Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants.

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

Comparative Analysis of Water Stress Regimes in Avocado Plants during the Early Development Stage.

The avocado cv. Hass requires a suitable rootstock for optimal development under water stress. This study evaluated the performance of two avocado rootstocks (ANRR88 and ANGI52) grafted onto cv. Hass under four water stress conditions, 50% and 25% deficit, and 50% and 25% excess during the nursery stage. Plant height, leaf area (LA), dry matter (DM), and Carbon (OC) content in the roots, stems, and leaves were measured. Root traits were evaluated using digital imaging, and three vegetation indices (NDVI, CIRE, and MTCI) were used to quantify stress. The results showed that genotype significantly influenced the response to water stress. ANRR88 exhibited adaptation to moderate to high water deficits. ANGI52 adapted better to both water deficit and excess, and showed greater root exploration. LA and DM reductions of up to 60% were observed in ANRR88, suggesting a higher sensitivity to extreme changes in water availability. More than 90% of the total OC accumulation was observed in the stem and roots. The NDVI and the MTCI quantified the presence and levels of stress applied, and the 720 nm band provided high precision and speed for detecting stress. These insights are crucial for selecting rootstocks that ensure optimal performance under varying water availability, enhancing productivity and sustainability.

Assembly structures of coastal woody species of eastern South America: Patterns and drivers.

Tropical regions have provided new insights into how ecological communities are assembled. In dry coastal communities, water stress has been hypothesized to determine plant assembly structure by favoring preadapted lineages from neighboring ecosystems, consistent with functional clustering. However, it is unclear whether this hypothesis is sufficient to explain how coastal communities in tropical ecosystems are assembled. Here, we test whether water stress or other factors drive community assembly in woody plant communities across the coastal zone of Brazil, a tropical ecosystem. We characterized functional and phylogenetic structures of these communities and determined the underlying environmental factors (e.g., water stress, historical climate stability, edaphic constraints, and habitat heterogeneity) that drive their community assembly. Assemblages of coastal woody species show geographically varied patterns, including stochastic arrangements, clustering, and overdispersion of species relative to their traits and phylogenetic relatedness. Topographic complexity, water vapor pressure, and soil nutrient availability best explained the gradient in the functional structure. Water deficit, water vapor pressure, and soil organic carbon were the best predictors of variation in phylogenetic structure. Our results support the water-stress conservatism hypothesis on functional and phylogenetic structure, as well as the effect of habitat heterogeneity on functional structure and edaphic constraints on functional and phylogenetic structure. These effects are associated with increased phenotypic and phylogenetic divergence of woody plant assemblages, which is likely mediated by abiotic filtering and niche opportunities, suggesting a complex pattern of ecological assembly.

Taxonomic diversity of amphibians (Amphibia, Anura) and reptiles (Reptilia, Testudines, Squamata) in a heterogeneous landscape in west-central Mexico: a checklist and notes on geographical distributions.

In Mexico, land use changes have significantly impacted the diversity of amphibians and reptiles in a negative way. In light of this, we evaluate the alpha and beta components of the taxonomic diversity of amphibians and reptiles in a heterogeneous landscape in west-central Mexico. Additionally, we provide a checklist of amphibian and reptile species recorded over nine years of observations within the studied landscape and surrounding areas. The land cover/use types with the highest species richness and alpha taxonomic diversity differed between amphibians and reptiles. Overall beta taxonomic diversity was high for both groups, but slightly higher in reptiles. This taxonomic differentiation mainly corresponded to a difference in the turnover component and was greater in pristine habitats compared to disturbed ones. The checklist records 20 species of amphibians (ten of which are endemic) and 48 of reptiles (30 endemics). Additionally, the study expands the known geographical distribution range of one species of frog and three species of snakes. Our findings suggest that heterogeneous landscapes with diverse land cover/use types can provide essential habitats for the conservation of amphibian and reptile species.

Does the diversity of vegetation and diatoms correlate with soil and water factors in a tropical cloud forest's complex land use/land cover scenario?

