Biodiversity Inventorying and Monitoring, Conservation and Training

BackgroundOceanic islands are globally recognised for their exceptional levels of biodiversity and endemism, often resulting from unique evolutionary processes in isolated environments. However, this biodiversity is also disproportionately threatened by anthropogenic pressures including habitat loss, invasive species and climate change. Targeted, long-term biodiversity monitoring is essential for detecting changes in these vulnerable ecosystems and providing information for conservation strategies.The EU BIODIVERSA + project BioMonI aims at building a global long-term monitoring network specifically tailored to the pressing needs of biodiversity conservation and monitoring on islands. In BioMonI, we use a novel approach that considers mapping previous and current monitoring schemes on islands, developing a harmonised monitoring scheme for island biodiversity and mobilising existing monitoring data. We are assembling data from BioMonI-Plot, a long-term vegetation plot network to understand biodiversity and ecosystem change. It will use baseline data from three focal archipelagos (Azores, Canary Islands and Mascarenes), but we aim to mobilise data from archipelagos worldwide.Plot-based data are a cornerstone of effective biodiversity monitoring on islands. These standardised data collections within permanent plots allow for consistent, replicable observations across temporal and spatial scales. Initiatives like the Global Island Monitoring Scheme (GIMS) highlight the value of permanent plots in capturing ecological gradients and anthropogenic disturbance patterns. Such data underpin the detection of subtle shifts in community composition, functional diversity and species distributions, which are critical for assessing the effectiveness of conservation actions and predicting future ecological scenarios.In summary, plot-based data are indispensable for targeted and effective biodiversity monitoring on islands. They provide the empirical backbone necessary to provide information for adaptive management strategies and contribute to global biodiversity targets.New informationThe BioMonI-Plot baseline data consist of 10 plots in each of the following islands: Terceira (Azores), Tenerife (Canaries) and Réunion Island (Mascarenes). As a first step, we describe the diversity and abundance of all woody species shoots with a diameter at breast height (DBH) ≥ 1 cm in each of the 10 plots of each Island. The majority of taxa belonged to the phylum Magnoliophyta, which accounted for 96.66% of the total species and subspecies, followed by Pteridophyta (2.22%) and Pinophyta (1.11%). Réunion Island exhibited the highest species richness, with 66 identified taxa, followed by Tenerife (16 taxa) and Terceira (11 taxa). Only one species, Morellafaya, was shared between the islands, occurring in both Terceira and Tenerife. Most of the recorded species were classified as endemic according to their colonisation status. Specifically, 32 species were endemic to the Mascarene Islands, 22 to Réunion, nine to the Azores, eleven to Macaronesia and four to the Canary Islands.The data presented in this Data Paper provide a valuable proxy for evaluating the ecological integrity and overall habitat quality of native montane forests across three oceanic archipelagos: the Azores, Canary Islands and Mascarene Islands. By focusing on tree species as primary ecological indicators, the dataset offers insights into essential structural and compositional attributes of these ecosystems, including species richness, relative abundance and patterns of dominance.The comprehensive species-level information contained in this dataset allows for comparisons of forest composition across islands and biogeographic regions, contributing to our understanding of insular forest dynamics, endemism patterns and conservation priorities in tropical and subtropical montane environments.

