A circumpolar study unveils a positive non-linear effect of temperature on arctic arthropod availability that may reduce the risk of warming-induced trophic mismatch for breeding shorebirds.

Seasonally abundant arthropods are a crucial food source for many migratory birds that breed in the Arctic. In cold environments, the growth and emergence of arthropods are particularly tied to temperature. Thus, the phenology of arthropods is anticipated to undergo a rapid change in response to a warming climate, potentially leading to a trophic mismatch between migratory insectivorous birds and their prey. Using data from 19 sites spanning a wide temperature gradient from the Subarctic to the High Arctic, we investigated the effects of temperature on the phenology and biomass of arthropods available to shorebirds during their short breeding season at high latitudes. We hypothesized that prolonged exposure to warmer summer temperatures would generate earlier peaks in arthropod biomass, as well as higher peak and seasonal biomass. Across the temperature gradient encompassed by our study sites (>10°C in average summer temperatures), we found a 3-day shift in average peak date for every increment of 80 cumulative thawing degree-days. Interestingly, we found a linear relationship between temperature and arthropod biomass only below temperature thresholds. Higher temperatures were associated with higher peak and seasonal biomass below 106 and 177 cumulative thawing degree-days, respectively, between June 5 and July 15. Beyond these thresholds, no relationship was observed between temperature and arthropod biomass. Our results suggest that prolonged exposure to elevated temperatures can positively influence prey availability for some arctic birds. This positive effect could, in part, stem from changes in arthropod assemblages and may reduce the risk of trophic mismatch.

Reading tea leaves worldwide: Decoupled drivers of initial litter decomposition mass-loss rate and stabilization.

The breakdown of plant material fuels soil functioning and biodiversity. Currently, process understanding of global decomposition patterns and the drivers of such patterns are hampered by the lack of coherent large-scale datasets. We buried 36,000 individual litterbags (tea bags) worldwide and found an overall negative correlation between initial mass-loss rates and stabilization factors of plant-derived carbon, using the Tea Bag Index (TBI). The stabilization factor quantifies the degree to which easy-to-degrade components accumulate during early-stage decomposition (e.g. by environmental limitations). However, agriculture and an interaction between moisture and temperature led to a decoupling between initial mass-loss rates and stabilization, notably in colder locations. Using TBI improved mass-loss estimates of natural litter compared to models that ignored stabilization. Ignoring the transformation of dead plant material to more recalcitrant substances during early-stage decomposition, and the environmental control of this transformation, could overestimate carbon losses during early decomposition in carbon cycle models.

Towards a toolkit for global insect biodiversity monitoring.

Insects are the most diverse group of animals on Earth, yet our knowledge of their diversity, ecology and population trends remains abysmally poor. Four major technological approaches are coming to fruition for use in insect monitoring and ecological research-molecular methods, computer vision, autonomous acoustic monitoring and radar-based remote sensing-each of which has seen major advances over the past years. Together, they have the potential to revolutionize insect ecology, and to make all-taxa, fine-grained insect monitoring feasible across the globe. So far, advances within and among technologies have largely taken place in isolation, and parallel efforts among projects have led to redundancy and a methodological sprawl; yet, given the commonalities in their goals and approaches, increased collaboration among projects and integration across technologies could provide unprecedented improvements in taxonomic and spatio-temporal resolution and coverage. This theme issue showcases recent developments and state-of-the-art applications of these technologies, and outlines the way forward regarding data processing, cost-effectiveness, meaningful trend analysis, technological integration and open data requirements. Together, these papers set the stage for the future of automated insect monitoring. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Lidar as a potential tool for monitoring migratory insects.

The seasonal migrations of insects involve a substantial displacement of biomass with significant ecological and economic consequences for regions of departure and arrival. Remote sensors have played a pivotal role in revealing the magnitude and general direction of bioflows above 150 m. Nevertheless, the takeoff and descent activity of insects below this height is poorly understood. Our lidar observations elucidate the low-height dusk movements and detailed information of insects in southern Sweden from May to July, during the yearly northward migration period. Importantly, by filtering out moths from other insects based on optical information and wingbeat frequency, we have introduced a promising new method to monitor the flight activities of nocturnal moths near the ground, many of which participate in migration through the area. Lidar thus holds the potential to enhance the scientific understanding of insect migratory behavior and improve pest control strategies.

Deep learning to extract the meteorological by-catch of wildlife cameras.