Soil and water characteristics in micro basins with different land uses/land cover (LULC) can influence riparian vegetation diversity, stream water quality, and benthic diatom diversity. We analyzed 18 streams in the upper part of the La Antigua River basin, México, surrounded by cloud forests, livestock pastures, and coffee plantations. Concentrations of P, C, and N were elevated in the humus of forested streams compared to other land uses. In contrast, cations, ammonium, and total suspended solids (TSS) of water streams were higher in pastures and coffee plantations. These results indicate that LULC affects stream chemistry differently across land uses. Vegetation richness was highest (86-133 spp.) in forest streams and lowest in pastures (46-102), whereas pasture streams had the greatest richness of diatoms (9-24), likely due to higher light and temperatures. Some soil and water characteristics correlated with both true diversity and taxonomic diversity; soil carbon exchange capacity (CEC) correlated with vegetation diversity (r = 0.60), while water temperature correlated negatively (r = - 0.68). Diatom diversity was related to soil aluminum (r = - 0.59), magnesium (r = 0.57), water phosphorus (r = 0.88), and chlorophyll (r = 0.75). These findings suggest that land use affects riparian vegetation, while physical and chemical changes influence diatom diversity in stream water and soil. The lack of correlation between vegetation and diatom diversity indicates that one cannot predict the other. This research is an essential first step in understanding how land use changes impact vegetation and diatom diversity in mountain landscapes, providing valuable insights for environmental monitoring and conservation efforts in tropical cloud forests.

Marine litter along the Peruvian coast: spatiotemporal composition, sources, hazard, and human modification relations.

Marine litter (ML) represents an escalating environmental issue, particularly in Latin America, where comprehensive studies are scarce despite critical solid waste management challenges and continuous human modification occurring on the coasts. To contribute to the knowledge of ML in the southeast Pacific, this study examined contamination across 10 beaches on Peru's extensive coast. Overall, ML contamination was categorized as moderate (with an ML concentration of 0.49 ± 0.64 items∙m-2), while significantly differing between summer (dirty with an ML concentration of 0.56 ± 0.66 items∙m-2) and winter (moderate with an ML concentration of 0.47 ± 0.60 items∙m-2). Three beaches were extremely dirty (concentrations of ML exceeded 1.0 items∙m-2). Predominant materials, items, and sources were plastic, cigarette butts (CBs), and mixed packaging. The Peruvian coast faced CB leachate impact (CBPI = 3.5 ± 3.5), reaching severe levels on two beaches, with considerable hazardous litter (HALI = 3.0 ± 2.9). Additionally, a higher degree of human modification was associated with higher ML levels along the coast.

Low-cost clamp for the measurement of vegetation spectral signatures.

Spectral signatures allow the characterization of a surface from the reflected or emitted energy along the electromagnetic spectrum. This type of measurement has several potential applications in precision agriculture. However, capturing the spectral signatures of plants requires specialized instruments, either in the field or the laboratory. The cost of these instruments is high, so their incorporation in crop monitoring tasks is not massive, given the low investment in agricultural technology. This paper presents a low-cost clamp to capture spectral leaf signatures in the laboratory and the field. The clamp can be 3D printed using PLA (polylactic acid); it allows the connection of 2 optical fibers: one for a spectrometer and one for a light source. It is designed for ease of use and holds a leave firmly without causing damage, allowing data to be collected with less disturbance. The article compares signatures captured directly using a fiber and the proposed clamp; noise reduction across the spectrum is achieved with the clamp.

Semantic Segmentation in Large-Size Orthomosaics to Detect the Vegetation Area in Opuntia spp. Crop.

This study focuses on semantic segmentation in crop Opuntia spp. orthomosaics; this is a significant challenge due to the inherent variability in the captured images. Manual measurement of Opuntia spp. vegetation areas can be slow and inefficient, highlighting the need for more advanced and accurate methods. For this reason, we propose to use deep learning techniques to provide a more precise and efficient measurement of the vegetation area. Our research focuses on the unique difficulties posed by segmenting high-resolution images exceeding 2000 pixels, a common problem in generating orthomosaics for agricultural monitoring. The research was carried out on a Opuntia spp. cultivation located in the agricultural region of Tulancingo, Hidalgo, Mexico. The images used in this study were obtained by drones and processed using advanced semantic segmentation architectures, including DeepLabV3+, UNet, and UNet Style Xception. The results offer a comparative analysis of the performance of these architectures in the semantic segmentation of Opuntia spp., thus contributing to the development and improvement of crop analysis techniques based on deep learning. This work sets a precedent for future research applying deep learning techniques in agriculture.

Coastal Dune Vegetation Dynamism and Anthropogenic-Induced Transitions in the Mexican Caribbean during the Last Decade.

