Biodiversity Monitoring Programs Research Articles

Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring

Abstract. Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purpose, we developed a national Swiss soil spectral library (SSL; n=4374) in the mid-infrared (mid-IR), calibrating 16 properties from legacy measurements on soils from the Swiss Biodiversity Monitoring program (BDM; n=3778; 1094 sites) and the Swiss long-term Soil Monitoring Network (NABO; n=596; 71 sites). General models were trained with the interpretable rule-based learner CUBIST, testing combinations of {5,10,20,50, and 100} ensembles of rules (committees) and {2, 5, 7, and 9} nearest neighbors used for local averaging with repeated 10-fold cross-validation grouped by location. To evaluate the information in spectra to facilitate long-term soil monitoring at a plot level, we conducted 71 model transfers for the NABO sites to induce locally relevant information from the SSL, using the data-driven sample selection method RS-LOCAL. In total, 10 soil properties were estimated with discrimination capacity suitable for screening (R2≥0.72; ratio of performance to interquartile distance (RPIQ) ≥ 2.0), out of which total carbon (C), organic C (OC), total nitrogen (N), pH and clay showed accuracy eligible for accurate diagnostics (R2>0.8; RPIQ ≥ 3.0). CUBIST and the spectra estimated total C accurately with the root mean square error (RMSE) = 8.4 g kg−1 and the RPIQ = 4.3, while the measured range was 1–583 g kg−1 and OC with RMSE = 9.3 g kg−1 and RPIQ = 3.4 (measured range 0–583 g kg−1). Compared to the general statistical learning approach, the local transfer approach – using two respective training samples – on average reduced the RMSE of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar compared to validation samples, in terms of both their spectral input space and the measured values. This suggests that data-driven selection with RS-LOCAL leverages chemical diversity in composition rather than similarity. Our results suggest that mid-IR soil estimates were sufficiently accurate to support many soil applications that require a large volume of input data, such as precision agriculture, soil C accounting and monitoring and digital soil mapping. This SSL can be updated continuously, for example, with samples from deeper profiles and organic soils, so that the measurement of key soil properties becomes even more accurate and efficient in the near future.

Linking camera-trap data to taxonomy: Identifying photographs of morphologically similar chipmunks.

Remote cameras are a common method for surveying wildlife and recently have been promoted for implementing large‐scale regional biodiversity monitoring programs. The use of camera‐trap data depends on the correct identification of animals captured in the photographs, yet misidentification rates can be high, especially when morphologically similar species co‐occur, and this can lead to faulty inferences and hinder conservation efforts. Correct identification is dependent on diagnosable taxonomic characters, photograph quality, and the experience and training of the observer. However, keys rooted in taxonomy are rarely used for the identification of camera‐trap images and error rates are rarely assessed, even when morphologically similar species are present in the study area. We tested a method for ensuring high identification accuracy using two sympatric and morphologically similar chipmunk (Neotamias) species as a case study. We hypothesized that the identification accuracy would improve with use of the identification key and with observer training, resulting in higher levels of observer confidence and higher levels of agreement among observers. We developed an identification key and tested identification accuracy based on photographs of verified museum specimens. Our results supported predictions for each of these hypotheses. In addition, we validated the method in the field by comparing remote‐camera data with live‐trapping data. We recommend use of these methods to evaluate error rates and to exclude ambiguous records in camera‐trap datasets. We urge that ensuring correct and scientifically defensible species identifications is incumbent on researchers and should be incorporated into the camera‐trap workflow.

Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library

Abstract. Traditional laboratory methods for acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modeling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL; n = 4374) in the mid-infrared range includes soil samples from the Biodiversity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally resolved data from the Swiss Soil Monitoring Network (NABO). Given that less than 2 % of the samples in the SSL originate from organic soils, we aimed to develop both an efficient calibration sampling scheme and accurate modeling strategy to estimate the soil carbon (SC) contents of heterogeneous samples between 0 and 2 m depth from 26 locations within two drained peatland regions (School of Agricultural, Forest and Food Sciences (HAFL) data set; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of the SSL. We used partial least square regressions (PLSRs), together with five repetitions of a location-grouped, 10-fold cross-validation, to predict SC ranging from 1 % to 52 % in the local HAFL data set. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL combined with local samples (2), commonly referred to as spiking, and subsets of local and SSL samples optimized for the peatland target sites using the resampling local (RS-LOCAL) algorithm (3). Using local and RS-LOCAL calibrations with at least five local samples, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.93 to 0.97; bias = -0.07 to 1.65; root mean square error (RMSE) = 2.71 % to 3.89 % total carbon; ratio of performance to deviation (RPD) = 3.38 to 4.86; and ratio of performance to interquartile range (RPIQ) = 4.93 to 7.09). However, calibrations using RS-LOCAL only required five or 10 local samples for very accurate models (RMSE = 3.16 % and 2.71 % total carbon, respectively), while purely local calibrations required 50 samples for similarly accurate results (RMSE < 3 % total carbon). Of the three approaches, the entire SSL spiked with local samples for model calibration led to validations with the lowest performance in terms of R2, bias, RMSE, RPD and RPIQ. Hence, we show that a simple and comprehensible modeling approach, using RS-LOCAL together with a SSL, is an efficient and accurate strategy when using infrared spectroscopy. It decreases field and laboratory work, the bias of SSL spiking approaches and the uncertainty of local models. If adequately mined, the information in the SSL is sufficient to predict SC in new and independent study regions, even if the local soil characteristics are very different from the ones in the SSL. This will help to efficiently scale up the acquisition of quantitative soil information over space and time.

Negative effects of nitrogen deposition on Swiss butterflies.

Nitrogen (N) deposition from agriculture and combustion of fossil fuels is a major threat to plant diversity, but its effects on organisms at higher trophic levels are unclear. We investigated how N deposition may affect species richness and abundance (number of individuals per species) in butterflies. We reviewed the peer-reviewed literature on variables used to explain spatial variation in butterfly species richness and found that vegetation variables appeared to be as important as climate and habitat variables in explaining butterfly species richness. It thus seemed likely that increased N deposition could indirectly affect butterfly communities via its influence on plant communities. To test this prediction, we analyzed data from the Swiss biodiversity monitoring program for vascular plants and butterflies in 383 study sites of 1 km2 that are evenly distributed throughout Switzerland. The area has a modeled N deposition gradient of 2-44 kg N ha-1 year-1 . We used traditional linear models and structural equation models to infer the drivers of the spatial variation in butterfly species richness across Switzerland. High N deposition was consistently linked to low butterfly diversity, suggesting a net loss of butterfly diversity through increased N deposition. We hypothesize that at low elevations, N deposition may contribute to a reduction in butterfly species richness via microclimatic cooling due to increased plant biomass. At higher elevations, negative effects of N deposition on butterfly species richness may also be mediated by reduced plant species richness. In most butterfly species, abundance was negatively related to N deposition, but the strongest negative effects were found for species of conservation concern. We conclude that in addition to factors such as intensified agriculture, habitat fragmentation, and climate change, N deposition is likely to play a key role in negatively affecting butterfly diversity and abundance.

Optimising monitoring for trend detection after 16 years of woodland‐bird surveys

Abstract Long‐term biodiversity monitoring programs provide important information about species' trajectories and broader environmental change. Often constrained by funding and organisational capability and commitment, monitoring programs need to be optimised to maximise ecological and economic efficiencies, as part of sound adaptive management. The monitoring design requirements for detecting biodiversity trends, across assemblages of species with different traits, can be informed by historical datasets. Using data from a landscape‐scale (c. 2,500 km2) bird monitoring program encompassing 151 sites visited three times annually over 16 years, we used resampling to simulate different monitoring designs. We quantified the capacity of modified monitoring regimes to detect population trends for 65 bird species with different densities, detectabilities and specialisations. The majority (58%) of species exhibited a significant decline in relative abundance, with the ability to detect trends proportional to the length of the time series used for analysis. The percentage of trends detected decreased as survey sites or sessions were dropped from the monitoring dataset. Statistically significant trends remained undetected for an additional 2.5 species for every 10% of sites excluded randomly from the program. As monitoring effort was reduced, the precision of trend estimates for rare species was particularly compromised. Conducting bird surveys every second year would produce better results than an equivalent reduction in effort achieved by surveying only half the sites each year, but could compromise the sustainability of the program. If the number of survey sites were reduced, trend detection would be optimised by retaining the spatial extent of the surveys (i.e. by dropping sites from well‐surveyed regions rather than excluding outlying, isolated sites), but the cost savings of this approach would be small. Synthesis and applications. Reduced monitoring effort will compromise trend detection for rare species or species that are difficult to observe, and declining species that will soon become rare. Consequently, monitoring effort that is considered ‘surplus’ today could provide critical data for detecting species‐level trends and prioritising conservation interventions in the future. Further, sampling efficiencies are not all‐important; we must also consider the impacts of survey design modification on the social and political sustainability of existing monitoring programs.

