate change with wetland conservation and management. The wetland distribution is associated with multiple variables, and the interactions among these variables are complex. In this study, we aim to explore an interpretable and quantitative analysis of factors related to wetland spatiotemporal variations on the Tibetan Plateau (TP). By combining SHapley Additive exPlanations with a spatially stratified heterogeneity model, we propose a locally explained stratified heterogeneity (LESH) model that well reveals the effects of multiple variable interactions on the spatiotemporal variations of wetlands. The results show that topographic variables are the most important variables related to the spatial distribution of wetlands on the TP, and climatic variables are the most relevant factors for the increase in the wetland area on the TP from 2015 to 2019. In addition, the interactions among multiple variables strongly influence wetlands on the TP. Among them, when other geographic variables interact with the evaporation variable, its explanatory power on wetland distribution is significantly increased. Knowledge of wetland distribution determinants can help us understand the evolution of wetlands and the impacts of climate change on wetland variations.

Biogeography and environmental preferences of Butia yatay (Mart.) Becc.

During the Quaternary, Chaco Phytogeographic Domain (Chaco) flora in subtropical South America experienced temperature and humidity fluctuations, primarily driven by wind dynamics, leading to significant shifts in species distribution. The palm Butia yatay is endemic to the Chaco and thrives in areas characterized by a warm-rainy climate and mostly restricted to sandy soils. To investigate the current geographic distribution of suitable habitat for B. yatay while assessing the significance of soil variables, we employed two distinct algorithms in species distribution modeling (SDM). We also determined whether the distribution of B. yatay has changed since the Pleistocene and whether these changes align with previously proposed Pleistocene refugia. In the present SDMs, we considered two separate sets of predictors, one set with bioclimatic variables only and the other set with bioclimatic topographic and soil variables. Additionally, we reconstructed the historical geographic distribution of suitable habitats using bioclimatic data. Our results suggested that the primary determinants of B. yatay's current distribution include precipitation and temperature of the driest month and soil cation exchange capacity. Incorporating soil variables affected the estimated size and range of suitable areas. Projections into the past indicated similar suitable habitat distributions during interglacial periods compared with the present. During the Last Glacial Maximum, climatically suitable habitat may have shifted northward, partially overlapping with previously suggested Pleistocene refugia located between the Paraná and Uruguay Rivers. These findings indicate the main factors driving the distribution and ecology of B. yatay and enhance understanding of subtropical flora shifts during the Quaternary. The approach also may prove valuable for other studies within the Chaco.

Pedology in Precision Agriculture from a Brazilian context

Precision agriculture (PA) is advancing in Brazil concerning several crops, mainly for medium to large-sized farms, occasionally evolving towards automation and digital agriculture. Soil knowledge is fundamental in this process, requiring studies on a detailed scale greater than 1:5,000, and demanding overcoming soil taxonomy concepts. This review article presents and discusses the most effective methods of PA from a soil management perspective, inducing advances for farming and producers in Brazil. Topography, electrical conductivity, proximal and remote sensing, and productivity work outlined soil mapping and their relationship to soil. The modeling of topographic variables through artificial intelligence offers new perspectives. The soil's apparent electrical conductivity can work at various depths, providing information about several pedological parameters. Finally, proximal, and remote sensing techniques could simulate different soil attributes, potentially integrating productivity data on studying plant attributes. Despite some differences, the four themes are complementary, and integrating data through geographic information systems results in a consistent option for defining management zones.

Suitability mapping and management monitoring in Castilian organic and conventional wheat fields with Sentinel-2 and spatial data