Microclimate-proximal climatic variation at scales of metres and minutes-can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro-scales, and deep learning tools rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, ecologically relevant meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africa's Maloti-Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. We achieve 91.7% accuracy on test cameras not seen during training. Furthermore, we show how effective accuracy is raised to 96% by disregarding 14.1% of classifications where ensemble member models did not reach a consensus. For two-class weather classification (overcast vs. sunshine) in a novel location in Svalbard, Norway, we achieve 79.3% accuracy (93.9% consensus accuracy), outperforming a benchmark model from the computer vision literature (75.5% accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2 million unlabelled images. Resulting micrometeorological data illustrated common seasonal patterns of summer hailstorms and autumn snowfalls across mountains in the northern and southern hemispheres. However, daily patterns of sunshine and shade diverged between sites, impacting daily temperature cycles. Crucially, we leverage micrometeorological data to demonstrate that (1) experimental warming using open-top chambers shortens early snow events in autumn, and (2) image-derived sunshine marginally outperforms sensor-derived temperature when predicting bumblebee foraging. These methods generate novel micrometeorological variables in synchrony with biological recordings, enabling new insights from an increasingly global network of wildlife cameras.

Little directional change in the timing of Arctic spring phenology over the past 25 years.

With the global change in climate, the Arctic has been pinpointed as the region experiencing the fastest rates of change. As a result, Arctic biological responses-such as shifts in phenology-are expected to outpace those at lower latitudes. 15 years ago, a decade-long dataset from Zackenberg in High Arctic Greenland revealed rapid rates of phenological change.1 To explore how the timing of spring phenology has developed since, we revisit the Zackenberg time series on flowering plants, arthropods, and birds. Drawing on the full 25-year period of 1996-2020, we find little directional change in the timing of events despite ongoing climatic change. We attribute this finding to a shift in the temporal patterns of climate conditions, from previous directional change to current high inter-annual variability. Additionally, some taxa appear to have reached the limits of their phenological responses, resulting in a leveling off in their phenological responses in warm years. Our findings demonstrate the importance of long-term monitoring of taxa from across trophic levels within the community, allowing for detecting shifts in sensitivities and responses and thus for updated inference in the light of added information.

Volatile responses of dwarf birch to mimicked insect herbivory and experimental warming at two elevations in Greenlandic tundra.

Plants release a complex blend of volatile organic compounds (VOCs) in response to stressors. VOC emissions vary between contrasting environments and increase with insect herbivory and rising temperatures. However, the joint effects of herbivory and warming on plant VOC emissions are understudied, particularly in high latitudes, which are warming fast and facing increasing herbivore pressure. We assessed the individual and combined effects of chemically mimicked insect herbivory, warming, and elevation on dwarf birch (Betula glandulosa) VOC emissions in high-latitude tundra ecosystems in Narsarsuaq, South Greenland. We hypothesized that VOC emissions and compositions would respond synergistically to warming and herbivory, with the magnitude differing between elevations. Warming increased emissions of green leaf volatiles (GLVs) and isoprene. Herbivory increased the homoterpene, (E)-4,8-dimethyl-1,3,7-nonatriene, emissions, and the response was stronger at high elevation. Warming and herbivory had synergistic effects on GLV emissions. Dwarf birch emitted VOCs at similar rates at both elevations, but the VOC blends differed between elevations. Several herbivory-associated VOC groups did not respond to herbivory. Harsher abiotic conditions at high elevations might not limit VOC emissions from dwarf birch, and high-elevation plants might be better at herbivory defense than assumed. The complexity of VOC responses to experimental warming, elevation, and herbivory are challenging our understanding and predictions of future VOC emissions from dwarf birch-dominated ecosystems.

Climate Change Helps Polar Invasives Establish and Flourish: Evidence from Long-Term Monitoring of the Blowfly Calliphora vicina.

The isolated sub-Antarctic islands are of major ecological interest because of their unique species diversity and long history of limited human disturbance. However, since the presence of Europeans, these islands and their sensitive biota have been under increasing pressure due to human activity and associated biological invasions. In such delicate ecosystems, biological invasions are an exceptional threat that may be further amplified by climate change. We examined the invasion trajectory of the blowfly Calliphora vicina (Robineau-Desvoidy 1830). First introduced in the sub-Antarctic Kerguelen Islands in the 1970s, it is thought to have persisted only in sheltered microclimates for several decades. Here, we show that, in recent decades, C. vicina has been able to establish itself more widely. We combine experimental thermal developmental data with long-term ecological and meteorological monitoring to address whether warming conditions help explain its current success and dynamics in the eastern Kerguelen Islands. We found that warming temperatures and accumulated degree days could explain the species' phenological and long-term invasion dynamics, indicating that climate change has likely assisted its establishment. This study represents a unique long-term view of a polar invader and stresses the rapidly increasing susceptibility of cold regions to invasion under climate change.