Model‐based integration of citizen science data from disparate sources increases the precision of bird population trends

AbstractAimTimely and accurate information on population trends is a prerequisite for effective biodiversity conservation. Structured biodiversity monitoring programmes have been shown to track population trends reliably, but require large financial and time investment. The data assembled in a large and growing number of online databases are less structured and suffer from bias, but the number of observations is much higher compared to structured monitoring programmes. Model‐based integration of data from these disparate sources could capitalize on their respective strengths.LocationGermany.MethodsAbundance data for 26 farmland bird species were gathered from the standardized Common Breeding Bird Survey (CBBS) and three online databases that varied with regard to their degree of survey standardization. Population trends were estimated with a benchmark model that included only CBBS data, and five Bayesian hierarchical models integrating all data sources in different combinations. Across models, we compared consistency and precision of the predicted population trends and the accuracy of the models. Bird species body mass, prevalence in the dataset and abundance were tested as potential predictors of the explored quantities.ResultsConsistency in predicted annual abundance indices was generally high especially when comparing the benchmark models to the integrated models without unstructured data. The accuracy of the estimated population changes was higher in the hierarchical models compared to the benchmark model but this was not related to data integration. Precision of the predicted population trends increased as more data sources were integrated.Main conclusionsModel‐based integration of data from different sources can lead to improved precision of bird population trend estimates. This opens up new opportunities for conservation managers to identify declining populations earlier. Integrating data from online databases could substantially increase sample size and thus allowing to derive trends for currently not well‐monitored species, especially at sub‐national scales.

Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics

DNA metabarcoding is an efficient tool to characterize invertebrate species composition in environmental samples. Most metabarcoding protocols for invertebrate bulk samples start with sample homogenization, followed by DNA extraction, amplification of a specific marker region and sequencing on a high-throughput sequencer. Many of the above-mentioned laboratory steps have been verified thoroughly and best practice strategies exist. Giving the amount of research done to validate almost all laboratory steps of metabarcoding workflows it is surprising that there is no clear recommendation for the basis of almost all metabarcoding studies: the homogenization of samples itself. For homogenization, different devices are used that can be divided into two major categories: bead mills and blenders. While bead-mills accelerate small, hard particles in a closed container or tube to break down specimens into small tissue fragments, blenders work with a rapidly rotating blades that reliably slice specimens as well as other substrate of the sample. Both methods are currently used in metabarcoding studies and have downsides to consider. Bead-mills rely on single-use plastics and therefore produce a lot of waste and are expensive. In addition to that, processing times can go up to 30 minutes making them unsuitable for large scale studies. Blenders on the other hand offer the opportunity to be cleaned and can handle larger sample volumes in shorter time, with an increased risk of cross-contamination. We here aimed to develop a fast, robust, cheap and reliable sample homogenization protocol that overcomes the above-mentioned limitations of both methods, i.e. does not produce difficult to discard waste and avoid single-use plastics while reducing overall costs. We tested the performance of the new protocol using six sorted Malaise trap insect samples and six unsorted stream macroinvertebrate kick-net samples. We used 14 technical replicates of each sample and many negative controls per sample (Fig. 1) to quantify impacts of i) insufficient homogenization and ii) possible sources of cross-contamination. Our results show that homogenization is sufficient after 3 minutes of homogenization. Rinsing the blender with water is sufficient in most cases but leads to low read-numbers in some of the negative controls. These could be further reduced by rinsing the blender with self-made drain-safe sterilization solution based on bleach but far less corrosive than pure bleach. Our results suggest that rinsing 1-2 times for 20 seconds is sufficient to avoid any cross-contamination. The improvements of the protocol in terms of speed, ease of handling, overall reduction of costs as well as the documented reliability and robustness make it an important candidate for sample homogenization after sampling in particular for large-scale and regulatory metabarcoding biodiversity assessments and monitoring programs.

