causalizeR: a text mining algorithm to identify causal relationships in scientific literature

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

Research gaps and trends in the Arctic tundra: a topic-modelling approach

Climate change is affecting the biodiversity, ecosystem services and the well-being of people that live in the Arctic tundra. Understanding the societal implications and adapting to these changes depend on knowledge produced by multiple disciplines. We analysed peer-reviewed publications to identify the main research themes relating to the Arctic tundra and assessed to what extent current research build on multiple disciplines to confront the upcoming challenges of rapid environmental changes. We used a topic-modelling approach, based on the Latent Dirichlet Allocation algorithm to detect topics based on semantic similarity. We found that plant and soil ecology dominate the tundra research and are highly connected to other ecological disciplines and biophysical sciences. Despite the fivefold increase in the number of publications during the past decades, the proportion of studies that address societal implications of climate change remains low. The strong scientific interest in the tundra reflects the concern of the rapid warming of the Arctic, but few studies include the cross-disciplinary approach necessary to fully assess the implications of these changes for society

Adinak oozitoetan eragindako gene adierazpen aldaketa

[eus]Ikusi da herrialde garatuetan gizakiek umeak gero eta beranduago izaten dituztela, azken urteetan umeak izateko arazoak dituzten bikoteen kopurua asko handitu da. 30-40 urtetako emakumeek oozito akastun gehiago ekoizten dituzte, uste da gene adierazpen aldaketak akats horiek nola eman diren azaldu dezakeela. Horregatik, sagu gazte eta zaharrei oozitoak erauziko zaizkie eta mikroarray bidez geneen adierazpena aztertuko da. Adinak eta meiosi fase desberdinek duten gene adierazpen aldakortasuna konparatzean ikusi da meiosi fase desberdinen artean aldakortasun handiagoa dagoela. Adinarekin erlazionatuak dauden geneek elektroi garraio katean, ziklo zelularra eta garraio zelularrean parte hartzen dute. Meiosi fase desberdinetan adierazpen maila desberdina duten geneek gene adierazpenean, prozesu metabolikoetan, ziklo zelularretan eta erregulazio zelularretan parte hartzen dute

Impact of the COVID-19 pandemic on human-nature relations in a remote nature-based tourism destination

Tourism and nature-based recreation has changed dramatically during the COVID-19 pandemic. Travel restrictions caused sharp declines in visitation numbers, particularly in remote areas, such as northern Norway. In addition, the pandemic may have altered human-nature relationships by changing visitor behaviour and preferences. We studied visitor numbers and behaviour in northern Norway, based on user-generated data, in the form of photo graphic material that was uploaded to the popular online platform Flickr. A total of 195.200 photographs, taken by 5.247 photographers were subjected to Google’s “Cloud Vision” automatic content analysis algorithm. The resulting collection of labels that were assigned to each photograph was analysed in structural topic models, using photography date (relative to the start of the pandemic measures in Norway) and reported or estimated photographers’ nationality as explanatory variables. Our results show that nature-based recreation relating to “mountains” and “winter” became more prevalent during the pandemic, amongst both domestic and international photographers. Shifts in preferences due to the pandemic outbreak strongly depended on nationality, with domestic visitors demonstrating a wide interest in topics while international visitors maintained their preference for nature-based experiences. Among those activities that suffered the most from decline in international tourism was northern lights and cruises as indicated by the topic models. On the other hand, images depicting mountains and flora and fauna increased their prevalence during the pandemic. Domestic visitors, on the other hand, spent more time in urban settings as a result of restrictions, which results in a higher prevalence of non-nature related images. Our results underscore the need to consider the dynamic nature of human-nature relationships. The contrast in flexibility to adapt to changing conditions and travel restrictions should be incorporated in collaborativ

Drones as a Tool to Monitor Human Impacts and Vegetation Changes in Parks and Protected Areas

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

Combining satellite-sensed and ground data and the BASGRA model to predict grass yield in high-latitude regions