Towards the fully automated monitoring of ecological communities.

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network.

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities.

Accurate image-based identification of macroinvertebrate specimens using deep learning-How much training data is needed?

Image-based methods for species identification offer cost-efficient solutions for biomonitoring. This is particularly relevant for invertebrate studies, where bulk samples often represent insurmountable workloads for sorting, identifying, and counting individual specimens. On the other hand, image-based classification using deep learning tools have strict requirements for the amount of training data, which is often a limiting factor. Here, we examine how classification accuracy increases with the amount of training data using the BIODISCOVER imaging system constructed for image-based classification and biomass estimation of invertebrate specimens. We use a balanced dataset of 60 specimens of each of 16 taxa of freshwater macroinvertebrates to systematically quantify how classification performance of a convolutional neural network (CNN) increases for individual taxa and the overall community as the number of specimens used for training is increased. We show a striking 99.2% classification accuracy when the CNN (EfficientNet-B6) is trained on 50 specimens of each taxon, and also how the lower classification accuracy of models trained on less data is particularly evident for morphologically similar species placed within the same taxonomic order. Even with as little as 15 specimens used for training, classification accuracy reached 97%. Our results add to a recent body of literature showing the huge potential of image-based methods and deep learning for specimen-based research, and furthermore offers a perspective to future automatized approaches for deriving ecological data from bulk arthropod samples.

A mobile observatory powered by sun and wind for near real time measurements of atmospheric, glacial, terrestrial, limnic and coastal oceanic conditions in remote off-grid areas.

Climate change is rapidly altering the Arctic environment. Although long-term environmental observations have been made at a few locations in the Arctic, the incomplete coverage from ground stations is a main limitation to observations in these remote areas. Here we present a wind and sun powered multi-purpose mobile observatory (ARC-MO) that enables near real time measurements of air, ice, land, rivers, and marine parameters in remote off-grid areas. Two test units were constructed and placed in Northeast Greenland where they have collected data from cabled and wireless instruments deployed in the environment since late summer 2021. The two units can communicate locally via WiFi (units placed 25 km apart) and transmit near-real time data globally over satellite. Data are streamed live and accessible from (https://gios.org). The cost of one mobile observatory unit is c. 304.000€. These test units demonstrate the possibility for integrative and automated environmental data collection in remote coastal areas and could serve as models for a proposed global observatory system.

Moths complement bumblebee pollination of red clover: a case for day-and-night insect surveillance.

Recent decades have seen a surge in awareness about insect pollinator declines. Social bees receive the most attention, but most flower-visiting species are lesser known, non-bee insects. Nocturnal flower visitors, e.g. moths, are especially difficult to observe and largely ignored in pollination studies. Clearly, achieving balanced monitoring of all pollinator taxa represents a major scientific challenge. Here, we use time-lapse cameras for season-wide, day-and-night pollinator surveillance of Trifolium pratense (L.; red clover) in an alpine grassland. We reveal the first evidence to suggest that moths, mainly Noctua pronuba (L.; large yellow underwing), pollinate this important wildflower and forage crop, providing 34% of visits (bumblebees: 61%). This is a remarkable finding; moths have received no recognition throughout a century of T. pratense pollinator research. We conclude that despite a non-negligible frequency and duration of nocturnal flower visits, nocturnal pollinators of T. pratense have been systematically overlooked. We further show how the relationship between visitation and seed set may only become clear after accounting for moth visits. As such, population trends in moths, as well as bees, could profoundly affect T. pratense seed yield. Ultimately, camera surveillance gives fair representation to non-bee pollinators and lays a foundation for automated monitoring of species interactions in future.

Emerging technologies revolutionise insect ecology and monitoring.

Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technological advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaboration.

Impacts of elevation on plant traits and volatile organic compound emissions in deciduous tundra shrubs.

The northernmost regions of our planet experience twice the rate of climate warming compared to the global average. Despite the currently low air temperatures, tundra shrubs are known to exhibit high leaf temperatures and are increasing in height due to warming, but it is unclear how the increase in height will affect the leaf temperature. To study how temperature, soil moisture, and changes in light availability influence the physiology and emissions of climate-relevant volatile organic compounds (VOCs), we conducted a study on two common deciduous tundra shrubs, Salix glauca (separating males and females for potential effects of plant sex) and Betula glandulosa, at two elevations in South Greenland. Low-elevation Salix shrubs were 45% taller, but had 37% lower rates of net CO2 assimilation and 63% lower rates of isoprene emission compared to high-elevation shrubs. Betula shrubs showed 40% higher stomatal conductance and 24% higher glandular trichome density, in the low-elevation valley, compared to those from the high-elevation mountain slope. Betula green leaf volatile emissions were 235% higher at high elevation compared to low elevation. Male Salix showed a distinct VOC blend and emitted 55% more oxygenated VOCs, compared to females, possibly due to plant defense mechanisms. In our light response curves, isoprene emissions increased linearly with light intensity, potentially indicating adaptation to strong light. Leaf temperature decreased with increasing Salix height, at 4 °C m-1, which can have implications for plant physiology. However, no similar relationship was observed for B. glandulosa. Our results highlight that tundra shrub traits and VOC emissions are sensitive to temperature and light, but that local variations in soil moisture strongly interact with temperature and light responses. Our results suggest that effects of climate warming, alone, poorly predict the actual plant responses in tundra vegetation.

