Dense Vegetation Research Articles

Effects of vegetative ground cover on seedling establishment of the invasive liana old man’s beard (Clematis vitalba)

Abstract Opportunistic use of limited resources is often attributed to invasive species, and as a mature vine, old man’s beard (Clematis vitalba L.) is known to have devastating negative impacts on the trees it colonizes. No previous experimental studies have been published on how easily C. vitalba seedlings can colonize ground covered by other established vegetation. This species has had an increasing presence in forestry blocks and riparian zones in New Zealand, both of which usually maintain some grass cover. To determine the importance of vegetative ground cover for preventing ingress of new C. vitalba plants, this study looked at seedling emergence through the soil and establishment of C. vitalba within four different levels of grassy cover at three sites: (1) ground kept bare after vegetation removal; (2) ground bare at C. vitalba seed sowing, but thereafter allowed to recover; (3) vegetative cover trimmed to 4 cm high at C. vitalba sowing, and then allowed to recover; and (4) unmanaged vegetation. At the highest level of vegetation density (unmanaged vegetation), no C. vitalba seedlings were ever detected throughout a 1-yr monitoring period. At lower ground cover densities, poor seedling emergence was observed, with a maximum of 36% of seeds sown in bare plots producing a seedling. Also, seedlings did not survive past 1 yr, except in bare plots or in plots where vegetation grew sparsely. However, seedlings that did survive began producing multiple stems within 6 mo of emergence. These results indicate that obstacles to seedling emergence and poor development at the young seedling stage when vegetative cover is dense severely limit C. vitalba’s chances to invade new sites via seed. Yet some successful seedling recruitment does occur due to the magnitude of the propagule pressure on the landscape and the difficulty of maintaining high-density ground cover across large areas throughout the year.

Optimizing GEDI Canopy Height Estimation and Analyzing Error Impact Factors Under Highly Complex Terrain and High-Density Vegetation Conditions

The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense canopy can significantly reduce the accuracy of GEDI canopy height estimations. We selected the South Taihang region of Henan Province, China, as our study area and proposed an optimization framework to improve GEDI canopy height estimation accuracy. This framework includes correcting geolocation errors in GEDI footprints, screening and analyzing features that affect estimation errors, and combining two regression models with feature selection methods. Our findings reveal a geolocation error of 4 to 6 m in GEDI footprints at the orbital scale, along with an overestimation of GEDI canopy height in the South Taihang region. Relative height (RH), waveform characteristics, topographic features, and canopy cover significantly influenced the estimation error. Some studies have suggested that GEDI canopy height estimates for areas with high canopy cover lead to underestimation, However, our study found that accuracy increased with higher canopy cover in complex terrain and dense vegetation. The model’s performance improved significantly after incorporating the canopy cover parameter into the optimization model. Overall, the R2 of the best-optimized model was improved from 0.06 to 0.61, the RMSE was decreased from 8.73 m to 2.23 m, and the rRMSE decreased from 65% to 17%, resulting in an accuracy improvement of 74.45%. In general, this study reveals the factors affecting the accuracy of GEDI canopy height estimation in areas with complex terrain and dense vegetation cover, on the premise of minimizing GEDI geolocation errors. Employing the proposed optimization framework significantly enhanced the accuracy of GEDI canopy height estimates. This study also highlighted the crucial role of canopy cover in improving the precision of GEDI canopy height estimation, providing an effective approach for forest monitoring in such regions and vegetation conditions. Future studies should further improve the classification of tree species and expand the diversity of sample tree species to test the accuracy of canopy height estimated by GEDI in different forest structures, consider the distortion of optical remote sensing images caused by rugged terrain, and further mine the information in GEDI waveforms so as to enhance the applicability of the optimization framework in more diverse forest environments.

