Biology subjects

Roslin, T.

Publications and source records attributed to Roslin, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Malaise trap samples of 1000 individuals per week suggest 4 million insects per hectare in the boreal zone

A basic question in ecological research and biodiversity monitoring concerns the estimation of species abundances from trap catches. As a case in point, a Malaise trap can yield thousands of arthropod individuals, but how this count should be converted to numbers of individuals per unit area has remained an open question. Here, we supplement observational data with an experimental approach targeted at quantifying catchability. We released marked insects in a boreal forest and examined their capture rate by a grid of Malaise traps around the release location. We estimated insect movement rates, mortality rates, and Malaise trapping capture rates by fitting a joint species movement model to these data. As a methodological novelty, we show how to convert the movement model parameters to the expected number of captured individuals, given their actual population density. Our results show that multiplying the sample content by 30 000 yields a rough estimate of the number of individuals per hectare. This conversion factor depends on the species, generally decreasing with increasing body size. We apply the estimated conversion factors to conclude that typical boreal forest contains some four million insect individuals per hectare, out of which around half belong to Diptera. SIGNIFICANCE STATEMENTTraditionally, the Malaise trap method has been used for assessing the state of the local communities and to estimate population abundances. However, a topical question is: how does the number of individuals observed in a sample relate to the true density of individuals in the surrounding community? To answer this question, we implement a movement model parametrized by a carefully designed mark-recapture experiment, in which we are able to obtain taxon-specific conversion factors for different groups of insects. We found that different insect groups come with different conversion factors, causing a mismatch between sample contents and true community composition. Thus, treating the sample contents as a direct representation of the reference community will be misleading.

ecology↗

bioRxiv2026

Predicting global biodiversity via Hubbell regression

Understanding global biodiversity patterns and their drivers is a prerequisite for countering the biodiversity crisis. In this paper, we introduce a novel generalized linear model, Hubbell regression, to estimate a key biodiversity descriptor, the fundamental biodiversity number. This can be converted into a set of biodiversity descriptors, including Shannon and Simpson indices, and more. Hence, quantifying the impact of environmental conditions on the fundamental biodiversity number allows us to predict the general properties of local biodiversity in any setting. In addition to having a strong mathematical foundation, Hubbell regression consistently outperformed current state-of-the-art models in predicting global biodiversity. We apply the method to arthropods, which account for the majority of terrestrial biodiversity. By parameterizing the models using samples of 1.78 million arthropods from 2415 samples collected at 135 sites spanning all continents, we pinpoint the drivers of arthropod biodiversity and its features at the global scale. We find that actual evapotranspiration is the single largest predictor of arthropod diversity and explains nearly 30% of the variation in richness. Moreover, we infer that high human activity has led to a 21.3 % and 29.2% decrease in potential insect richness in tropical and dry zones, respectively, but increased insect richness in polar regions. These insights bring a new foundation for biodiversity research and action.

ecology↗

bioRxiv2026

Habitat context alters the pace of climate-driven community warming across terrestrial and freshwater ecosystems

As global temperatures rise, ecological communities are increasingly dominated by warm-affiliated species, a process known as community warming or thermophilisation. Yet, why different taxa exhibit different rates of community warming remains unclear. Habitat composition and structure are likely drivers of this variation, as the ecological consequences of warming are filtered by local environmental conditions. Using over 40 years of monitoring data spanning terrestrial (birds, insects, plants) and freshwater (phytoplankton) communities, we show that habitat structure determines how strongly communities track warming. Forest cover systematically slows thermophilisation by reducing communities sensitivity to temperature change, whereas habitat heterogeneity has weak and variable effects that differ among ecosystems. Together, these results demonstrate that uneven thermophilisation arises from habitat-mediated differences in how communities respond to a shared climatic signal. Incorporating these effects is essential for improving predictions of biodiversity change under ongoing climate warming.