Strong isolation by distance among local populations of an endangered butterfly species (Euphydryas aurinia).

The marsh fritillary (Euphydryas aurinia) is a critically endangered butterfly species in Denmark known to be particularly vulnerable to habitat fragmentation due to its poor dispersal capacity. We identified and genotyped 318 novel SNP loci across 273 individuals obtained from 10 small and fragmented populations in Denmark using a genotyping-by-sequencing (GBS) approach to investigate its population genetic structure. Our results showed clear genetic substructuring and highly significant population differentiation based on genetic divergence (F ST) among the 10 populations. The populations clustered in three overall clusters, and due to further substructuring among these, it was possible to clearly distinguish six clusters in total. We found highly significant deviations from Hardy-Weinberg equilibrium due to heterozygote deficiency within every population investigated, which indicates substructuring and/or inbreeding (due to mating among closely related individuals). The stringent filtering procedure that we have applied to our genotype quality could have overestimated the heterozygote deficiency and the degree of substructuring of our clusters but is allowing relative comparisons of the genetic parameters among clusters. Genetic divergence increased significantly with geographic distance, suggesting limited gene flow at spatial scales comparable to the dispersal distance of individual butterflies and strong isolation by distance. Altogether, our results clearly indicate that the marsh fritillary populations are genetically isolated. Further, our results highlight that the relevant spatial scale for conservation of rare, low mobile species may be smaller than previously anticipated.

Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning.

Invasive alien plant species (IAPS) pose a threat to biodiversity as they propagate and outcompete natural vegetation. In this study, a system for monitoring IAPS on the roadside is presented. The system consists of a camera that acquires images at high speed mounted on a vehicle that follows the traffic. Images of seven IAPS (Cytisus scoparius, Heracleum, Lupinus polyphyllus, Pastinaca sativa, Reynoutria, Rosa rugosa, and Solidago) were collected on Danish motorways. Three deep convolutional neural networks for classification (ResNet50V2 and MobileNetV2) and object detection (YOLOv3) were trained and evaluated at different image sizes. The results showed that the performance of the networks varied with the input image size and also the size of the IAPS in the images. Binary classification of IAPS vs. non-IAPS showed an increased performance, compared to the classification of individual IAPS. This study shows that automatic detection and mapping of invasive plants along the roadside is possible at high speeds.

Thermal acclimation has limited effect on the thermal tolerances of summer-collected Arctic and sub-Arctic wolf spiders.

High-latitude ectotherms contend with large daily and seasonal temperature variation. Summer-collected wolf spiders (Araneae; Lycosidae) from sub-Arctic and Arctic habitats have been previously documented as having low temperature tolerance insufficient for surviving year-round in their habitat. We tested two competing hypotheses: that they would have broad thermal breadth, or that they would use plasticity to extend the range of their thermal performance. We collected Pardosa moesta and P. lapponica from the Yukon Territory, Canada, P. furcifera, P. groenlandica, and P. hyperborea from southern Greenland, and P. hyperborea from sub-Arctic Norway, and acclimated them to warm (12 or 20 °C) or cool (4 °C) conditions under constant light for one week. We measured critical thermal minimum (CTmin) or supercooling point (SCP) as a measure of lower thermal limit, and critical thermal maximum (CTmax) as a measure of upper thermal limit. We found relatively little impact of acclimation on thermal limits, and some counterintuitive responses; for example, warm acclimation decreased the SCP and/or cool acclimation increased the CTmax in several cases. Together, this meant that acclimation did not appear to modify the thermal breadth, which supports our first hypothesis, but allows us to reject the hypothesis that spiders use plasticity to fine-tune their thermal physiology, at least in the summer. We note that we still cannot explain how these spiders withstand the very cold winters, and speculate that there are acclimatisation cues or processes that we were unable to capture in our study.

An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning.

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

Deep learning and computer vision will transform entomology.

Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.