Optimization and validation of a cost-effective protocol for biosurveillance of invasive alien species.

Environmental DNA (eDNA) metabarcoding has revolutionized biodiversity monitoring and invasive pest biosurveillance programs. The introduction of insect pests considered invasive alien species (IAS) into a non‐native range poses a threat to native plant health. The early detection of IAS can allow for prompt actions by regulating authorities, thereby mitigating their impacts. In the present study, we optimized and validated a fast and cost‐effective eDNA metabarcoding protocol for biosurveillance of IAS and characterization of insect and microorganism diversity. Forty‐eight traps were placed, following the CFIA's annual forest insect trapping survey, at four locations in southern Ontario that are high risk for forest IAS. We collected insects and eDNA samples using Lindgren funnel traps that contained a saturated salt (NaCl) solution in the collection jar. Using cytochrome c oxidase I (COI) as a molecular marker, a modified Illumina protocol effectively identified 2,535 Barcode Index Numbers (BINs). BINs were distributed among 57 Orders and 304 Families, with the vast majority being arthropods. Two IAS (Agrilus planipennis and Lymantria dispar) are regulated by the Canadian Food Inspection Agency (CFIA) as plant health pests, are known to occur in the study area, and were identified through eDNA in collected traps. Similarly, using 16S ribosomal RNA and nuclear ribosomal internal transcribed spacer (ITS), five bacterial and three fungal genera, which contain species of regulatory concern across several Canadian jurisdictions, were recovered from all sampling locations. Our study results reaffirm the effectiveness and importance of integrating eDNA metabarcoding as part of identification protocols in biosurveillance programs.

Periphytic algal flora of the lower Doce river basin after ore tailings flow, Espírito Santo State, Brazil: Sampling design and methods

ABSTRACT The present work aims to describe the sampling design and the methods used in the series “Periphytic algal flora of the Lower Doce River basin (Espírito Santo State, Brazil) after ore tailings flow”. This series will present the biodiversity of periphytic algae registered during the Aquatic Biodiversity Monitoring Program, Environmental Area I, carried out in the Lower Doce River basin three years after the passage of the ore tailings from Fundão dam (Mariana, Minas Gerais). Samples from the periphytic community were collected monthly, between October/2018 and February/2020, at five sampling stations in lotic environments and seven in lentic environments of the basin. These samples were fixed and the taxa will be identified and described, following specific methods. These taxa will be presented in sequential fascicles, split according to their taxonomic and morphological classification. More than 900 taxa have been recorded in this survey. This is the first taxonomic study about inland algae carried out in the Lower Doce River basin.

Global warning: challenges, threats and opportunities for ground beetles (Coleoptera: Carabidae) in high altitude habitats

Aim of this paper is to provide the first comprehensive synthesis about ground beetle (Coleoptera: Carabidae) distribution in high altitude habitats. Specifically, the attention is focused on the species assemblages living on the most common ice-related mountain landforms (glaciers, debris-covered glaciers, glacier forelands and rock glaciers) and the challenges, threats and opportunities carabids living in these habitats have to face concerning the ongoing climate warming. The suggested role of the ice-related alpine landforms, as present climatic refugia for cold-adapted ground beetles, is discussed. Finally, the needs to develop a large-scale High-alpine Biodiversity Monitoring Program to describe how the current climate change is shaping the distribution of high altitude specialists is highlighted.

How to get a national biodiversity monitoring programme off the ground: lessons from New Zealand

How to get a national biodiversity monitoring programme off the ground: lessons from New Zealand

Sponging up diversity: Evaluating metabarcoding performance for a taxonomically challenging phylum within a complex cryptobenthic community