ecology↗

bioRxiv2026

Inferences about phenological shifts in an Arctic community vary with time-windows

Long-term monitoring data have enabled detection of phenological change, yet it remains poorly understood how its temporal dimensions-- duration and choice of start and end years-- influence the inferences drawn. To examine which phenological signals emerge at different temporal scales, we analyzed the longest continuous dataset on high-arctic plant and arthropod phenology, collected from 1996 to 2024 in the Zackenberg valley, northeast Greenland. These data have been used to suggest both rapid advancement of spring in the High Arctic (2007) and little directional change but decadal regime shifts (2023). To reconcile these differing conclusions, we quantified how trend estimates varied across moving time-windows and determined the minimum time-series length required to achieve a high probability of agreement with long-term trends. We find that while trend directionality shifts with temporal windows, confidence in trend estimates increases with time-series length. Using the full time-series, we show dampened signals of warming trends, with annual increases in spring and summer air temperatures by 0.04 [-0.05, 0.13] and 0.05 [-0.01, 0.11] {degrees}C per year, respectively, alongside a 0.82% [-1.85, 0.19] decline in spring snow cover. We also see modest advancement in the seasonal activity of most arthropod taxa (by [~]0.1 days/year), whereas flowering phenology shows no consistent directional change. Shorter time-series revealed cyclical patterns in abiotic drivers yet variable biotic responses, indicating that a single pattern of "climate change" will translate into varied responses within communities. Finally, almost two decades of data were needed to reliably capture long-term trends. Ecologically, these suggest that 1) phenological shifts in the High Arctic are more moderate than early assessments implied and 2) reflect a dynamic balance between species life histories and ongoing climate variability. These may alter interaction potentials within communities, with consequences for ecosystem functioning.

ecology↗

bioRxiv2026

Accuracy of occurrence and abundance estimates from insect metabarcoding

1. DNA metabarcoding--high-throughput sequencing of barcode regions from bulk samples--has become a key tool for insect biodiversity assessment. Yet, how methodological choices affect the accuracy of metabarcoding data remains insufficiently explored. In this paper, we ask: (1) How does the lysis method (non-destructive lysis vs. destructive homogenization) affect community recovery? (2) How comprehensively does metabarcoding capture species richness? (3) To what extent can spike-ins improve abundance estimates? (4) How accurately can species abundances be estimated? 2. We evaluated the accuracy of insect metabarcoding using 4,749 bulk samples from a large-scale biodiversity survey subjected to mild lysis. Of these samples, 856 were also homogenized, allowing a systematic comparison of the effect of alternative treatments. To potentially improve abundance estimates, we added six biological spike-ins (i.e., foreign insects) to all samples, and two synthetic spike-ins (artificial DNA fragments) to the homogenization treatment. In addition, we established the contents of 15 samples by individually barcoding all specimens, enabling direct assessment of occurrence and abundance estimates. 3. Our results revealed consistent differences between destructive and non-destructive treatments. While both methods reliably detected the majority of species, small and soft-bodied taxa were more often recovered after mild lysis than after homogenization, while the reverse was true for heavily sclerotized, hairy, and large taxa. Using biological spike-ins for calibration reduced the variance in read numbers per specimen considerably, especially in homogenized samples, while synthetic spike-ins were less effective. In a Bayesian analysis, where species data were matched to the best-fitting spike-in calibration curve, accurate abundance estimates (+/-1 individual) were obtained for 72.9% of species occurrences. 4. Our results show that it is possible to obtain reasonably accurate abundance estimates from metabarcoding data, and that mild lysis and homogenization result in different taxon-specific biases in terms of occurrence data, with neither method outperforming the other. Accuracy is improved by homogenization rather than mild lysis of samples, and by the use of biological rather than synthetic spike-ins. Together, these findings provide a major step towards robust, quantitative biodiversity monitoring using DNA-metabarcoding.

zoology↗

bioRxiv2025

Is there a fly in my soup? To what extent do metabarcoding and individual barcoding tell the same story?