The Mediterranean region is a climate change hotspot due to projected precipitation decreases, soil erosion, and rising temperatures in the coming decades. In such a context, wheat productivity is expected to decrease; thus, all available croplands will need to be farmed to meet food demands. It is therefore crucial to have adequate management to help balance wheat production and environmental protection in specific target environments. With this aim, we present a study in Castile and Leon (Spain) in which we assess conventional and certified organic wheat grain yield and quality. Moreover, we spatially define wheat cultivation suitability categories and monitor crop rotation practices. For wheat suitability mapping, a fuzzy interference system was used to standardize topographic (height and slope), edaphic (pH, soil erosion, organic matter, and texture), and climatic (rainfall and temperature) variables and create marginal, suitable, and very suitable wheat cultivation suitability categories. We calculated the area of wheat suitability categories with confidence intervals and discussed factors affecting productivity. Additionally, we assessed the performance of certified organic and conventionally managed fields with organic and chemical fertilization. We also examined crop rotations using Sentinel-2 data over two seasons (2020−21). Certified organic management yielded 50% less than conventional management in similar environments while maintaining grain quality. Besides management, the main causes of the yield gap are the growing environment and variety. In conventional wheat management, organic fertilization achieves comparable yields to chemical fertilization. Crop rotation practices are uncommon, and over 50% of fields rotate wheat-to-barley or wheat-to-wheat, indicating poor soil management. The region has 25% of cropland classified as marginal wheat croplands (163,503 ± 33,796 ha) producing <2500 kg/ha. Wheat organic agriculture has decreased yield gaps in marginal croplands compared to more productive areas. By farming organic wheat in marginal croplands, the best croplands might be used for more productive farming.

Factors influencing the distribution of woody plants in tropical karst hills, south China.

The seasonal rainforests distributed across the tropical karst hills of south China are of high biodiversity conservation value and serve many important ecosystem functions. However, knowledge surrounding distribution patterns of woody plants in tropical karst hills remains limited. In this study, we surveyed the distribution of families, genera and species of woody flora at four slope positions (depression, lower slope, middle slope, and upper slope), and analyzed the influence of topographic and soil variables on the distribution of woody plants in the tropical karst hills of south China. Forty forest plots (each 20 m × 20 m) contained 306 species of woody plants with a diameter at breast height (DBH) ≥1 cm, representing 187 genera and 66 families. As slope increased, the number of families increased slowly, and the number of genera and species followed a concave-shaped trend, with the lowest number of genera and species in the lower slope position. Differences in species composition were significantly stronger between slope positions than within slope positions. The topographic and soil variables explained 22.4% and 19.6%, respectively, of the distribution of woody plants, with slope position, slope degree, soil potassium and soil water content as the most significant variables. The results of generalized linear mixed model analysis showed that total R2 of fixed effects on variation of woody species richness was 0.498, and rock outcrop rate and soil total phosphorus were the best fitting effects. Our results help to explain the community assembly mechanism and to inform management and protection strategies for species-rich seasonal rainforests in the karst area.

The effect of meteorological and environmental variables on food collection by honey bees (Apis mellifera)

Bees are strongly dependent on environmental and climatic conditions. Anthropogenic and climate changes are mainly responsible for restricted access of bees to food resources. Therefore, this study aimed to evaluate the effect of the most important meteorological and environmental variables affecting food collection by honey bees. Using electronic beehive scales, remote monitoring of daily hive weight changes of 60 colonies from 60 apiaries was performed during three beekeeping seasons. The hive weight changes mainly depended on the meteorological variables, particularly sunshine duration and air temperature. The topographic (e.g. landscape structure, forest habitat type, the share of the area with plant species attractive to honey bees) and spatial (e.g. proportion of forest, arable land, green area) variables contributed to the hive weight changes to a much lesser extent. Monitoring of the hive weight revealed that the meteorological conditions and the share of the area with most attractive plant species affected food collection by honey bees the most strongly. In conclusion, the long-term remote monitoring of honey bee colony hive weight changes on a large scale can be a source of substantial data about the impact of environmental and climate changes on bees and their food resources in order to restore and conserve the environment that will meet the nutritional needs of different species of bees.

Modelling landscape vulnerability to the Bracken fern (Pteridium aquilinum) invasion in a remnant urban Sandstone Sourveldt grassland ecosystem

The Bracken fern (Pteridium aquilinum) is one of the world's most pervasive alien invasive plants, exerting adverse effects on the sustainability and balance of remnant urban green ecosystems and thereby threatening the provision of valuable ecological goods and services in urban landscapes. To identify vulnerable landscapes and future distribution of the fern, this study aimed to model landscape vulnerability to the Bracken fern invasion using bio-physical, climatic, and remotely sensed data using Maxent species distribution model. Furthermore, the study sought to evaluate the value of variable selection on Maxent's modelling performance. Vulnerability to invasion was modelled based on four scenarios of different bio-climatic, physical and remotely sensed data inputs and model performance assessed using the area under the receiver operator characteristic curve (AUC). The results show that Model 1 (topographic variables) and Model 2 (topographic variables and Sentinel-2 spectral bands) predicted the western part of the eThekwini Municipal as highly suitable for the Bracken fern invasion, while Model 3 (topographic and bio-climatic variables) and Model 4 (topographic variables, Sentinel-2 spectral bands and bio-climatic variables), i.e., all variables predicted the central and western parts of the area as highly vulnerable to the Bracken fern invasion. Results also showed that elevation and rainfall wettest month (Bio13) were the most influential variables in determining the potential spread of the Bracken fern invasion and the model built using solely topographic and all the variables had the lowest (AUC of 0.895) and highest (AUC of 0.978) performance. The findings of this study are valuable to planning conservation initiatives in the often-vulnerable urban green ecology and managing invasive species in the remnant urban green spaces.