Under cover: the nuanced influence of functional properties of cover on resource selection by pygmy rabbits (Brachylagus idahoensis)

Abstract Animals at risk of predation select habitat that enhances security from predators. Two properties of cover related to security are concealment (i.e., habitat structure that blocks an individual from detection by others) and visibility (i.e., visual information accessible relative to habitat structure). Although these properties are often negatively correlated, they are not always inverse; animals in habitat with heterogeneous structure may be able to select for both. We investigated habitat use by pygmy rabbits (Brachylagus idahoensis) at 2 scales (patch and microsite) to evaluate the influence of both structural properties of cover and visual properties (concealment and visibility) on habitat use by prey. We contrasted vegetation structure at paired used and unused patches. At each patch, we measured concealment and viewshed (i.e., visibility) in 3 orientations (i.e., aerial, terrestrial, and overall) and structural density using lidar. We also measured heights of the 3 tallest shrubs. Additionally, within used patches, we assessed the density of fecal pellets as an index of intensity of use and also measured distance to nearest burrow. At the patch scale, rabbits selected for structural properties of cover (dense vegetation and tall shrubs), but not visual properties of cover. Pygmy rabbits more intensively used microsites associated with high terrestrial concealment and in proximity to burrows. Our results suggest that pygmy rabbits may perceive greater threat from terrestrial as opposed to aerial predators at both scales, and they also indicate a nuanced relationship between properties of cover and habitat use.

Structural characterization and species composition of mangrove vegetation in Lhokseumawe, Indonesia: Insight from multivariate analysis

The structure of the plant community significantly influences the equilibrium of the surrounding environment, affecting the trophic interactions within its ecosystem. The investigation of mangrove vegetation in Cut Mamplam village was carried out in September 2021 to determine the condition and structural characteristics of the mangrove ecosystem by multivariate analysis. The mangrove vegetation in Cut Mamplam Village was sampled at three observation stations by establishing a transect parallel to the coast. Subsequently, the community structure was examined and assessed using cluster analysis and nonmetric multidimensional scaling using the PRIMER v7 software. The study findings indicate that the mangrove forest vegetation in Cut Mamplam Village consists of five species from three families. Avicennia alba, A. lanata, Bruguiera gymnorrhiza, Rhizophora mucronata and Sonneratia alba. The highest density of mangrove vegetation was observed in A. alba in all categories: trees (616.67 ind/ha), seedlings (833.33 ind/ha) and saplings (66666.67 ind/ha). Additionally, the highest important value index (IVI) was recorded in A. alba for all categories: trees (232.16%), seedlings (102.40%), and saplings (228.43%). A similarity of mangrove density between species was noted at 60%, resulting in the formation of two distinct groups. Regarding the basal area, the vegetation stands of A. alba and A. lanata exhibited the highest values (280.61 and 266.03 m2/ha, respectively). The similarity of basal area among the observed species was 20%, resulting in the formation of two distinct groups. Additionally, Station II, classified as having mature vegetation, demonstrated a maturity similarity of 80% between observation stations.

Geospatial Analysis of Climate Change Impacts on Vegetation Dynamics in Owerri West, Imo State

Climate change is a global phenomenon with profound effects on the environment, economies, and societies. It refers to significant changes in global temperatures and weather patterns over time. This study explores the impact of climate change on vegetation in Owerri West, Imo State, Nigeria, using geospatial techniques over a 20-year period (2003–2023). By analyzing satellite imagery and Normalized Difference Vegetation Index (NDVI) values alongside key climatic variables such as precipitation and land surface temperature (LST), the research seeks to quantify the extent of vegetation changes due to climate variability. The study uses Landsat 8 satellite data to evaluate spatiotemporal trends in vegetation health. NDVI values in 2003 ranged from a high of 0.45, indicating healthy and dense vegetation, to a low of -0.04, representing barren areas. By 2013, NDVI values had drastically declined, with a maximum of 0.12 and a minimum of -0.33, despite an increase in precipitation from 3300 mm (2003) to 3900 mm (2013). This decline suggests that other factors, such as extreme weather events or urbanization, contributed to vegetation stress. In 2023, NDVI values showed partial recovery, with a maximum of 0.28 and a minimum of 0.05, although precipitation levels dropped to a range of 600 mm to 540 mm. The broader temperature range in 2023, with a maximum of 32°C and a minimum of 17°C, likely reduced heat stress, allowing for vegetation recovery, though still below the 2003 levels, The findings highlight the complex interplay between climatic variables and vegetation health, where precipitation and temperature changes significantly influence vegetation dynamics. However, the NDVI decline in 2013, despite high precipitation, suggests that anthropogenic factors like urbanization and land use changes also play a critical role. This research emphasizes the importance of integrating remote sensing and GIS for monitoring vegetation responses to climate change. The study calls for sustainable land management practices and climate adaptation strategies to enhance vegetation resilience in the region.

