Local and Regional Scale Determinants of Biodiversity Patterns in Boreal Agricultural Landscapes

This thesis examines the local and regional scale determinants of biodiversity patterns using existing species and environmental data. The research focuses on agricultural environments that have experienced rapid declines of biodiversity during past decades. Existing digital databases provide vast opportunities for habitat mapping, predictive mapping of species occurrences and richness and understanding the speciesenvironment relationships. The applicability of these databases depends on the required accuracy and quality of the data needed to answer the landscape ecological and biogeographical questions in hand. Patterns of biodiversity arise from confounded effects of different factors, such as climate, land cover and geographical location. Complementary statistical approaches that can show the relative effects of different factors are needed in biodiversity analyses in addition to classical multivariate models. Better understanding of the key factors underlying the variation in diversity requires the analyses of multiple taxonomic groups from different perspectives, such as richness, occurrence, threat status and population trends. The geographical coincidence of species richness of different taxonomic groups can be rather limited. This implies that multiple geographical regions should be taken into account in order to preserve various groups of species. Boreal agricultural biodiversity and in particular, distribution and richness of threatened species is strongly associated with various grasslands. Further, heterogeneous agricultural landscapes characterized by moderate field size, forest patches and non-crop agricultural habitats enhance the biodiversity of rural environments. From the landscape ecological perspective, the major threats to Finnish agricultural biodiversity are the decline of connected grassland habitat networks, and general homogenization of landscape structure resulting from both intensification and marginalization of agriculture. The maintenance of key habitats, such as meadows and pastures is an essential task in conservation of agricultural biodiversity. Furthermore, a larger landscape context should be incorporated in conservation planning and decision making processes in order to respond to the needs of different species and to maintain heterogeneous rural landscapes and viable agricultural diversity in the future.Siirretty Doriast

The role of atmospheric circulation patterns in driving recent changes in indices of extreme seasonal precipitation across Arctic Fennoscandia

Extreme precipitation events (EPEs) have a major impact across Arctic Fennoscandia (AF). Here we examine the spatial variability of seasonal 50-year trends in three EPEs across AF for 1968–2017, using daily precipitation data from 46 meteorological stations, and analyse how these are related to contemporaneous changes in the principal atmospheric circulation patterns that impact AF climate. Positive trends in seasonal wet-day precipitation (PRCPTOT) are widespread across AF in all seasons except autumn. Spring (autumn) has the most widespread negative (positive) trends in consecutive dry days (CDD). There is less seasonal dependence for trends in consecutive wet days (CWDs), but the majority of the stations show an increase. Clear seasonal differences in the circulation pattern that exerted most influence on these AF EPE trends exist. In spring, PRCPTOT and CDD are most affected by the Scandinavian pattern at more than half the stations while it also has a marked influence on CWD. The East Atlantic/Western Russia pattern generally has the greatest influence on the most station EPE trends in summer and autumn, yet has no effect during either spring or winter. In winter, the dominant circulation pattern across AF varies more between the different EPEs, with the North Atlantic Oscillation, Polar/Eurasia and East Atlantic patterns all exerting a major influence. There are distinct geographical distributions to the dominant pattern affecting particular EPEs in some seasons, especially winter, while in others there is no discernible spatial relationship

Metsätalouden ja porotalouden keskinäiset vaikutukset ja suhteen muutokset Pohjois-Suomessa