In the Mexican Caribbean, environmental changes, hydrometeorological events, and anthropogenic activities promote dynamism in the coastal vegetation cover associated with the dune; however, their pace and magnitude remain uncertain. Using Landsat 7 imagery, spatial and temporal changes in coastal dune vegetation were estimated for the 2011-2020 period in the Sian Ka'an Biosphere Reserve. The SAVI index revealed cover changes at different magnitudes and paces at the biannual, seasonal, and monthly timeframes. Climatic seasons had a significant influence on vegetation cover, with increases in cover during northerlies (SAVI: p = 0.000), while the topographic profile of the dune was relevant for structure. Distance-based multiple regressions and redundancy analysis showed that temperature had a significant effect (p < 0.05) on SAVI patterns, whereas precipitation showed little influence (p > 0.05). The Mann-Kendall tendency test indicated high dynamism in vegetation loss and recovery with no defined patterns, mostly associated with anthropogenic disturbance. High-density vegetation such as mangroves, palm trees, and shrubs was the most drastically affected, although a reduction in bare soil was also recorded. This study demonstrated that hydrometeorological events and climate variability in the long term have little influence on vegetation dynamism. Lastly, it was observed that anthropogenic activities promoted vegetation loss and transitions; however, the latter were also linked to recoveries in areas with pristine environments, relevant for tourism.

Influence of Distance, Environmental Factors, and Native Vegetation on Honeybee (Apis mellifera) Foraging in Arid Shrublands and Grasslands.

This study determined the influence of foraging distance, environmental factors, and native vegetation on honeybee (Apis mellifera) foraging in arid shrublands and grasslands in Northern Mexico. Apiary distance from inflorescence sites did not have a significant influence on the intensity of foraging. Apiary location and landscape were decisive factors in the response of honeybees to environmental factors. Air temperature, minimum temperature, wind velocity, and relative humidity explained foraging by 87, 80, 68, and 41% (R2), respectively, in shrubland sites in open landscapes but had no significant influence on foraging in the grassland sites in a valley surrounded by hills (1820-2020 amsl). Nights with a minimum temperature of <20 °C increased foraging activity during the day. Minimum temperature, which has the least correlative influence among climate elements, can be used to determine climate change's impact on bees. The quantity of available inflorescence explained the foraging intensity by 78% in shrublands and 84% in grasslands. Moreover, when honeybees depended mainly on native vegetation in grasslands, the quantity of inflorescence explained the intensity of foraging by 95%. High intensity of honeybee foraging was observed in allthorn (Koeberlinia spinosa) and wait-a-minute bush (Mimosa aculeaticarpa) in shrublands and honey mesquite (Neltuma glandulosa) and wait-a-minute bush (Mimosa aculeaticarpa) in grasslands. The findings and baseline data contributed by this study may be used to identify suitable environments for increasing apiary productivity and other agricultural and ecological benefits.

Time since fire as a driver of taxonomic and phylogenetic patterns of grassland plant communities.

Fire plays a key role in grasslands, determining the distribution and evolution of species and boundaries with neighboring ecosystems. Evidence of community-wide responses to fire is largely based on taxonomic and functional descriptors, while the phylogenetic dimension is overlooked. Here we evaluated how the taxonomic and phylogenetic structure of grassland plant communities responded to a time since fire (TSF) gradient. We sampled 12 communities in Southern Brazil under varying TSF and calculated taxonomic species richness (S) and dominance (D), phylogenetic diversity (PD), and mean phylogenetic distances (MPD). We used Structural Equation Models to test the relationships between the environmental gradient and community descriptors. Communities with longer TSF presented higher PD and MPD but lower species richness and increased taxonomic dominance. These sites were dominated by monocots, specifically C4 grasses, but also presented exclusive clades, whereas recently-burned sites presented lower taxonomic dominance and more species distributed in a wider variety of clades. Our results indicate that these scenarios are interchangeable and dependent on fire management. Fire adaptation was not constrained by phylogenetic relatedness, contrasting with previous findings for tropical savannahs and indicating that temperate and tropical non-forest ecosystems from South America respond differently to fire, possibly due to different evolutionary histories.

Towards accurate monitoring of water content in woody tissue across tropical forests and other biomes.