Establishment of mangroves on a new mud bank by a combination of UAS SfM photogrammetry and LiDAR monitoring

Abstract. Understanding how mangroves respond to sea level rise is critical for coastal management and conservation. This study investigates the feasibility of two remote sensing techniques, Light Detection and Ranging (LiDAR) and Structure-from-Motion (SfM) photogrammetry, to monitor changes in mud elevation that are critical to mangrove establishment dynamics. SfM photogrammetry can provide low-cost 3D modeling from imagery, while LiDAR provides high-precision elevation and vegetation data. Our analysis shows that SfM photogrammetry provides reasonably accurate surface elevation data with a root mean square error (RMSE) of 0.14 meters and a significant correlation coefficient (r = 0.74) compared to LiDAR measurements. This highlights the complementarity between the two, as the effectiveness of SfM photogrammetry is in the early stages of colonization, and then the need to use LiDAR with dense vegetation as the mangroves mature. This shows that a simple method of photogrammetry can remain effective until the vegetation reaches a certain threshold, and this can be particularly useful in the perspective of using mud elevation as a first low-cost diagnosis for potential mangrove rehabilitation project to assess the colonization suitability of mudflats.

Analisis Potensi Kawasan Pengembangan Lahan Pertanian Tanaman Padi Sawah di Kabupaten Tanah Datar

This study aims to determine 1) the level of suitability of wet-rice land on food agricultural land in Tanah Datar Regency, 2) existing land cover in Tanah Datar Regency and 3) potential wet-rice land development areas in Tanah Datar Regency. The method used is a descriptive approach with quantitative research. Data was processed using fuzzy logic, supervised classification, and spatial data analysis. The results of this study show 1) Tanah Datar Regency has 3 levels of land suitability with an index value of 0-0.59 which is classified at the level of unsuitable to less suitable for rice paddy agricultural crops, with an area of 7,583.9 Ha with a percentage of 5.5%. The second category of land suitability with an index value of 0.6-0.79 is classified as moderately suitable to suitable, with an area of 98,955.7 Ha with a percentage of 71.8%. The third category of land suitability with an index value of 0.8-1.0 which is classified as moderately - highly suitable for wetland agricultural crops, with an area of 31,267.7 Ha with a percentage of 22.7% of the total area of the study site. 2) Analysis of existing land cover using Landsat 8 satellite images in 2023 in Tanah Datar District shows that the structure of land cover in 2023 is as follows: dense vegetation cover covering 93,385.5 Ha with a percentage of 67.9%, followed by agricultural land covering 35,630.6 Ha with a percentage of 25.8%, settlements covering 1,828.3 Ha with a percentage of 1.4%, open land covering 137.6 Ha with a percentage of 0.09%, and waters.

IDENTIFIKASI PERUBAHAN KERAPATAN VEGETASI DI KAWASAN HUTAN DENGAN TUJUAN KHUSUS UNIVERSITAS LAMBUNG MANGKURAT

The Forest Area with a Special Purpose of Lambung Mangkurat University has an area of approximately 2000 Ha, divided into 2 areas, namely Bukit Babaris and Bukit Waringin. Identification of changes in land cover and vegetation density has never been carried out, especially in the Babaris mountain area (1,617 hectares). This study aims to compare the results of the accuracy of the Maximum Likelihood Classification (MLC) method with the Normalized Different Vegetation Index (NDVI) method in identifying vegetation density from 2018 to 2022. The results of the accuracy test for the vegetation density method using the confusion matrix show that the Overall Accuracy value of the MLC method is low lower, namely 92.5%, and the NDVI method 93.75%. It can be concluded that the NDVI method is more sensitive in detecting vegetation than the MLC method. Factors that affect the inaccuracy of the MLC method depend on the accuracy and the number of class class training samples taken. Based on the results of the interpretation of density changes from the two methods, from 2018 to 2022 the area of vegetation classes tends to increase and looks fluctuating. The area of change in vegetation density in the MLC method of the non-vegetation classification class is relatively stable at over 9 ha/year, although the classification class rarely dominates over the year. Changes in the density of non-vegetation class vegetation in the NDVI method tend to decrease significantly in 2020 and 2021. Based on the two most accurate methods obtained by the NDVI method, that there is an increase in vegetation density in the KHDTK ULM area of the Babaris hill.