Context In high-latitude regions, variable weather conditions during the growing season and in winter cause considerable variation in forage grass productivity. Tools for predicting grassland status and yield, such as field measurements, satellite image analysis and process-based simulation models, can be combined in decision support for grassland management. Here, we calibrated and validated the BASic GRAssland (BASGRA) model against dry matter and Leaf area index data from temporary grasslands in northern Norway. Objective The objective of this study was to compare the performance of model versions calibrated against i) only region-specific ground data, ii) both region-specific ground and Sentinel-2 satellite data and, iii) field trial data from other regions. Methods Ground and satellite sensed data including biomass dry matter, leaf area index, and autumn and spring ground cover from 2020 to 2022 were acquired from 13 non-permanent grassland fields at four locations. These data were input to BASGRA calibrations together with soil and daily weather data, and information about cutting and nitrogen fertilizer application regimes. The effect of the winter season was taken into account in simulations by initiating the simulations either in autumn or in early spring. Results Within datasets, initiating the model in spring resulted in higher dry matter prediction accuracy (normalised RMSE 22.3–54.0 %) than initiating the model in autumn (normalised RMSE 41.1–93.4 %). Regional specific calibrations resulted in more accurate biomass predictions than calibrations from other regions while using satellite sensing data in addition to ground data resulted in only minor changes in biomass prediction accuracy. Conclusion All regional calibrations against data from northern Norway changed model parameter values and improved dry matter prediction accuracy compared with the reference calibration parameter values. Including satellite-sensed data in addition to ground data in calibrations did not further increase prediction accuracy compared with using only ground data. Implications Our findings show that regional data from farmers’ fields can substantially improve the performance of the BASGRA model compared to using controlled field trial data from other regions. This emphasises the need to account for regional diversity in non-permanent grassland when estimating grassland production potential and stress impact across geographic regions. Further use of satellite data in grassland model calibrations would probably benefit from more detailed assessments of the effect of grass growth characteristics and light and cloud conditions on estimates of grassland leaf area index and biomass from remote sensing.publishedVersio

Efficient Sampling for Ecosystem Service Supply Assessment at a Landscape Scale

Decision makers and stakeholders need high-quality data to manage ecosystem services (ES) efficiently. Landscape-level data on ES that are of sufficient quality to identify spatial tradeoffs, co-occurrence and hotspots of ES are costly to collect, and it is therefore important to increase the efficiency of sampling of primary data. We demonstrate how ES could be assessed more efficiently through image-based point intercept method and determine the tradeoff between the number of sample points (pins) used per image and the robustness of the measurements. We performed a permutation study to assess the reliability implications of reducing the number of pins per image. We present a flexible approach to optimize landscape-level assessments of ES that maximizes the information obtained from 1 m2 digital images. Our results show that 30 pins are sufficient to measure ecosystem service indicators with a crown cover higher than 5% for landscape scale assessments. Reducing the number of pins from 100 to 30 reduces the processing time up to a 50% allowing to increase the number of sampled plots, resulting in more management-relevant ecosystem service maps. The three criteria presented here provide a flexible approach for optimal design of landscape-level assessments of ES

Optimizing recruitment in an online environmental PPGIS—is it worth the time and costs?

Public participation GIS surveys use both random and volunteer sampling to recruit people to participate in a self-administered mapping exercise online. In random sampling designs, the participation rate is known to be relatively low and biased to specific segments (e.g. middle-aged, educated men). Volunteer sampling provides the opportunity to reach a large crowd at reasonable costs but generally suffers from unknown sampling biases and lower data quality. The low participation rates and the quality of mapping question the validity and generalizability of the results, limiting their use as a democratic tool for enhancing participation in spatial planning. We therefore asked: How can we increase participation in online environmental PPGIS surveys? Is it worth the time and costs? We reviewed environmentally related online PPGIS surveys (n ¼ 26) and analyzed the sampling biases and recruitment strategies utilized in a large-scale online PPGIS platform in coastal areas of northern Norway via both random (16,978 invited participants) and volunteer sampling. We found that the time, effort, and costs required to increase participation rates yielded meager results. We discuss the time and cost efficiency of different recruitment methods and the implications of participation levels despite the recruitment methods used