GIS-Based RUSLE Reservoir Sedimentation Estimates: Temporally Variable C-Factors, Sediment Delivery Ratio, and Adjustment for Stream Channel and Bank Sediment Sources

The empirical Revised Universal Soil Loss Equation (RUSLE) has been adapted to geographical information system (GIS) frameworks to study the spatial variability of soil erosion across landscapes and has also been used to estimate reservoir sedimentation. The literature presents contradictory results about the efficacy of using RUSLE in a GIS context for quantifying reservoir sedimentation, requiring further evaluation and validation of its estimates relative to measured reservoir sedimentation. Our primary objective was to determine if these contradictory results may be a function of the RUSLE’s inability to account for sediments derived from gullies, stream channels, or stream banks; the temporal variability of some of RUSLE’s empirically based factors such as the land cover/land management (C-) factor; and in some model renditions, the choice of value for the sediment delivery ratio (SDR). The usefulness of adjusting these estimates using a regional representative value of gully/stream bank sediment contributions was also assessed. High-spatial horizontal resolution (2 m) digital elevation models (DEMs) for 12 watersheds were used together with C-factor data for five representative years in a GIS-based RUSLE model that incorporates SDR within a sediment routing routine to study the impacts of choice of C-factor and SDR on reservoir sedimentation estimates. Choice of image date for developing C-factors was found to impact reservoir estimates. We also found that the value of SDR for some of the study watersheds would have to be unrealistically small to produce sedimentation estimates comparable to measured values. Estimates of reservoir sedimentation were comparable to measured data for 5 of the 12 watersheds, when the regionally based adjustment for gully/stream bank contributions was applied. However, differences remained large for the remaining seven watersheds. Statistical analysis revealed that certain combinations of geomorphic, pedologic, or topographic variables could be used to predict the degree of sediment underestimation with a significant and high level of correlation (0.72 &lt; R2 ≤ 0.99; p-value &lt; 0.05). Our findings indicate that the level of agreement between GIS-based RUSLE estimates of reservoir sedimentation and measured values is a function of watershed characteristics; for example, the area-weighted soil erodibility (K-) factor of the soils within the watershed and stream channels, the stream entrenchment ratio and bank full depth, the percentage of the stream corridor having slopes ≥ 21°, and the width of the stream flood way as a percentage of the watershed area. Within the context of GIS, these metrics are easily obtained from digital elevation models and publicly available soils data and may be useful in prioritizing reservoirs’ assessments for function and safety.

Vegetation–environment interactions: plant species distribution and community assembly in mixed coniferous forests of Northwestern Himalayas