AbstractDespite their ecological importance, sponges are often avoided in biodiversity studies and monitoring programs because they are notoriously difficult to identify using morphological or molecular methods. Here, we investigate the metabarcoding performance of universal degenerate cytochrome c oxidase subunit I (COI) primers in detecting species from this challenging phylum in a cryptobenthic community. Twenty‐two modified Autonomous Reef Monitoring Structures (ARMS) were deployed for 2 years in mesocosms receiving unfiltered seawater from an adjacent reef slope. Upon recovery, each unit was inspected by a marine sponge taxonomist who used a combination of taxonomy, imagery, and DNA barcoding (28S rRNA and COI) to identify sponges and generate a validated taxonomic richness value for each ARMS unit. A total of 69 unique sponge barcoded morphologies (BMs) were identified from the classes Calcarea, Demospongiae, and Homoscleromorpha. Metabarcoding identified 41 unique sponge molecular operational taxonomic units (MOTUs) from Demospongiae and Homoscleromorpha but the primers failed to amplify any species from the class Calcarea which comprised 22% of the BMs. Sponge richness did not differ between BMs and MOTUs assigned to the classes Demospongiae and Homoscleromorpha. However, assignments at the order and family level in Demospongiae underscore known limitations in sponge taxonomic resolution using the COI gene. The prevalence of false positives within the order Suberitida and the pervasiveness of false negatives within the order Haplosclerida highlighted both technical and biological constraints in the metabarcoding method. Overall, these results confirm the need for discretion in sponge MOTU assignments using universal degenerate barcoding primers that target a short fragment of the COI gene. However, they also demonstrate that COI metabarcoding is capable of capturing sponge richness from a complex natural community.

A New Long-Term Marine Biodiversity Monitoring Program for the Knowledge and Management in Marine Protected Areas of the Mexican Caribbean

In the Mexican Caribbean, 15 marine protected areas (MPAs) have been established for managing and protecting marine ecosystems. These MPAs receive high anthropogenic pressure from coastal development, tourism, and fishing, all in synergy with climate change. To contribute to the MPAs’ effectiveness, it is necessary to provide a long-term observation system of the condition of marine ecosystems and species. Our study proposes the establishment of a new marine biodiversity monitoring program (MBMP) focusing on three MPAs of the Mexican Caribbean. Five conservation objects (COs) were defined (coral reefs, seagrass beds, mangroves, marine turtles, and sharks-rays) for their ecological relevance and the pressures they are facing. Coral reef, seagrass and mangroves have multiple biological, biogeochemical and physical interactions. Marine turtles are listed as endangered species, and the status of their populations is unknown in the marine area of the MPAs. Elasmobranchs play a key role as top and medium predators, and their populations have been poorly studied. Indicators were proposed for monitoring each CO. As a technological innovation, all information obtained from the MBMP will be uploaded to the Coastal Marine Information and Analysis System (SIMAR), a public, user-friendly and interactive web platform that allows for automatic data management and processing.

The Unmeasured ecological effect of mosquito control

Intensive mosquito control programs are likely to contribute to insect diversity loss, but these effects are both underestimated and understudied. We recommend to conduct direct biodiversity monitoring programs to understand the effects of both chemical and biological control.

Diversity of diatoms, benthic macroinvertebrates, and fish varies in response to different environmental correlates in Arctic rivers across North America

Abstract Climate change poses a significant threat to Arctic freshwater biodiversity, but impacts depend upon the strength of organism response to climate‐related drivers. Currently, there is insufficient knowledge about Arctic freshwater biodiversity patterns to guide assessment, prediction, and management of biodiversity change. As part of the Circumpolar Biodiversity Monitoring Program's first freshwater assessment, we evaluated diversity of diatoms, benthic macroinvertebrates, and fish in North American Arctic rivers. Alpha diversity was assessed in relation to temperature, water chemistry, bedrock geology, and glaciation history to identify important environmental correlates. Biotic composition was compared among groups to evaluate response to environmental gradients. Macroinvertebrate α‐diversity declined strongly with increasing latitude from 48°N to 82°N, whereas diatom and fish diversity peaked around 70°N without a clear latitudinal decline. Macroinvertebrate diversity was significantly positively related to air temperature. Diatom diversity was related to bedrock geology and temperature, whereas fish diversity was related to glaciation history. Fish and macroinvertebrate assemblages differed between sites in western Canada, where invertebrate composition was more variable, and Alaska, where fish composition was more variable. In sites with both diatom and macroinvertebrate data, diatom composition was distinct in Alaska, where richness was highest in former glacial refugia. Macroinvertebrate composition was distinct in lowest‐latitude eastern and high‐latitude western Canadian sites where temperature was highest. Temperature, precipitation, geology, calcium, and substrate size were important environmental correlates for diatoms and macroinvertebrates, although the relative importance of each correlate differed. Diatom taxa were most strongly associated with water chemistry, whereas benthic invertebrate composition related most strongly to precipitation and temperature. This large‐scale study provides the most substantial integration and analysis of river diatom, macroinvertebrate, and fish data from the North American Arctic to date. Findings suggest that macroinvertebrates will show the strongest response to climate‐related shifts in temperature, whereas diatoms and fish are more likely to respond to climate‐induced shifts in nutrients and hydraulic connectivity. However, significant gaps in data coverage limited our ability to reliably evaluate spatial patterns and detect change. These gaps could be reduced by improving collaborative efforts between the U.S.A. and Canada to harmonise future monitoring.