Evaluating the Spatio-Temporal Distribution of Nitrogen Dioxide, Land Surface Temperature and NDVI in Nairobi City County

Abstract. Cities are becoming larger and it is estimated that by the year 2050, more than 6 billion people will be living in cities. As cities expand and grow, the quality of life and conditions will also transform. An integral part of environmental studies has been statistical analysis in modelling the spatial dynamics of land use changes. The research involved the use of satellite imagery to determine yearly averaged values of LST and NDVI from Landsat 8 OLI/TIR and monthly mean values of Nitrogen Dioxide (NO2) from Sentinel 5-Precursor (Sentinel-5P) across Nairobi City County. The datasets covered the period 2019, 2020, 2021, 2022 and 2023 and were analysed in Google Earth Engine. Results indicated that the yearly mean values in NO2 and LST in 2020 reduced by 2% and 12% respectively from 2019, while the mean NDVI value significantly increased by 28% in 2020 from 2019. NO2 has a negative correlation with LST in all years and a positive correlation with NDVI. Pearson correlation with population densities in constituencies in Nairobi in 2019 and 2023 indicate a negative correlation with NDVI and a positive correlation with NO2 and LST. Constituencies that have higher population densities tend to have lower vegetation densities and higher NO2 concentrations and temperature. Vegetation therefore plays a crucial role in air quality and that climatic factors such as precipitation and temperature influence the concentration of pollutants.

Desert landscape features influencing the microgeographic genetic structure of Nelson's pocket mouse Chaetodipus nelsoni.

Elucidating the factors that drive the genetic patterns of natural populations is key in evolutionary biology, ecology and conservation. Hence, it is crucial to understand the role that environmental features play in species genetic diversity and structure. Landscape genetics measures functional connectivity and evaluates the effects of landscape composition, configuration, and heterogeneity on microevolutionary processes. Deserts constitute one of the world's most widespread biomes and exhibit a striking heterogeneity of microhabitats, yet few landscape genetics studies have been performed with rodents in deserts. We evaluated the relationship between landscape and functional connectivity, at a microgeographic scale, of the Nelson's pocket mouse Chaetodipus nelsoni in the Mapimí Biosphere Reserve (Chihuahuan desert). We used single-nucleotide polymorphisms and characterized the landscape based on on-site environmental data and from Landsat satellite images. We identified two distinct genetic clusters shaped by elevation, vegetation and soil. High elevation group showed higher connectivity in the elevated zones (1250-1350 m), with scarce vegetation and predominantly rocky soils; whereas that of Low elevation group was at <1200 m, with denser vegetation and sandy soils. These genetic patterns are likely associated with the species' locomotion type, feeding strategy and building of burrows. Interestingly, we also identified morphological differences, where hind foot size was significantly smaller in individuals from High elevation compared to Low elevation, suggesting the possibility of ecomorphs associated with habitat differences and potential local adaptation processes, which should be explored further. These findings improve our understanding of the genetics and ecology of C. nelsoni and other desert rodents.

An improved instantaneous gross primary productivity model considering the difference in contributions of sunlit and shaded leaves to canopy sun-induced chlorophyll fluorescence

Sun-induced chlorophyll fluorescence (SIF) from plants offers an effective proxy for estimating gross primary productivity (GPP) by modeling SIF-GPP relationships, a widely used method to evaluate the global carbon sink. However, most SIF-GPP models ignore SIF differences between shaded and sunlit leaves, resulting in GPP underestimation, particularly in dense vegetation. This study aims to partition the contributions of sunlit and shaded leaves to canopy SIF and GPP to refine the SIF-GPP estimation model. Data from 40 eddy covariance (EC) sites representing eight major biomes and TROPOMI SIF satellite data were used for site-specific and global-scale analyses. Our results showed that the contributions of sunlit and shaded leaves to canopy SIF were 80 % and 20 %, and to canopy GPP were 55 % and 45 %, respectively. For site-specific or satellite data, the SIF-GPP relationships were the strongest for sunlit leaves (R2 > 0.51, RMSE = 4.03 μmol m−2 s−1, p < 0.001). The new SIF-GPP model, including sunlit-shaded SIF separation, can improve the accuracy of GPP estimation (R2 = 0.53, RMSE = 4.38 μmol m−2 s−1, p < 0.001). Compared with the model established with observed data, R2 was increased by 0.1, and RMSE decreased by 13.26 μmol m−2 s−1, indicating that the ‘two-leaf’ model could notably improve the SIF-GPP model. This study confirms the different contributions of sunlit and shaded leaves to canopy SIF and GPP, and ignoring this disparity would induce systematic bias in GPP estimation. Our methods and findings on sunlit-shaded SIF separation can be referenced by other studies to enhance GPP estimation accuracy.