One of the main goals of ecological studies is to disentangle the dynamics that underlie the spatiotemporal distribution of biodiversity and further functions of the ecosystem. However, due to many ecological and geopolitical reasons, many remote areas with high plant species diversity have not been assessed using newly based analytical approaches for vegetation characterization. Here, we classified and characterized different vegetation types (i.e., major plant communities) based on indicator species and on the influence of different environmental gradients in the Himalayan mixed coniferous forest, Pakistan. For that, we addressed the following questions: Does the vegetation composition of the Himalayan mixed coniferous forest correlate with climatic, topographic, geographic, and edaphic variables? Is it possible to identify plant communities through indicator species in relation to environmental gradients using multivariate approaches? Can this multivariate be helpful for conservation planning? During four consecutive years we assessed the vegetation composition and environmental variables (21 variables divided in geographic, climatic, topographic, and edaphic groups) of 156 50 m-trasects between an elevation of 2000–4000 m. Using newly based analytical approaches for community characterization, we found a total of 218 plant species clustered into four plant communities with the influence of environmental gradients. The highest index of similarity was recorded between Pinus-Cedrus-Viburnum (PCV) and Viburnum-Pinus-Abies (VPA) communities, and the highest index of dissimilarity was recorded between PCV and Abies-Juniperus-Picea (AJP) communities. Among these four communities, highest number of plant species (156 species) was recorded in PCV, maximum alpha diversity (H’ = 3.68) was reported in VPA, highest Simpson index (0.961) and Pielou’s evenness (0.862) were reported in VPA and AJP. The edaphic gradients (i.e., organic matter, phosphorous, pH and soil texture) and climatic factors (temperature, humidity) were the strongest environmental gradients that were responsible for structuring and hosting the diverse plant communities in mixed coniferous forest. Finally, the Himalayan mixed coniferous structure is more influenced by the spatial turnover beta-diversity process (βsim) than by the species loss (nestedness-resultant, βsne). Our analysis of the vegetation structure along the environmental gradient in the Himalayan mixed coniferous forest supported by sophisticated analytical approaches reveled indicator species groups, which are associated to specific microclimatic zones (i.e., vegetation communities). Within this focus, we side with the view that these results can support conservation planning and management for similar and different areas providing mitigating and preventive measures to reduce potential negative impacts, such as anthropic and climatic.

Using the Alternative Stable States Framework to understand the persistence of secondary shrubland within a Mexican oak forest with livestock and agricultural legacies

Many forests exist in human-modified mosaic landscapes subject to logging, agriculture, and livestock grazing. These disturbances can reduce ecosystem resilience and ultimately result in crossing thresholds, leading to the transition of the ecosystem into new stable states with different plant communities. The interaction between these disturbances and forest succession is still poorly understood, despite the growing need to manage these human-modified landscapes for biodiversity and productivity. We studied abrupt changes in vegetation composition in a Central Mexican oak forest and explored how these changes could indicate the coexistence of various alternative stable states caused by agricultural and livestock legacies. We established 36 strip transects covering 9000 m2 in patches with various land use legacies where we sampled vegetation, soil characteristics, and human disturbance. We assessed future and past tendencies in vegetation spatial patterns by evaluating seedling composition and analyzed time series of NDVI signals recorded by LANDSAT images (1972–2019). We utilized NMDS, PERMANOVA, and ANOVA analyses to assess if stable states can be distinguished based on historical and current variations in vegetation composition and structure in patches with different management legacies. We then assess potential factors that may contribute to differences in plant community composition by fitting edaphic, topographic, and anthropogenic disturbance variables to the ordination analyses.We identified three distinct plant communities: oak forest (dominated by late-successional Oak species), secondary shrubland (dominated by Vachellia-Dodonaea association), and a transitional forest-shrub community (a mixture of the first two communities). Time-series analyses and seedling composition provide evidence of long-term community segregation. Variation partition analysis demonstrated that anthropogenic disturbance (particularly livestock browsing), had a greater impact on species composition differentiation than environmental variables.A state-and-transition model is proposed, where cattle browsing and agricultural legacies trigger the transition from oak forests to a stable secondary shrubland community. Three potential self-reinforcing and interacting feedbacks are suggested: (i) micro-environmental filtering, (ii) plant-trait mediated assembly process, and (iii) cascading effects on tree recruitment caused by cattle browsing. The proposed state-and-transition model explains forest successional dynamics in systems with multiple anthropogenic disturbances and provides a conceptual framework for understanding and guiding successional trajectories.

Spatially explicit models of seed availability improve predictions of conifer regeneration following the 2018 Carr Fire in northern California