O_LIMetabarcoding has become the method of choice for characterising complex arthropod communities. The extent to which metabarcoded bulk samples will recover the same community composition as individual sequencing of all individuals in the sample remains poorly quantified. Biases such as unequal extraction of DNA from different taxa, primer mismatches and non-random PCR may cause the selective drop-out of species from metabarcoding data. At the same time, DNA metabarcoding may reveal arthropod taxa present not as individuals, but as DNA residues on the surface or in the gut of insects. C_LIO_LITo quantify the consistency in sample contents established by different means, we metabarcoded 45 bulk insect samples, then extracted all arthropods and sequenced them individually. Metabarcoding targeted 418 bp at the 3 end of the Folmer barcoding region, while individual barcodes captured the entire 658 bp Folmer region. The metabarcoding workflow, including PCR amplification, sequencing, and bioinformatics, was performed in three replicates from three separate lysate aliquots per sample. Sequences were assigned to Barcode Index Numbers (BINs) as identical taxonomic categories across data types. C_LIO_LIWe found that metabarcoding is replicable, as different replicates of the same sample recover similar species richness and composition. Individual barcoding and metabarcoding provide the same impression of relative differences in community structure: estimates of relative species richness and relative dissimilarity between sample pairs are congruent among data types. Dissimilarity between data types varies with BIN richness in the sample, but this relationship reflects nestedness rather than turnover: metabarcoding recovers the same set of core species as individual barcoding but adds hundreds of species on top. Any BIN recovered as an individual occurred with high probability in the metabarcoding data, and any BIN found in high read abundances by metabarcoding was likely found as an individual. C_LIO_LIOur analysis suggests that metabarcoding data will closely mimic the sample contents in terms of arthropod species richness and composition. Taxa recovered in low copy numbers in metabarcoding sequence data will typically represent DNA left as residues from past biotic interactions. Barring sequencing errors, both types of data yield biologically relevant insights into the taxa present in the source community. C_LI

ecology↗

bioRxiv2025

Experimental community ecology in decline: A call to embrace technology

Community dynamics are complex and thus challenging to infer from observational data alone. Experiments, with their ability to control variables and isolate mechanisms, are a powerful tool for uncovering the causal processes that drive community dynamics. They therefore allow us to move beyond correlations and to directly test theoretical predictions. Yet, because experiments are often logistically demanding and resource-intensive, they are less frequently employed than observational approaches in community ecology. Here, we trace the past three decades of experimental research in community ecology through a systematic literature review. We focus on the motivation behind experiments, their links to ecological theory, the types of questions they address, their scale, and the methods used to do this. Our results corroborate the historically tight relationship between experiments and ecological theory and document a gradual increase in experimental complexity --particularly related to the use of molecular methods. However, persistent gaps remain in the taxa and ecosystems studied, with aquatic ecosystems, fungi, and microbes still underrepresented compared to terrestrial plants and animals. Moreover, experiments are still limited in their spatial and temporal scale; they are typically short-term, local, and reliant on manual methods. The integration of high-throughput technologies with experimental workflows is still in its infancy, even though they are increasingly common in biomonitoring. To illustrate the potential of such tools in experimental research, we present a proof-of-concept study. It shows how automated technologies can be incorporated at different stages of the experimental workflow to expand the scale of experiments while reducing the reliance on human labor and potentially lowering financial costs. We conclude that many of the long-lasting biases and challenges in experimental community ecology could be addressed by combining technological innovations with broader collaboration among research groups. Coordinated networks, standardized protocols, and the integration of long-term and large-scale experimental designs can substantially improve in situ replication as well as cross-site comparability. Such efforts are essential for developing a more comprehensive mechanistic understanding of community dynamics across diverse ecosystems.

ecology↗

bioRxiv2025

Discovering the unseen: a performance comparison of taxonomic classification methods under unknown DNA barcodes