GPS-free autonomous navigation in cluttered tree rows with deep semantic segmentation

Segmentation-based autonomous navigation has recently been presented as an appealing approach to guiding robotic platforms through crop rows without requiring perfect GPS localization. Nevertheless, current techniques are restricted to situations where the distinct separation between the plants and the sky allows for the identification of the row’s center. However, tall, dense vegetation, such as high tree rows and orchards, is the primary cause of GPS signal blockage. In this study, we increase the overall robustness and adaptability of the control algorithm by extending the segmentation-based robotic guiding to those cases where canopies and branches occlude the sky and prevent the utilization of GPS and earlier approaches. An efficient Deep Neural Network architecture has been used to address semantic segmentation, performing the training with synthetic data only. Numerous vineyards and tree fields have undergone extensive testing in both simulation and real world to show the solution’s competitive benefits. The system achieved unseen results in orchards, with a Mean Average Error smaller than 9% of the maximum width of each row, improving state-of-the-art algorithms by disclosing new scenarios such as close canopy crops. The official code can be found at: https://github.com/PIC4SeR/SegMinNavigation.git.

Translating mineral systems criteria into a prospectivity model for IOCG deposits in the Kolari region, Finland

The mineral system approach facilitates mapping the fingerprints of geological processes in ore formation using mineral prospectivity modeling (MPM). It aims to reduce the time and cost of exploration as a priority in the mineral exploration industry. Finland, which is covered with thick soil, dense vegetation, and snow in winter and thus has limited outcrops, serves as a suitable testing ground for this method. In this contribution, MPM was employed to predict favorable targets for Fe-Cu-Au (IOCG) deposits in the Kolari region, northwestern Finland. The mineral system components ore metal and sulfur sources, pathways, energy sources/drivers, traps, and the geological factors influencing the ore-forming processes were translated into mappable criteria and were considered for a regional-scale analysis. Knowledge-driven, fuzzy logic overlay, fuzzy inference system, and geometric average methods integrate evidential layers derived from geological (scale-free geology map), geochemical (till and bedrock sample geochemistry), and geophysical data (magnetic, radiometric, electromagnetic measurements, and gravity worms). Subsequently, the data were modeled using a combined conceptual/empirical approach, i.e. fuzzified evidential layers and logistic regression. In the prospectivity models, existing IOCG deposits are consistently placed within high-favorability areas. In addition, new exploration targets were identified. The models were validated against known IOCG deposits using Receiver Operating Characteristic (ROC) analysis and average Area Under the Curve (AUC) with values of final prospectivity models consistently reaching scores > 0.8. This indicates a favorable outcome of the application of MPM, making the approach applicable to other regions and ore deposit types. A majority voting ensemble technique was employed to combine the favorable areas of each prospectivity model. Then a confidence index was adopted reducing uncertainty linked to the models generated. These outputs consistently facilitated the identification of exploration targets more reliably.

Modeling of non-equilibrium suspended sediment transport process in open channel flows with vegetation

A mathematical model based on advection-diffusion theory is established to study the non-equilibrium sediment transport process in vegetated channels. The effects of vegetation on velocity distribution and sediment diffusion coefficients were considered, respectively. Validation against experimental data from flume studies confirms the model's ability to accurately predict the longitudinal sediment deposition rate and the vertical distribution of suspended sediment concentration (SSC). A comparative analysis of three sediment diffusion coefficient formulations indicates that the linear-exponential formula provides a more precise estimate of εsz, and the linear-exponential formula performs well in predicting the turbulent diffusion coefficients of both rigid and flexible vegetation when gently swaying. Moreover, the distance required for SSC to regain equilibrium is influenced by the submergence level of the vegetation canopy. At lower submergence levels, the canopy shear vortices significantly affect the vertical exchange of sediment, and the sediment diffusion coefficients exhibit pronounced stratification near the vegetation canopy. An increase in vegetation density at these lower submergence levels intensifies the shear vortices, thereby extending the distance needed for SSC to reach equilibrium. At higher submergence levels, the impact of canopy shear vortices is lessened, which reduces sediment diffusion coefficient stratification characteristics, and the flow is similar to rough boundary layer flow. An increase in vegetation density increases flow resistance, which shortens the distance required for SSC to attain equilibrium. However, further efforts are required to explore turbulent characteristics with highly flexible vegetation motion and the grain size distribution of non-uniform sediments in vegetated flows.