Tropical forests host at least two-thirds of the world’s flora and fauna diversity and store 25% of the terrestrial above and belowground carbon. However, biodiversity decline due to deforestation and forest degradation of tropical forest is increasing at an alarming rate. Biodiversity dynamics due to natural and anthropogenic disturbances are mainly monitored using established field survey approaches. However, such approaches appear to fall short at addressing complex disturbance factors and responses. We argue that the integration of state-of-the-art monitoring approaches can improve the detection of subtle biodiversity disturbances and responses in changing tropical forests, which are often data-poor. We assess the state-of-the-art technologies used to monitor biodiversity dynamics of changing tropical forests, and how their potential integration can increase the detail and accuracy of biodiversity monitoring. Moreover, the relevance of these biodiversity monitoring techniques in support of the UNCBD Aichi targets was explored using the Essential Biodiversity Variables (EBVs) as a framework. Our review indicates that although established field surveys were generally the dominant monitoring systems employed, the temporal trend of monitoring approaches indicates the increasing application of remote sensing and in -situ sensors in detecting disturbances related to agricultural activities, logging, hunting and infrastructure. The relevance of new technologies (i.e., remote sensing, in situ sensors, and DNA barcoding) in operationalising EBVs (especially towards the ecosystem structure, ecosystem function, and species population classes) and the Aichi targets has been assessed. Remote sensing application is limited for EBV classes such as genetic composition and species traits but was found most suitable for ecosystem structure class. The complementarity of remote sensing and emerging technologies were shown in relation to EBV candidates such as species distribution, net primary productivity, and habitat structure. We also developed a framework based on the primary biodiversity attributes, which indicated the potential of integration between monitoring approaches. In situ sensors are suitable to help measure biodiversity composition, while approaches based on remote sensing are powerful for addressing structural and functional biodiversity attributes. We conclude that, synergy between the recent biodiversity monitoring approaches is important and possible. However, testing the suitability of monitoring methods across scales, integrating heterogeneous monitoring technologies, setting up metadata standards, and making interpolation and/or extrapolation from observation at different scales is still required to design a robust biodiversity monitoring system that can contribute to effective conservation measures.

Classification of species at the individual tree level would be beneficial to many applications including forest landscape visualization, forest management, and biodiversity monitoring. This article develops a patch-based classification algorithm of individual tree species based on convolutional neural network. The individual trees are first detected using the local maximum method from the canopy height model, as derived from light detection and ranging (LiDAR) data. The detected individual trees are then cropped into patches for classification based on the tree apexes, and three spatial scale image patches are chosen for analysis and discussion. A modified ResNet50 deep network is further employed for the cropped individual tree patches classification. The patch-based method accounts for the contexture information of a tree and does not require the feature selection or the feature reduction processes. About 1388 training samples including Ficus microcarpa Linn. f., Delonix regia, Chorisia speciosa A.St.-Hil., Dimocarpus longan Lour., Musa nana Lour., Carica papaya, and Others (the other tree species except the above six) were collected from both field work and visual interpretation. Aerial images, LiDAR data, and Worldview images were used for the tree species classification. For 362 test tree samples, the results of patch size 64 achieve the best accuracies, and the proposed method outperforms the traditional machine learning method with the overall accuracy of 89.06% + 0.58% using aerial images only. Transferability Study to the Luhu Park also indicated the feasibility of our method. While challenges in individual tree detection and multisource data fusion remain, the solution shows the potential in characterizing tree species at the individual tree level using remote sensing data.

Airborne remote sensing has an important role to play in mapping and monitoring biodiversity over large spatial scales. Techniques for applying this technology to biodiversity mapping have focused on remote species identification of individual crowns; however, this requires collection of a large number of crowns to train a classifier, which may limit the usefulness of this approach in many study regions. Based on the premise that the spectral variation among sites is related to their ecological dissimilarity, we asked whether it is possible to estimate the beta diversity, or turnover in species composition, among sites without the use of training data. We evaluated alternative methods using simulated communities constructed from the spectra of field-identified tree and shrub crowns from an African savanna. A method based on the k-means clustering of crown spectra produced beta diversity estimates (measured as Bray-Curtis dissimilarity) among sites with an average pairwise correlation of ~0.5 with the true beta diversity, compared to an average correlation of ~0.8 obtained by a supervised species classification approach. When applied to savanna landscapes, the unsupervised clustering method produced beta diversity estimates similar to those obtained from supervised classification. The unsupervised method proposed here can be used to estimate the spatial structure of species turnover in a landscape when training data (e.g., tree crowns) are unavailable, providing top-down information for science, conservation and ecosystem management applications.

The goal of the study that was conducted in the High School of Economics and Trade in Zaječar was to establish whether and to what extent teacher training courses on work in a digital environment contribute to a change in a teacher’s attitude to the digitalization of education. Two online surveys (the Google questionnaire) were conducted, with 68 teachers participating. The first survey was conducted at the beginning of the coronavirus pandemic and the second one 6 months later. The study began with the hypothesis that a rather strong resistance to digitalization exhibited in the first survey would, after a series of training courses, grow weaker in intensity as more and more advantages are listed. The results of both surveys showed an equally negative attitude to digitalization, with the reasons of resistance being different – in the first survey, they include inexperience in work with digital tools, and in the second one they include the impossibility of making an objective evaluation of students’ achievements and increased workload. The conclusion is that in the digital model of education teachers recognize inherent weaknesses, for which reason it is viewed solely as an addition to the traditional form of teaching.