Forest ecosystems face increasing drought exposure due to climate change, necessitating accurate measurements of vegetation water content to assess drought stress and tree mortality risks. Although Frequency Domain Reflectometry offers a viable method for monitoring stem water content by measuring dielectric permittivity, challenges arise from uncertainties in sensor calibration linked to wood properties and species variability, impeding its wider usage. We sampled tropical forest trees and palms in eastern Amazônia to evaluate how sensor output differences are controlled by wood density, temperature and taxonomic identity. Three individuals per species were felled and cut into segments within a diverse dataset comprising five dicotyledonous tree and three monocotyledonous palm species on a wide range of wood densities. Water content was estimated gravimetrically for each segment using a temporally explicit wet-up/dry-down approach and the relationship with the dielectric permittivity was examined. Woody tissue density had no significant impact on the calibration, but species identity and temperature significantly affected sensor readings. The temperature artefact was quantitatively important at large temperature differences, which may have led to significant bias of daily and seasonal water content dynamics in previous studies. We established the first tropical tree and palm calibration equation which performed well for estimating water content. Notably, we demonstrated that the sensitivity remained consistent across species, enabling the creation of a simplified one-slope calibration for accurate, species-independent measurements of relative water content. Our one-slope calibration serves as a general, species-independent standard calibration for assessing relative water content in woody tissue, offering a valuable tool for quantifying drought responses and stress in trees and forest ecosystems.

Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants.

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

Mercury distribution, bioaccumulation, and biomagnification in riparian ecosystems from a neotropical savanna floodplain, Araguaia River, central Brazil.

Litterfall is the main source of dry deposition of mercury (Hg) into the soil in forest ecosystems. The accumulation of Hg in soil and litter suggests the possibility of transfer to terrestrial invertebrates through environmental exposure or ingestion of plant tissues. We quantified total mercury (THg) concentrations in two soil layers (organic: 0-0.2 m; mineral: 0.8-1 m), litter, fresh leaves, and terrestrial invertebrates of the Araguaia River floodplain, aiming to evaluate the THg distribution among terrestrial compartments, bioaccumulation in invertebrates, and the factors influencing THg concentrations in soil and invertebrates. The mean THg concentrations were significantly different between the compartments evaluated, being higher in organic soil compared to mineral soil, and higher in litter compared to mineral soil and fresh leaves. Soil organic matter content was positively related to THg concentration in this compartment. The order Araneae showed significantly higher Hg concentrations among the most abundant invertebrate taxa. The higher Hg concentrations in Araneae were positively influenced by the concentrations determined in litter and individuals of the order Hymenoptera, confirming the process of biomagnification in the terrestrial trophic chain. In contrast, the THg concentrations in Coleoptera, Orthoptera and Hymenoptera were not significantly related to the concentrations determined in the soil, litter and fresh leaves. Our results showed the importance of organic matter for the immobilization of THg in the soil and indicated the process of biomagnification in the terrestrial food web, providing insights for future studies on the environmental distribution of Hg in floodplains.

Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments.

The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with the data transmission between IoT devices, resulting in the need for signal propagation modeling, which considers the effect of vegetation on its propagation. In this context, this research was conducted at the Federal University of Pará, using measurements in a wooded environment composed of the Pau-Mulato species, typical of the Amazon. Two machine learning-based propagation models, GRNN and MLPNN, were developed to consider the effect of Amazonian trees on propagation, analyzing different factors, such as the transmitter's height relative to the trunk, the beginning of foliage, and the middle of the tree canopy, as well as the LoRa spreading factor (SF) 12, and the co-polarization of the transmitter and receiver antennas. The proposed models demonstrated higher accuracy, achieving values of root mean square error (RMSE) of 3.86 dB and standard deviation (SD) of 3.8614 dB, respectively, compared to existing empirical models like CI, FI, Early ITU-R, COST235, Weissberger, and FITU-R. The significance of this study lies in its potential to boost wireless communications in wooded environments. Furthermore, this research contributes to enhancing more efficient and robust LoRa networks for applications in agriculture, environmental monitoring, and smart urban infrastructure.

Dissimilar forests along the Rio Doce watershed call for multiple restoration references to avoid biotic homogenization.