Dispersal patterns of an introduced wild bee, Megachile sculpturalis Smith, 1853 (Hymenoptera: Megachilidae) in European alpine countries.

Biodiversity monitoring programs are the baseline of species abundancy studies, which in case of introduced species are especially critical. Megachile sculpturalis Smith, 1853 native to Eastern-Asia, constitutes the first ever recorded wild bee species accidently introduced in Europe. Since its first discovery in 2008, M. sculpturalis has been spreading across the continent. By initiating a citizen science monitoring program, we aimed to investigate the occurrence pattern of M. sculpturalis. Within only two years after starting the project, 111 new reports from Switzerland, Liechtenstein and Austria were recorded. Comparably to other European countries, the population progressed remarkably fast from year to year expanding its area geographically but also ecologically by increasing its altitudinal range. The distribution pattern indicates human assisted jump-dispersal travelling on the major traffic routes of central Europe.

The current state of DNA barcoding of macroalgae in the Mediterranean Sea: presently lacking but urgently required

AbstractThis review article explores the state of DNA barcoding of macroalgae in the Mediterranean Sea. Data from the Barcode of Life Data System (BOLD) were utilised in conjunction with a thorough bibliographic review. Our findings indicate that from around 1124 records of algae in the Mediterranean Sea, only 114 species have been barcoded. We thus conclude that there are insufficient macroalgal genetic data from the Mediterranean and that this area would greatly benefit from studies involving DNA barcoding. Such research would contribute to resolving numerous questions about macroalgal systematics in the area and address queries related to biogeography, especially those concerned with non-indigenous species. It could also possibly result in the development and application of better, cost-effective biodiversity monitoring programmes emanating from UN conventions and EU Directives. One possible way of achieving this is to construct DNA libraries via sequencing and barcoding, subsequently enabling better cost-effective biodiversity monitoring through environmental DNA metabarcoding.

Analysing the quality of Swiss National Forest Inventory measurements of woody species richness

BackgroundUnder ongoing climate and land-use change, biodiversity is continuously decreasing and monitoring biodiversity is becoming increasingly important. National Forest Inventory (NFI) programmes provide valuable time-series data on biodiversity and thus contribute to assessments of the state and trends in biodiversity, as well as ecosystem functioning. Data quality in this context is of paramount relevance, particularly for ensuring a meaningful interpretation of changes. The Swiss NFI revisits about 8%–10% of its sample plots regularly in repeat surveys to supervise the quality of fieldwork.MethodsWe analysed the relevance of observer bias with equivalence tests, examined data quality objectives defined by the Swiss NFI instructors, and calculated the pseudo-turnover (PT) of species composition, that is, the percentage of species not observed by both teams. Three attributes of woody species richness from the latest Swiss NFI cycles (3 and 4) were analysed: occurrence of small tree and shrub species (1) on the sample plot and (2) at the forest edge, and (3) main shrub and trees species in the upper storey.ResultsWe found equivalent results between regular and repeat surveys for all attributes. Data quality, however, was significantly below expectations in all cases, that is, as much as 20%–30% below the expected data quality limit of 70%–80% (proportion of observations that should not deviate from a predefined threshold). PT values were about 10%–20%, and the PT of two out of three attributes decreased significantly in NFI4. This type of uncertainty – typically caused by a mixture of overlooking and misidentifying species – should be considered carefully when interpreting change figures on species richness estimates from NFI data.ConclusionsOur results provide important information on the data quality achieved in Swiss NFIs in terms of the reproducibility of the collected data. The three applied approaches proved to be effective for evaluating the quality of plot-level species richness and composition data in forest inventories and other biodiversity monitoring programmes. As such, they could also be recommended for assessing the quality of biodiversity indices derived from monitoring data.