For many conifer species in dry conifer forests of North America, seeds must be present for postfire regeneration to occur, suggesting that seed dispersal from surviving trees plays a critical role in postfire forest recovery. However, the application of tree fecundity and spatial arrangement to postfire conifer recovery predictions have only recently become more common, and is often included at relatively coarse scales (i.e., 30 meters). In this study, we mapped surviving trees using lidar and created a spatially explicit estimate of seed density (seed shadows) with 10 m, 50 m, and 100 m median dispersal distances. We estimated the number of seeds produced by each tree using allometric relationships between tree size and fecundity. Along with the seed shadows, we used a suite of topographic variables as inputs to negative binomial hurdle models to predict conifer seedling abundance in 131 plots following the 2018 Carr Fire in northern California, USA. We compared models using each of the seed shadows to each other as well as to a model using the distance to the nearest surviving tree, which served as a baseline. All model formulations indicated that estimated seed availability was positively associated with conifer regeneration. Despite the importance of seed availability plays in regeneration and the substantial differences in seed availability represented by the different seed shadows in our analysis, we found surprisingly little difference in model performance regardless of which seed shadow was used. However, the models employing seed shadows outperformed the models with distance to the nearest live tree. Although we have demonstrated a modest improvement in predicting postfire conifer regeneration, the uncertainty in our results highlights the importance of tree detection and classification in future studies of this kind. Future studies may find it useful to consider other factors such as predation, site suitability, and seed mortality as potential drivers of discrepancies between total and realized dispersal kernels.

Species catalogue of Drymaeus (Mesembrinus) Alberts, 1850 (Gastropoda: Bulimulidae) from Brazil and new data on morphology and distribution of Drymaeus (Mesembrinus) interpunctus (Martens, 1887).

Herein, we attempted to obtain detailed data on the distribution of the species of Drymaeus (Mesembrinus) in Brazil, using biodiversity databases, malacological collections and literature as sources of occurrence records. We provided a catalogue of species, along with distribution maps. We also estimated the suitable distribution of Drymaeus (Mesembrinus) interpunctus using the maximum entropy approach. A detailed description of the anatomy of the soft parts of this species was provided, with new data on the pallial system. For each species we provided information on the compiled data associated with museum collections and the literature. Distribution maps including geographic boundaries, Brazilian biomes and altitude were made with QGIS software 3.16.10 Hannover. For niche modelling, nineteen bioclimatic variables and a topographic variable were used as predictors. The models were performed with MaxEnt version 3.3.3k. Most of the species are represented by scarce material in malacological collections; for some species, these records correspond to type material, indicating that they have not been recollected. Most of the species were represented by shells making anatomical comparison and DNA analysis difficult, limiting our ability to provide new criteria for species delimitation. Our results allowed us to expand the known distribution area for three species, Drymaeus dutaillyi, D. gereti and D. oreades, with new occurrence records in Brazil. The MaxEnt model showed a thin area of high suitability to D. (M.) interpunctus in the Southeastern Brazil, corresponding to the Atlantic Forest. Minimum temperature of the coldest month and mean temperature of coldest quarter were the variables that most influenced the development of the model. Drymaeus interpunctus was described based on specimens collected in Brazil without mention to the exact localities. Herein the new records from databases allowed to expand the known geographic distribution for this species and to infer its potential distribution. Although the type locality of D. interpunctus is in Brazil, the anatomy of the soft parts of specimens from this country was not previously described. The anatomy of the reproductive system of the specimens analyzed herein mostly corresponds to a previous description for specimens from Paraguay, except for the absence of penial sheath and the relative length of the bursa copulatrix duct. The results of niche modeling showed a thin area of high suitability for D. interpunctus and a vast area of moderate suitability, indicating that this species present a niche breadth that favors its occurrence in a range of different biomes, including less suitable areas. The small number of records obtained for most of the species and their restricted ranges associated with habitat destruction may indicate that they are of conservation concern.

Harnessing multiple data sources and emerging technologies for comprehensive urban green space evaluation

Urban greening is vital for environmental sustainability and human welfare, but assessing its impact can be challenging due to traditional methodologies' limitations. These methods usually focus on singular metrics, giving a constrained understanding of urban green spaces. This study presents an innovative methodology that overcomes traditional research constraints, providing a comprehensive view of urban green spaces. The approach integrates longitudinal and cross-sectional assessments, remote sensing images, and Google Street View data. It leverages geodata processing and semantic segmentation models for key metrics—green coverage rate and green view index—while classifying various landscape types through an image model. Osaka City has been selected as the study's exemplar to integrate topographic variables and validate this novel methodology. The study results showed that Osaka has conditions such as uneven distribution of green spaces and different quality of green spaces. Targeted suggestions such as focusing on areas with low green visibility and improving the quality of existing green spaces need to be developed accordingly based on the results, paving the way for strengthening the city's urban greening initiatives. Subsequent research must tackle limitations like data quality and model accuracy, spotlighting the crucial role of green spaces in sustainable urban development.