An environmental disaster caused by the rupture of a mining tailings dam has impacted a large area of the Rio Doce watershed in the Brazilian Atlantic Forest, resulting in unprecedented damage at spatial and temporal scales. The Atlantic Forest is one of the world's most important biodiversity hotspots. A long history of land use conversion has resulted in a highly fragmented landscape. Despite numerous restoration initiatives, these efforts have often biased criteria and use limited species assemblages. We conducted a comprehensive synthesis of the plant community in riparian forests along the Rio Doce watershed. Our work detailed vegetation composition (tree and sapling strata) and examined its relationship with edaphic and landscape factors, aiming to inform restoration projects with scientifically robust knowledge. A total of 4906 individuals from the tree strata and 4565 individuals from the sapling strata were recorded, representing a total of 1192 species from 75 families. Only 0.8% of the tree species and 0.5% of the sapling species occurred in all sampled sectors, with over 84% of the species occurring in a single watershed sector for both strata. We observed a high species heterogeneity modulated by turnover (92.3% in the tree, and 92.7% in the sapling strata) among sites. Overall, our research revealed a gradient of soil fertility influencing species composition across different strata. Additionally, we discovered that preserved landscapes had a positive impact on species diversity within both strata. The species exclusivity in the sampled sites and the high turnover rate imply the need to consider multiple reference ecosystems when restoring the watershed to reduce the risk of biotic homogenization. Finally, the reference ecosystems defined here serve as a basis for the selection of locally particular species in the implementation of restoration projects that aim to improve biodiversity, ecosystem services, and water security.

Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize.

Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher -logp values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1. Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities.

LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions.

Designing and deploying telecommunications and broadcasting networks in the challenging terrain of the Amazon region pose significant obstacles due to its unique morphological characteristics. Within low-power wide-area networks (LPWANs), this research study introduces a comprehensive approach to modeling large-scale propagation loss channels specific to the LoRaWAN protocol operating at 915 MHz. The objective of this study is to facilitate the planning of Internet of Things (IoT) networks in riverside communities while accounting for the mobility of end nodes. We conducted extensive measurement campaigns along the banks of Universidade Federal do Pará, capturing received signal strength indication (RSSI), signal-to-noise ratio (SNR), and geolocated point data across various spreading factors. We fitted the empirical close-in (CI) and floating intercept (FI) propagation models for uplink path loss prediction and compared them with the Okumura-Hata model. We also present a new model for path loss with dense vegetation. Furthermore, we calculated received packet rate statistics between communication links to assess channel quality for the LoRa physical layer (PHY). Remarkably, both CI and FI models exhibited similar behaviors, with the newly proposed model demonstrating enhanced accuracy in estimating radio loss within densely vegetated scenarios, boasting lower root mean square error (RMSE) values than the Okumura-Hata model, particularly for spreading factor 9 (SF9). The radius coverage threshold, accounting for node mobility, was 945 m. This comprehensive analysis contributes valuable insights for the effective deployment and optimization of LoRa-based IoT networks in the intricate environmental conditions of the Amazon region.

Dataset about Mexico's forest diversity: Site locations of tree species, wood densities, and geographic database of forest-vegetation provinces.

A dataset about three topics is provided, as a follow-up to the article "Mexico's forest diversity: common tree species and proposed forest-vegetation provinces" by Ricker et al. [1]. Firstly, 6927 site locations are provided for 22,532 trees of 1452 species. Secondly, measurements of basic wood-densities are reported for 779 tree species, obtained from 5256 trunk-core samples from Mexico's national forest inventory, and ranging from 0.05 to 0.93 g/cm3. Third, the data and maps of the forest-vegetation provinces from [1] were updated with the new cartography of Mexico's vegetation and land use (base year 2018). The maps are available now in an adjusted presentation as a shapefile-set for ArcGIS, as well as map-package and image files.

Modeling the Brazilian Cerrado land use change highlights the need to account for private property sizes for biodiversity conservation.

Simulating future land use changes can be an important tool to support decision-making, especially in areas that are experiencing rapid anthropogenic pressure, such as the Cerrado-Brazilian savanna. Here we used a spatially-explicit model to identify the main drivers of native vegetation loss in the Cerrado and then extrapolate this loss for 2050 and 2070. We also analyzed the role of property size in complex Brazilian environmental laws in determining different outcomes of these projections. Our results show that distance to rivers, roads, and cities, agricultural potential, permanent and annual crop agriculture, and cattle led to observed/historical loss of vegetation, while protected areas prevented such loss. Assuming full adoption of the current Forest Code, the Cerrado may lose 26.5 million ha (± 11.8 95% C.I.) of native vegetation by 2050 and 30.6 million ha (± 12.8 95% C.I.) by 2070, and this loss shall occur mainly within large properties. In terms of reconciling conservation and agricultural production, we recommend that public policies focus primarily on large farms, such as protecting 30% of the area of properties larger than 2500 ha, which would avoid a loss of more than 4.1 million hectares of native vegetation, corresponding to 13% of the predicted loss by 2070.