O_LIDNA barcoding and metabarcoding have emerged as cost-efficient, standardized methods for characterizing local biodiversity. Based on the sequencing of a small targeted gene fragment, it is theoretically possible to identify a wide diversity of taxa by comparing them with reference sequence databases. However, a key challenge for accurate taxonomic classification is the incompleteness of such databases, leading to most query sequences lacking species-level matches. C_LIO_LIWhere species-level matches are missing, it may be possible to classify query sequences to a higher taxonomic level, such as genus or family, based on the similarity of related reference taxa. The challenge then lies in confidently recognizing whether the sequence belongs to an unobserved (here, "novel") taxon on a given taxonomic level. C_LIO_LIIn this study, we evaluated the performance and utility of several methods for taxonomic classification. Methods were assessed based on the classification accuracy of both observed and novel taxa, training time, space requirements, and run time. We did this for two cases: the COI barcode for arthropods, and the ITS barcode for fungi, with the latter representing an instance with substantially greater sequence similarity variation within classes. To test classification of novel taxa, we used well-curated datasets with partially distinct taxonomic distribution between the training and test set. Novel taxa occurred at all evaluated taxonomic levels, such as novel species in observed genera and novel genera in observed families. We further assessed the effect on performance when shifting from full-length barcodes to shorter sequences as generated through metabarcoding in the testing dataset. C_LIO_LIThis study sheds light on the strengths and limitations of different classification algorithms across varied ecological contexts and provides guidance for researchers in selecting suitable algorithms for DNA barcoding and metabarcoding applications. In particular, it demonstrates the supreme performance of phylogenetic placement methods such as EPA-ng for classification of arthropod COI barcodes, and composition-based classifiers such as SINTAX, RDP-NBC, and IDTAXA for fungal ITS. C_LI

bioinformatics↗

bioRxiv2025

The environmental dependence of ecological interactionnetworks

Interactions between species are often assumed to occur whenever species pairs co-occur, allowing us to reconstruct local ecological networks from a deterministic "metaweb". Thus, a species set of metaweb interactions defines its Eltonian niche - the biotic conditions under which it can exist. However, both species and interactions may also have Grinnellian niches, reflecting relationships between probability of occurrence and environmental conditions. Over the past few years, we have explored how stochasticity in the occurrence of species and links, and environmental imprints on these, shape ecological interaction networks. In this perspective, we use a set of our empirically-characterised networks to show that both species and link occurrence are stochastic, with probabilities conditional on the environment. This insight improves our ability to model observed variation in network structures. We argue that future research should incorporate environmental impacts on both species and links, thus merging the Grinnellian and the Eltonian niche concept.

ecology↗

bioRxiv2024

HAPP: High-Accuracy Pipeline for Processing deep metabarcoding data

We introduce HAPP, a high-accuracy pipeline for processing deep metabarcoding data, leveraging data richness to enhance the signal-to-noise-ratio. Starting with denoised amplicon sequence variants, the pipeline consists of four steps: (1) additional chimera removal, using UCHIME and a strict sample-based approach; (2) taxonomic annotation, combining k-mer matching (SINTAX) to a reference library with phylogenetic placement (EPA-NG) on a reference tree; (3) OTU clustering using SWARM, an open-source algorithm with precision and recall comparable to RESL used in circumscribing BOLD BINs; and (4) noise filtering (NUMTs and sequencing errors), using a new algorithm introduced here, NEEAT, which combines "echo" signals across samples with detection of unusual evolutionary signatures among clusters with similar DNA sequences. HAPP computations are parallelized across taxa, making analyses tractable on very large datasets. The performance of HAPP was validated through extensive benchmarks, involving CO1 data from BOLD and Malaise trap data, demonstrating significant improvements over the state of the art.

bioinformatics↗

bioRxiv2024

Harmonising digitised herbarium data to enhance biodiversity knowledge: creating an updated checklist for the flora of Greenland.