AJI training courses scheduled for November

THE TRAINING OF SHOE-MACHINISTS

Identifying the hierarchical spatial levels that show the greatest dissimilarities between communities and how these patterns are generated is essential to provide insights into the monitoring and protection of biodiversity. In this study, we additively partitioned diversity of macroinvertebrates into alpha, beta, and gamma diversity across multiple scales in typical and semi‐arid tropical estuaries. We also determined which components of the total beta diversity, in terms of species replacement or richness difference (presence‐absence data) and abundance difference (relative abundance data), had the greatest relative importance in structuring the composition of benthic macrofauna. In typical and semi‐arid tropical estuaries, a non‐random spatial pattern was observed in additive partitioning of diversity, with higher values of beta diversity obtained at the largest scales analyzed. When considering the presence‐absence data, in general there was no clear trend which components of beta diversity had greater relative importance in typical estuaries. In the semi‐arid tropical estuaries, the richness difference component showed greater relative importance in the rainy season, whereas the species replacement presented greater proportions in the dry season. When considering abundance data, in general the abundance difference component showed greater relative importance in typical and semi‐arid tropical estuaries in the two seasonal periods. Therefore, approaches based on the presence/absence and on the relative abundance of species provided complementary answers about the distribution patterns of benthic macroinvertebrate communities. We demonstrated that environmental filtering and dispersal limitation may affect the patterns of distribution of benthic macrofauna in estuaries located in regions with different climatic conditions.

Assessing plant diversity using remote sensing, including airborne imaging spectroscopy, shows promise for large‐scale biodiversity monitoring in landscape restoration and conservation. Enriching plantations with native trees is a key restoration strategy to enhance biodiversity and ecosystem functions in agricultural lands. In this study, we tested how well imaging spectroscopy characterizes plant diversity in 37 experimental plots of varying sizes and planted diversity levels in a biodiversity‐enriched oil palm plantation in Sumatra, Indonesia. Six years after establishing the plots, we acquired airborne imaging spectroscopy data comprising 160 spectral bands (400–1000 nm, at ~3.7 nm bandwidth) at 0.3 m spatial resolution. We calculated spectral diversity as the variance among image pixels and partitioned spectral diversity into alpha and beta diversity components. After controlling for differences in sampling area through rarefaction, we found no significant relationship between spectral and plant alpha diversity. Further, the relationships between the local contribution of spectral beta diversity and plant beta diversity revealed no significant trends. Spectral variability within plots was substantially higher than among plots (spectral alpha diversity ~82%–87%, spectral beta diversity ~11%–18%). These discrepancies are likely due to the structural dominance of oil palm crowns, which absorbed most of the light, while most of the plant diversity occurring below the oil palm canopy was not detectable by airborne spectroscopy. Our study highlights that remote sensing of plant diversity in ecosystems with strong vertical stratification and high understory diversity, such as agroforests, would benefit from combining data from passive with data from active sensors, such as LiDAR, to capture structural diversity.

Environmental DNA metabarcoding is increasingly implemented in biodiversity monitoring, including phytoplankton studies. Using 21 mock communities composed of seven unicellular diatom and dinoflagellate algae, assembled with different composition and abundance by controlling the number of cells, we tested the accuracy of an eDNA metabarcoding protocol in reconstructing patterns of alpha and beta diversity. This approach allowed us to directly evaluate both qualitative and quantitative metabarcoding estimates. Our results showed non-negligible rates (17–25%) of false negatives (i.e., failure to detect a taxon in a community where it was included), for three taxa. This led to a statistically significant underestimation of metabarcoding-derived alpha diversity (Wilcoxon p = 0.02), with the detected species richness being lower than expected (based on cell numbers) in 8/21 mock communities. Considering beta diversity, the correlation between metabarcoding-derived and expected community dissimilarities was significant but not strong (R2 = 0.41), indicating suboptimal accuracy of metabarcoding results. Average biovolume and rDNA gene copy number were estimated for the seven taxa, highlighting a potential, though not exhaustive, role of the latter in explaining the recorded biases. Our findings highlight the importance of mock communities for assessing the reliability of phytoplankton eDNA metabarcoding studies and identifying their limitations.