Artikkelissa tarkastellaan metsätalouden ja porotalouden keskinäisiä vaikutuksia sekä näiden elinkeinojen välisten suhteiden muutoksia erityisesti ekologisten vaikutusten näkökulmasta. Metsätalouden ja porotalouden päällekkäinen maankäyttö on aiheuttanut kiistoja elinkeinojen välille Pohjois-Suomessa jo yli sadan vuoden ajan. Monet voimaperäiset metsätalouden toimenpiteet, kuten avohakkuut ja auraus, ovat vaikuttaneet haitallisesti porotalouteen 1950-luvulta lähtien. Metsätaloustoimet ovat vähentäneet, pirstaloineet ja heikentäneet porolaitumia, erityisesti luppoisia vanhoja metsiä, haitanneet poroja ja vaikeuttaneet poronhoitotöitä. Toisaalta porotalous on paikoin myös koettu metsätaloutta heikentäväksi toiminnaksi, mutta sen vaikutukset metsätalouteen ovat olleet enimmäkseen marginaalisia. Poro- ja metsätalouden harjoittajille osoitetun kyselytutkimuksen mukaan elinkeinojen väliset suhteet ovat parantuneet viime vuosikymmenten aikana Metsähallituksen ja paliskuntien välille kehitetyn neuvottelumenettelyn ansiosta. Elinkeinon harjoittajien mukaan myös yksityis- ja yhteismetsätalouden ja porotalouden välille tulisi kehittää vastaava, mutta vapaaehtoinen neuvottelumenettely. Lisäksi metsätaloudessa tulisi hyödyntää nykyistä enemmän porotalouden vaatimuksiin mukautettuja toimenpiteitä, jotka tähtäävät metsien eri-ikäisrakenteeseen, vanhojen metsien säästämiseen, hakkuutähteiden korjuuseen poroille tärkeiltä, jäkäläisimmiltä laitumilta, mahdollisimman kevyeen maanpinnan käsittelyyn ja luontaiseen uudistamiseen

Long-Term Climate Trends and Extreme Events in Northern Fennoscandia (1914-2013)

We studied climate trends and the occurrence of rare and extreme temperature and precipitation events in northern Fennoscandia in 1914-2013. Weather data were derived from nine observation stations located in Finland, Norway, Sweden and Russia. The results showed that spring and autumn temperatures and to a lesser extent summer temperatures increased significantly in the study region, the observed changes being the greatest for daily minimum temperatures. The number of frost days declined both in spring and autumn. Rarely cold winter, spring, summer and autumn seasons had a low occurrence and rarely warm spring and autumn seasons a high occurrence during the last 20-year interval (1994-2013), compared to the other 20-year intervals. That period was also characterized by a low number of days with extremely low temperature in all seasons (4%-9% of all extremely cold days) and a high number of April and October days with extremely high temperature (36%-42% of all extremely warm days). A tendency of exceptionally high daily precipitation sums to grow even higher towards the end of the study period was also observed. To summarize, the results indicate a shortening of the cold season in northern Fennoscandia. Furthermore, the results suggest significant declines in extremely cold climate events in all seasons and increases in extremely warm climate events particularly in spring and autumn seasons

Spatiotemporal distribution of threatened high-latitude snowbed and snow patch habitats in warming climate

Peer reviewe

Developing fine-grained nationwide predictions of valuable forests using biodiversity indicator bird species