Environmental Impact Assessment: Possible Application of the Ecomorphic Approach

Environmental impact assessment (EIA) is a process by which environmental impact information is collected and precautions are determined. In many EIA countries, it is enshrined legislatively, but there is a common problem – in practice, many elements of the procedure are performed at a sub-standardized level. Scientists and practitioners note the problematic nature of the effectiveness of EIA procedures, which is primarily caused by the difficulties of turning assessments of biological diversity into clear management decisions. Biodiversity asses-sment and monitoring programs should consist of a list of protocols that ap-ply reliable methods of accounting for living organisms, taking into account spa-tial and time context. It is particularly noted that without “scientific rigor” and methodology of evaluation, the collected data may not matter for decision-making and represent wasted time and money. The effectiveness of EIA procedures can be improved by incorporating ecosystem services into the concept procedure, as it is a reflection of the functioning of ecosystems, which is a prerequisite for the socio-economic wellbeing of the population. One of the markers of ecosystem services is the ecomorphic structure of living cover. The investigation revealed the role of the ecomorphic structure of the soil macrofauna community as an indicator of the level of anthropogenic effects on the ecosystem. The results indicate that the ecomorphic characteristics of the soil macrofauna community are informative markers of the state of the ecosystem. The soil macrofauna community is divided into functional groups, which should be considered as a mechanism to maintain the stability of the ecosystem as a whole. This structuring has a place in both ecological and geographical space. Comparing grou-ping projections in both of these spaces provides an opportunity to interpret functional groups. It is important to note that both natural and anthropogenic factors structurally affect soil macrofaunf. An important aspect of anthropogenic influence is the inhibition of the endogeic block of the soil macrofauna community. Functionally, it is replaced by an epigeic macrofauna, which is represen-ted by extremely mobile species capable of recolonizing microsites with a temporary reduction in anthropogenic pressure. Thus, the ecomorphic structure of the soil macrofauna community has a high potential for application in the system of environmental impact assessment tools.

Taxonomic error rates affect interpretations of a national‐scale ground beetle monitoring program at National Ecological Observatory Network

AbstractParataxonomists are responsible for taxonomic identifications in large‐scale biodiversity monitoring programs. However, they may lack formal taxonomic training, and thus, quantifying error rate in identification is paramount for evaluating data quality of larger biomonitoring efforts. In large‐scale biomonitoring in particular, parataxonomist error rate could vary among regions with different species richness and composition. Here, we tested whether error rates in identification of ground beetles (Coleoptera: Carabidae) by parataxonomists increased in regions with greater species richness throughout the National Ecological Observatory Network (NEON), a national biomonitoring network spanning the United States. We compared identifications made by both parataxonomist and experts of 33,516 specimens collected between 2013 and 2017 from 18 ecoclimatic regions and analyzed error rates across ecoclimatic regions as a function of total richness identified by taxonomic experts. We then compared the additional level of taxonomic support that would be required to resolve identifications to species‐level identifications. We demonstrated the extent to which parataxonomist error rate can affect interpretation of common objectives of biomonitoring results, such as comparisons of species richness between ecoclimatic regions and capacity to identify target species of interest such as non‐indigenous species. Overall parataxonomist error rate was 11.1% and did not increase in regions with greater species richness. Expert taxonomists were required to resolve parataxonomist identifications to species in an additional 16% of specimens. With an average error rate of 11.1%, species richness estimates based on parataxonomists generally mirrored richness determined by experts. However, parataxonomist error rates as low as 5% were sufficient to misrepresent the gradient of species richness across ecoclimatic regions. Parataxonomist errors also led to false detection/missed detections of non‐indigenous species. As error rates were not influenced by increasing species richness, our study suggests that parataxonomists may be used consistently in large‐scale biomonitoring efforts to amplify the abilities of taxonomists by increasing the quantity and speed in which specimens are processed. However, our study also highlights that due to parataxonomist limitations, a subset of their materials must be regularly verified by professionals to ensure the quality of data collected.