SedaDNA reveals regional climate and bedrock nutrients as major drivers of terrestrial plant diversity in northern Fennoscandia.

Increased visitation to protected areas could have adverse impacts on the conservation values in the protected areas, and therefore effective visitor monitoring methods are needed to meet the complex management challenges that arise. Collecting data on human impacts is highly time consuming, thus requiring more effective tools that allow for high‐quality and long‐term measurements. In this study, we show how unmanned aerial vehicles (i.e. UAV or drones) could be used to monitor tourism impacts in protected areas. Tourism has boomed in national parks in Norway in recent years, such as in Jotunheimen National Park for which this study applies. We test the use of drones on a site where new tourist facilities will be established to set a baseline to identify future changes. We demonstrate how drones could help protected area management by monitoring visitor use patterns and commonly associated impacts such as trail condition (width and depth), vegetation structure and disturbances, informal trail proliferation, trampling, and trash and other impacts along the trails. We assessed accuracy and reliability compared with intensive field measurements of impacts and found low‐cost drones to be effective in mapping the study area with a resolution of 0.5 cm/pixel: drone derived trail measurements were comparable to traditional measurements with a negligible divergence on trail width measurements and a consistent 1.05 cm divergence on trail depth measurements that can be corrected with a few validation points. In addition, we created a high‐resolution vegetation classification map that could be used as a baseline for monitoring impacts. We conclude that drones can effectively contribute to visitor monitoring by reducing time spent in the field and by providing high‐resolution time series that could be used as baseline to measure tourism impacts on conservation values in protected areas.

We present a Holocene record of floristic diversity and environmental change for the central Varanger Peninsula, Finnmark, based on ancient DNA extracted from the sediments of a small lake ( sed a DNA ). The record covers the period c . 10 700 to 3300 cal. a BP and is complemented by pollen data. Measures of species richness, sample evenness and beta diversity were calculated based on sed a DNA sampling intervals and 1000‐year time windows. We identified 101 vascular plant and 17 bryophyte taxa, a high proportion (86%) of which are still growing within the region today. The high species richness (>60 taxa) observed in the Early Holocene, including representatives from all important plant functional groups, shows that modern shrub‐tundra communities, and much of their species complement, were in place as early as c . 10 700 cal. a BP . We infer that postglacial colonization of the area occurred prior to the full Holocene, during the Pleistocene‐Holocene transition, Younger Dryas stadial or earlier. Abundant DNA of the extra‐limital aquatic plant Callitriche hermaphroditica suggests it expanded its range northward between c . 10 200 and 9600 cal. a BP , when summers were warmer than present. High values of Pinus DNA occur throughout the record, but we cannot say with certainty if they represent prior local presence; however, pollen influx values >500 grains cm ⁻² a ⁻¹ between c . 8000 and 7300 cal. a BP strongly suggest the presence of pine woodland during this period. As the site lies beyond the modern tree limit of pine, it is likely that this expansion also reflects a response to warmer Early Holocene summers.

Ruminant fodder production in agricultural lands in latitudes above the Arctic Circle is constrained by short and hectic growing seasons with a 24-hour photoperiod and low growth temperatures. The use of remote sensing to measure crop production at high latitudes is hindered by intrinsic challenges, such as a low sun elevation angle and a coastal climate with high humidity, which influences the spectral signatures of the sampled vegetation. We used a portable spectrometer (ASD FieldSpec 3) to assess spectra of grass crops and found that when applying multivariate models to the hyperspectral datasets, results show significant predictability of yields (R² > 0.55, root mean squared error (RMSE) < 180), even when captured under sub-optimal conditions. These results are consistent both in the full spectral range of the spectrometer (350–2500 nm) and in the 350–900 nm spectral range, which is a region more robust against air moisture. Sentinel-2A simulations resulted in moderately robust models that could be used in qualitative assessments of field productivity. In addition, simulation of the upcoming hyperspectral EnMap satellite bands showed its potential applicability to measure yields in northern latitudes both in the full spectral range of the satellite (420–2450 nm) with similar performance as the Sentinel-2A satellite and in the 420–900 nm range with a comparable reliability to the portable spectrometer. The combination of EnMap and Sentinel-2A to detect fields with low productivity and portable spectrometers to identify the fields or specific regions of fields with the lowest production can help optimize the management of fodder production in high latitudes.

Complex interactions among multiple abiotic and biotic drivers result in rapid changes in ecosystems worldwide. Predicting how specific interactions can cause ripple effects potentially resulting in abrupt shifts in ecosystems is of high relevance to policymakers, but difficult to quantify using data from singular cases. We present causalizeR (https://github.com/fjmurguzur/causalizeR), a text-processing algorithm that extracts causal relations from literature based on simple grammatical rules that can be used to synthesize evidence in unstructured texts in a structured manner. The algorithm extracts causal links using the relative position of nouns relative to the keyword of choice to extract the cause and effects of interest. The resulting database can be combined with network analysis tools to estimate the direct and indirect effects of multiple drivers at the network level, which is useful for synthesizing available knowledge and for hypothesis creation and testing. We illustrate the use of the algorithm by detecting causal relationships in scientific literature relating to the tundra ecosystem.