International efforts to digitise herbarium specimens provide the building blocks for a global digital herbarium. However, taxonomic changes and errors can result in inconsistencies when amalgamating specimen metadata, that compromise the assignment of occurrence records to correct taxa, and the subsequent interpretation of patterns in biodiversity. We present a novel workflow to mass-curate digital specimens. By employing existing digital taxonomic backbones, we aggregate specimen names by their accepted name and flag remaining cases for manual review. We then validate names using site-specific floras, balancing automation with taxonomic expert-based curation. Applying our workflow to the vascular plants of Greenland, we harmonised 175,266 digitised herbarium specimens and observations from 92 data providers from the Global Biodiversity Information Facility (GBIF). The harmonised metacollection for the Greenland flora contains 780 plant species. Our workflow increases the number of species known from Greenland compared to other currently available species checklists and increases the mean number of occurrences per species by 42.6. Our workflow illustrates the integration required in order to create a global, universally accessible digital herbarium, and shows how previous obstacles to database curation can be overcome through a combination of automation and expert curation. From the specific perspective of the Greenland flora, our approach arrives at a new checklist of taxa, a new curated metacollection of occurrence data, and revised estimates of plant richness. The list of taxa and their prevalence allow a new basis for biodiversity assessment and conservation planning. Societal Impact StatementDigitising plant collections has allowed for data to be aggregated across multiple collections, forming a single harmonised resource of unprecedented scale. This resource is only accurate once the database names are assigned to one accepted name per species. We established a semi-automated workflow for processing plant name data, leveraging taxonomic backbones and employing taxonomic expertise at key stages. Applying our workflow to the flora of Greenland, we developed a curated checklist of 780 species, capturing greater species richness than previously published, while also curating 175,266 plant records. Our findings redefine our knowledge of Greenlandic plant diversity, while harmonising a vast digital collection for further research.

ecology↗

bioRxiv2024

High-throughput biodiversity surveying sheds new light on the brightest of insect taxa

Sampling of species-rich taxa followed by DNA metabarcoding is quickly becoming a popular high-throughput method for biodiversity inventories. Unfortunately, we know little about its accuracy and efficiency, as the results mostly pertain to poorly-known organism groups in underexplored environments or regions of the world. Here we ask what an extensive sampling effort based on Malaise trapping and metabarcoding can tell us about the lepidopteran fauna of Sweden - one of the best-understood insect taxa in one of the most-surveyed countries of the world. Specifically, we deployed 197 Malaise traps for a single year across Sweden in a systematic sampling design, then metabarcoded the resulting 4,749 bulk samples, and compared the results to existing data sources. We detected more than half (1,535) of the 2,990 lepidopteran species ever recorded as occurring in Sweden, and 323 species not reported during the sampling period by other data providers. Full-length barcoding of individual specimens confirmed three new species for the country and extensive range extensions for two species. It also corroborated eight genetically distinct COI variants that may represent new species to science, one of which has since been described. Most of the new records are for small and inconspicuous species and poorly surveyed regions, suggesting that they represent previously overlooked components of the fauna. Our findings, corroborated by independent metagenomic analyses, show that DNA metabarcoding can be a highly efficient and accurate method of biodiversity sampling, to the extent that it can generate significant new discoveries even for the most well-known of insect faunas.

zoology↗

bioRxiv2024

Opportunistic partner choice among arctic plants and root-associated fungi is driven by environmental conditions.