Integrating Public Perception and Quantitative Metrics for Improved Urban Park Planning in Isfahan, Iran

This study delves into the intricate realm of public perception regarding urban parks, aiming to uncover key factors influencing this perception and offer insights for urban planning and park management in Isfahan City, Central Iran. A comprehensive survey was conducted across 63 urban parks, gathering subjective feedback from park visitors on park size, vegetation, and exposure to external disturbances. Objective metrics were the area of the park, the mean and stdev of the Sentinel-2 NDVI as measures of vegetation density and diversity, respectively, distance to other parks and the park perimeter-to-area ratio. Linear and non-linear regression analysis between subjective feedback and objective metrics revealed that satisfaction peaks in parks with sizes of 15 ha or more. Higher vegetation density maximizes satisfaction. Parks with simpler, more regular shapes tend to offer a greater sense of freedom from external disruptions. According to the Generalized Additive Model model (R2= 0.737, explained deviance = 77.1%), lush and healthy vegetation is often associated with attractive and inviting parks, especially in parks with simpler, more regular shapes that minimize the exposure to (noise and visual) disturbances. Such green and circular park designs can guide urban planners and managers in creating inviting and user-friendly parks, aligning with public expectations.

Carbon Sequestration Assessment Using Satellite Data and GIS at Chiang Mai Rajabhat University

This study conducted a project to assess carbon sequestration in the forest area of Chiang Mai Rajabhat University, Mae Rim Campus, covering a total area of approximately 5,600 rai, with about 75% consisting of dry dipterocarp forest. The Sentinel-2 satellite data from 2019 to 2023 were used to analyze and classify forest density using the Normalized Difference Vegetation Index (NDVI). It was classified into four NDVI levels: highest, high, moderate, and low. Then, eight sample plots were distributed across all density levels to collect field data on tree species, number of trees, height, and diameter. The biomass and carbon sequestration in the sample plots showed a strong correlation with vegetation density, with the highest average correlation in February, particularly on February 13, 2023, showing the highest correlation coefficient of 0.817. This relationship is described by the equation y=78.601x‒25.726, indicating that this model is effective for estimating carbon sequestration. The analysis revealed that the area with the highest NDVI level of dry dipterocarp forest had the highest above-ground carbon sequestration rate of 16.25 tons per rai, whereas the forest with the lowest NDVI level had an above-ground carbon sequestration rate of 0.21 tons per rai. In total, the above-ground carbon sequestration for the trees amounted to 50,907.35 tons. This preliminary assessment serves as a promising foundation for future efforts the conservation and restoration of the university’s forest area, contributing to sustainable strategies for mitigating global warming.

Dense aquatic vegetation can reduce parasite transmission to amphibians

Submerged aquatic vegetation (macrophytes) can provide prey with refuges from predators and may perform a similar role for interactions with other natural enemies such as parasites. This could occur by interfering with the ability of free-swimming infectious parasite stages to locate or move towards hosts, reducing infections. Alternatively, infections may increase if macrophytes reduce host anti-parasite behaviours such as detection or evasion. Both scenarios could be affected by macrophyte density and structural complexity. Here we investigated whether experimental infection of tadpoles (Rana sylvatica and Rana pipiens) by parasitic flatworms (the trematodes Ribeiroia ondatrae and Echinostoma spp. was affected by the presence of artificial vegetation with varying density and complexity (simple versus branching), as well as tadpole activity under these conditions. Macrophyte presence significantly reduced tadpole infection loads only in the highest density treatment, but there was no effect of structural complexity. Related to this, tadpoles spent significantly more time near aquatic vegetation when it was dense but showed no preference for either structural type. Our results indicate that aquatic vegetation can reduce parasite transmission in certain scenarios, with further studies needed to explore how structural complexity in natural systems can affect host-parasite interactions, considering the massive physical alterations possible through eutrophication and the introduction of invasive plant species.