The Rodna Mountains National Park is a protected areas established in 1990 as a national park with 47.000 ha, being one of the biodiversity hot spot at Carpathian level. The Rodna Mountains National Park Administration implemented in the period 2004-2017 more than 26 projects in partnership with 35 institutions (universities, NGOs, museums, county councils, mayors, ministries, national and international agencies, administrations and custodians of protected areas etc.). The total budget accessed was 4.403.500 euros in partnership with other stakeholders through more than 15 funding sources. Over 7.450 volunteers were involved in the Rodna Mountains National Park in various activities, with priority being the inventory, mapping and monitoring of biodiversity. Most volunteers come from the surrounding localities of the Rodna Mountains and only a small part of the countryside. The good practice model developed and implemented by the Rodna Mountains National Park Administration is supported through various sources of funding and is a complex process whose results are appreciated at national and international level.

Abstract. Several species of Araucaria and Agathis (Araucariaceae) occur as canopy emergents in rain forests of the western pacific region, often representing major components of total stand biomass. New data from permanent forest plots (and other published work) for three species (Araucaria hunsteinii from New Guinea, A. laubenfelsii from New Caledonia, and Agathis australis from New Zealand) are used to test the validity of the temporal stand replacement model proposed by Ogden (1985) and Ogden & Stewart (1995) to explain the structural and compositional properties of New Zealand rain forests containing the conifer Agathis australis. Here we propose the model as a general one which explains the stand dynamics of rain forests with Araucariaceae across a range of sites and species in the western Pacific.Forest stands representing putative stages in the model were examined for changes through time in species recruitment, growth and survivorship, and stand richness, density and basal area. Support for the model was found on the basis of: 1. Evidence for a phase of massive conifer recruitment following landscape‐scale disturbances (e.g. by fire at the Huapai site, New Zealand for Agathis australis); 2. Increasing species richness of angiosperm trees in the pole stage of forest stand development (i.e. as the initial cohort of conifers reach tree size; >10 cm DBH); 3. A high turnover rate for angiosperms (<100 yr), and low turnover for conifers (≥ 100 yr) in the pole stage, but similar turnover rates for both components (50–100 yr) as forests enter the mature to senescent phase for the initial conifer cohort; 4. Very low rates of recruitment for conifers within mature stands, and projected forest compositions which show increasing dominance by angiosperm tree species; 5. A low probability of conifer recruitment in large canopy gaps created by conifer tree falls during the initial cohort senescent phase, which could produce a second generation low density stand in the absence of landscape scale disturbance; 6. Evidence that each of the three species examined required open canopy conditions (canopy openness > 10 %) for successful recruitment.The evidence presented here supports the temporal stand replacement model, but more long‐term supporting data are needed, especially for the phase immediately following landscape level disturbance.

Reliable and comparable estimates of biodiversity are the foundation for understanding ecological systems and informing policy and decision‐making, especially in an era of massive anthropogenic impacts on biodiversity. Environmental DNA (eDNA) metabarcoding is at the forefront of technological advances in biodiversity monitoring, and the last few years have seen major progress and solutions to technical challenges from the laboratory to bioinformatics. Water eDNA has been shown to allow the fast and efficient recovery of biodiversity signals, but the rapid pace of technological development has meant that some important principles regarding sampling design, which are well established in traditional biodiversity inventories, have been neglected. Using a spatially explicit river flow model, we illustrate how sampling must be adjusted to the size of the watercourse to increase the quality of the biodiversity signal recovered. We additionally investigate the effect of sampling parameters (volume, number of sites, sequencing depth) on detection probability in an empirical data set. Based on traditional sampling principles, we propose that aquatic eDNA sampling replication and volume must be scaled to match the organisms' and ecosystems' properties to provide reliable biodiversity estimates. We present a generalizable conceptual equation describing sampling features as a function of the size of the ecosystem monitored, the abundance of target organisms, and the properties of the sequencing procedure. The aim of this formalization is to enhance the standardization of critical steps in the design of biodiversity inventory studies using eDNA. More robust sampling standards will generate more comparable biodiversity data from eDNA, which is necessary for the method's long‐term plausibility and comparability.

Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data

Woody species diversity as predictor of vascular plant species diversity in forest ecosystems