Interactions between plants and soil microbes play an important role in structuring plant communities. Yet, little is known about how fungal networks are structured on the one hand by fungal responses to their environment (including their host plant) and on the other by responses to each other. We quantified changes in plant-fungus networks along geographic and environmental gradients across the Arctic, assessing the degree to which plants and fungi showed preference for specific interaction partners and how specificity varies along environmental gradients. To this aim, we sampled roots of 12 widely distributed plant taxa: Saxifraga oppositifolia; Bistorta vivipara; Dryas spp.; Vaccinium tis-idaea; Vaccinium uliginosum; Vaccinium myrtillus; Empetrum nigrum; Betula nana; alix arctica; Salix polaris; Cassiope tetragona; and Silene acaulis. To quantify the pool of fungi from which plant roots may recruit association partners, we also sampled fungi in the surrounding soil. Identifying fungaI communities by DNA metabarcoding, we used Hierarchical Modelling of Species Communities (HMSC) to assess how fungal communities change along environmental gradients, and whether plants actively select their root-associated fungi from the pool of fungi present in the bulk soil. We found that although the fungal communities within the soil and rhizosphere share 85% of genera, their composition differs significantly from each other. The two community types show similar responses to the environment and taxa show low partner fidelity. Thus, the structure of fungal communities on plant rhizosphere is mainly driven by abiotic rather than biotic conditions. Overall, in comparison with null models, networks of plants and rhizosphere-associated fungi showed a distinctly non-random structure, responding strongly to pH and temperature gradients. Our findings suggest that the dynamics and structure of plant-root associated interactions might be severely altered by abiotic changes in the rapidly changing arctic environment. Open Research statementData are privately provided for peer review. The raw sequences for the soil and root samples generated during the current study will be available in the Sequence Read Archive repository, in the BioProject PRJNA1094865 upon acceptance. For review purposes, the code and datasets used for the analyses of this study are temporarily available in Figshare open access repository at https://figshare.com/s/1b074f1751682d3487cf upon acceptance.

ecology↗

bioRxiv2024

Cold and lonely: low-abundance microbiota and no core microbiome across a broad geographic sampling of a mosquito species in Greenland

Microbial symbionts are key players in insect ecology and evolution, but microbiota vary widely across and within host species. The microbiota of insect clades such as high-latitude mosquitoes remain understudied, despite their abundance and potential economic and medical relevance. Here, we resolve temporal and spatial variation in the abundance and diversity of bacteria and fungi associated with Ochlerotatus mosquitoes in Greenland. To this aim, we apply a comprehensive workflow combining the simultaneous characterization of host, bacterial, and fungal marker regions, contamination filtering, as well as bacterial quantification and joint species distribution modelling. We find that 573 mosquitoes collected in five regions of Greenland between 2009 and 2024 represented two species (O. nigripes and O. impiger). Their microbial communities were low-abundance, highly diverse, and dominated by environmentally versatile taxa, showing high individual-to-individual-rather than spatio-temporal variation, although taxa such as Pseudomonas and Serratia exhibited sex-specific associations. DNA from local vertebrates and potential vertebrate pathogens (e.g., Bartonella) was detected, likely reflecting blood meals. These findings suggest that Greenland Ochlerotatus mosquitoes do not rely on core microbiota, but engage in flexible associations with diverse, low-abundance microbial communities. They also point to mosquitoes as likely vectors of bacterial and fungal pathogens in the Arctic.

ecology↗

bioRxiv2024

A new tool in a toolbox: Addressing challenges in high-throughput microbiota surveys across diverse wild insects

With their significant effects on the biology of higher organisms, host-associated microbiota has attracted the research communitys attention. The rapid progress in sequencing techniques has greatly facilitated microbial community characterization. However, the most popular surveying technique, marker gene amplicon sequencing, has multiple caveats that are not often addressed satisfactorily, including the uncertainty about the identity of the surveyed wild-caught specimens, variable and sometimes very low abundance of microbes in some samples, or reagent- and cross-contamination. As a result, researchers often obtain incomplete, biased, and sometimes totally incorrect microbial community profiles. Here, we present a versatile, cost-effective, and high-throughput quantitative multi-target amplicon sequencing workflow for the characterization of host-associated microbial communities, combining laboratory and bioinformatic steps and addressing most of the known methodological issues. Optimized for the study of the microbiota of wild insects, it can be easily adapted for other sample types. Outputs include contamination-controlled data on the absolute abundance and identity of microbes present in insect samples, both at genotype- and OTU-level, as well as host barcodes alongside information on parasite infections. Using 1384 samples from Zackenberg Valley, NE Greenland, we demonstrate the potential of the workflow to study insect and symbiont diversity patterns across a large portion of a diverse natural community.