Influence of vegetative cover on snowpack mercury speciation and stocks in the greening Canadian Subarctic region.

A notable greening and warming of the Arctic and Subarctic due to climate change has uncertain implications for the global cycling of mercury (Hg). Snowpacks are dynamic reservoirs for Hg susceptible to solar radiation and wind pumping, with vegetative cover potentially altering Hg photochemistry. However, the impact of northern greening on the transformation of major Hg species and on Hg stocks remain poorly understood. Temporal surface snow and snowpit sampling was conducted under tree canopies and open tundra sites at the boreal-tundra ecotone in Nunavik, Canada. Maximum (mean) concentrations of 69.1 ng/L (8.8 ng/L) total mercury (HgT) and 46.9 ng/L (5.5 ng/L) reactive mercury (HgR) were measured in forest surface snow, with maximums attributed to rapid atmospheric oxidation events. Significant post-depositional reductions were recorded in the bay, tundra, and forest (67-99% HgR) and suggested greater Hg sequestration may occur under tree canopies. Increasing methylmercury (MeHg), HgT, and dissolved organic carbon (DOC) concentrations were detected across a vegetation gradient shifting towards humic-like organic matter. Notably, springtime depth profiles presented an approximate 12-fold greater accumulation of HgT under tree canopies compared to open tundra (p<0.01), with up to 16-times higher stocks (HgT, MeHg, DOC) at elevated vegetation density (p<0.05). In the North, increasing vegetation cover and surface warming may favor Hg accumulation and methylation in snowpacks, facilitated by interactions with organic matter, and further enriched by the reduced wind and solar exposure experienced under forest canopies.

Global analysis of acoustic frequency characteristics in birds.

Animal communication plays a crucial role in biology, yet the wide variability in vocalizations is not fully understood. Previous studies in birds have been limited in taxonomic and analytical breadth. Here, we analyse an extensive dataset of >140 000 recordings of vocalizations from 8450 bird species, representing nearly every avian order and family, under a structural causal model framework, to explore the influence of eco-evolutionary traits on acoustic frequency characteristics. We find that body mass, beak size, habitat associations and geography influence acoustic frequency characteristics, with varying degrees of interaction with song acquisition type. We find no evidence for the influence of vegetation density, sexual dimorphism, range size and competition on our measures of acoustic frequency characteristics. Our results, built on decades of researchers' empirical observations collected across the globe, provide a new breadth of evidence about how eco-evolutionary processes shape bird communication.

ECOLOGICAL NICHES AND IMPLICATIONS FOR REFORESTATION OF PTEROCARPUS ERINACEUS, KHAYA SENEGALENSIS AND ISOBERLINIA DOKA IN THE CLASSIFIED FORESTS OF NORTHERN COTE DIVOIRE

Deforestation in Cote dIvoire, particularly in the Pale and Pouniakele classified forests, threatens biodiversity and essential ecosystem services. This study explores the potential of Pterocarpus erinaceus, Khaya senegalensis, and Isoberliniadoka to restore degraded ecosystems through reforestation projects, using maximum entropy modeling. The objective is to analyze the ecological niches of these species in order to optimize reforestation strategies. The results show that Pterocarpus erinaceus has a high tolerance to climate variations, with an average AUC of 0.878, and a concentrated distribution in the central and northern savannahs. The main factors influencing its presence are the standard deviation of temperatures (47%) and humidity (15.4%). Khaya senegalensis, on the other hand, is well adapted to open environments with low vegetation cover and high climate variability. Its mean AUC of 0.893 indicates a robust prediction, with areas of presence in the central-eastern part of the country. Major contributions include temperature standard deviation (21.4%) and NDVI (18.5%). Finally, Isoberliniadoka shows greater sensitivity to stable climatic conditions, with a mean AUC of 0.848 and a presence limited to northern regions, where forest cover (32.1%) and mean temperature (39.7%) are the dominant variables. Ecological differences are observed between species, mainly influenced by climate variability and vegetation density. This implies the importance of adapting reforestation programs to the environmental specificities of each species. This study offers practical recommendations to improve the sustainability of reforestation projects in Cote dIvoire, taking into account local ecological challenges.