ecology↗

bioRxiv2024

Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods

Modern DNA-based biodiversity surveys result in massive-scale data, including up to millions of species - of which most are rare. Making the most of such data for inference and prediction requires modelling approaches that can relate species occurrences to environmental and spatial predictors, while incorporating information about their taxonomic or phylogenetic placement. Even if the scalability of joint species distribution models to large communities has greatly advanced, incorporating hundreds of thousands of species has not been feasible to date, leading to compromised analyses. Here we present a novel "common to rare transfer learning" approach (CORAL), based on borrowing information from the common species to enable statistically and computationally efficient modelling of both common and rare species. We illustrate that CORAL leads to much improved prediction and inference in the context of DNA metabarcoding data from Madagascar, comprising 255,188 arthropod species detected in 2874 samples.

ecology↗

bioRxiv2024

RESOLVING BIOLOGY'S DARK MATTER: SPECIES RICHNESS, SPATIOTEMPORAL DISTRIBUTION, AND COMMUNITY COMPOSITION OF A DARK TAXON

BackgroundZoologys dark matter comprises hyperdiverse, poorly known taxa that are numerically dominant but largely unstudied, even in temperate regions where charismatic taxa are well understood. It is everywhere, but high diversity, abundance, and small size have historically stymied its study. We demonstrate how entomological dark matter can be elucidated using high-throughput DNA barcoding ("megabarcoding"). We reveal the high abundance and diversity of scuttle flies (Diptera: Phoridae) in Sweden using 31,800 specimens from 37 sites across four seasonal periods. We investigate the number of scuttle fly species in Sweden and the environmental factors driving community changes across time and space. ResultsSwedish scuttle fly diversity is much higher than previously known, with 549 mOTUs (putative species) detected, compared to 374 previously recorded species. Hierarchical Modelling of Species Communities reveals that scuttle fly communities are highly structured by latitude and strongly driven by climatic factors. Large dissimilarities between sites and seasons are driven by turnover rather than nestedness. Climate changes are predicted to significantly affect the 47% of species that show significant responses to mean annual temperature. Results were robust whether using haplotype diversity or species-proxies (mOTUs) as response variables. Additionally, species-level models of common taxa adequately predict overall species richness. ConclusionsUnderstanding the bulk of the diversity around us is imperative during an era of biodiversity loss. We show that dark insect taxa can be efficiently characterized and surveyed with megabarcoding. Undersampling of rare taxa and choice of operational taxonomic units do not alter the main ecological inferences, making it an opportune time to tackle zoologys dark matter.

ecology↗

bioRxiv2024

A Gridded Microclimate Dataset from a Sub-Arctic Biodiversity Hotspot in Finland

Microclimate measurements are crucial for explaining and predicting functions and patterns in nature, yet the availability of microclimate data often poses a challenge. This study introduces a dataset comprising 63 spatially continuous microclimate surfaces for the Kilpisjarvi region in northwestern Finland. The study region is a biodiversity hotspot for arctic-alpine flora and fauna and one of the most extensively investigated regions in Northern Europe. The data were gathered through a collaborative network of microclimate loggers, encompassing 430 measurement locations that comprehensively cover the 300 km2 landscape under study. We employed predominantly well-performing Random Forest models to project microclimate variables across the study area at a 3-metre spatial resolution. The high-resolution and extensive spatial coverage of the dataset facilitates examination of microclimate characteristics and variability across this sub-Arctic region, providing a valuable resource for both theoretical and applied research, as well as for biodiversity conservation.

ecology↗