Publisher Copyright: © 2021 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America.The use of indicator species in forest conservation and management planning can facilitate enhanced preservation of biodiversity from the negative effects of forestry and other uses of land. However, this requires detailed and spatially comprehensive knowledge of the habitat preferences and distributions of selected focal indicator species. Unfortunately, due to limited resources for field surveys, only a small proportion of the occurrences of focal species is usually known. This shortcoming can be circumvented by using modelling techniques to predict the spatial distribution of suitable sites for the target species. Airborne laser scanning (ALS) and other remote sensing (RS) techniques have the potential to provide useful environmental data covering systematically large areas for these purposes. Here, we focused on six bird of prey and woodpecker species known to be good indicators of boreal forest biodiversity values. We used known nest sites of the six indicator species based on nestling ringing records. Thus, the most suitable nesting sites of these species provide important information for biodiversity-friendly forest management and conservation planning. We developed fine-grained, i.e., 96 x 96 m grid cell resolution, predictive maps across the whole of Finland of the suitable nesting habitats based on ALS and other RS data and spatial information on the distribution of important forest stands for the six studied biodiversity indicator bird species based on nesting habitat suitability modelling, i.e., the MaxEnt model. Habitat preferences of the study species, as determined by MaxEnt, were in line with the previous knowledge of species-habitat relations. The proportion of suitable habitats of these species in protected areas was considerable, but our analysis also revealed many potentially high-quality forest stands outside protected areas. However, many of these sites are increasingly threatened by logging due to increased pressures for using forests for bioeconomy and forest industry based on National Forest Strategy. Predicting habitat suitability based on information on the nest sites of indicator species provides a new tool for systematic conservation planning over large areas in boreal forests in Europe, and corresponding approach would also be feasible and recommendable elsewhere where similar data are available.The use of indicator species in forest conservation and management planning can facilitate enhanced preservation of biodiversity from the negative effects of forestry and other uses of land. However, this requires detailed and spatially comprehensive knowledge of the habitat preferences and distributions of selected focal indicator species. Unfortunately, due to limited resources for field surveys, only a small proportion of the occurrences of focal species is usually known. This shortcoming can be circumvented by using modeling techniques to predict the spatial distribution of suitable sites for the target species. Airborne laser scanning (ALS) and other remote sensing (RS) techniques have the potential to provide useful environmental data covering systematically large areas for these purposes. Here, we focused on six bird of prey and woodpecker species known to be good indicators of boreal forest biodiversity values. We used known nest sites of the six indicator species based on nestling ringing records. Thus, the most suitable nesting sites of these species provide important information for biodiversity-friendly forest management and conservation planning. We developed fine-grained, that is, 96 x 96 m grid cell resolution, predictive maps across the whole of Finland of the suitable nesting habitats based on ALS and other RS data and spatial information on the distribution of important forest stands for the six studied biodiversity indicator bird species based on nesting-habitat suitability modeling, that is, the MaxEnt model. Habitat preferences of the study species, as determined by MaxEnt, were in line with the previous knowledge of species-habitat relations. The proportion of suitable habitats of these species in protected areas (PAs) was considerable, but our analysis also revealed many potentially high-quality forest stands outside PAs. However, many of these sites are increasingly threatened by logging because of increased pressures for using forests for bioeconomy and forest industry based on National Forest Strategy. Predicting habitat suitability based on information on the nest sites of indicator species provides a new tool for systematic conservation planning over large areas in boreal forests in Europe, and a corresponding approach would also be feasible and recommendable elsewhere where similar data are available.Peer reviewe

Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests.Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras:Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.peerReviewe

‘‘Generality of mis-fit’’? The real-life difficulty of matching scales in an interconnected world

A clear understanding of processes at multiple scales and levels is of special significance when conceiving strategies for human–environment interactions. However, understanding and application of the scale concept often differ between administrative-political and ecological disciplines. These mirror major differences in potential solutions whether and how scales can, at all, be made congruent. As a result, opportunities of seeking ‘‘goodness-of-fit’’ between different concepts of governance should perhaps be reconsidered in the light of a potential ‘‘generality of mis-fit.’’ This article reviews the interdisciplinary considerations inherent in the concept of scale in its ecological, as well as administrative-political, significance and argues that issues of how to manage ‘‘misfit’’ should be awarded more emphasis in social-ecological research and management practices. These considerations are exemplified by the case of reindeer husbandry in Fennoscandia. Whilst an indigenous small-scale practice, reindeer husbandry involves multi-level ecological and administrative-political complexities—complexities thatwe argue may arise in any multi-level system.</div

Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks

During the last two decades, forest monitoring and inventory systems have moved from field surveys to remote sensing-based methods. These methods tend to focus on economically significant components of forests, thus leaving out many factors vital for forest biodiversity, such as the occurrence of species with low economical but high ecological values. Airborne hyperspectral imagery has shown significant potential for tree species classification, but the most common analysis methods, such as random forest and support vector machines, require manual feature engineering in order to utilize both spatial and spectral features, whereas deep learning methods are able to extract these features from the raw data. Our research focused on the classification of the major tree species Scots pine, Norway spruce and birch, together with an ecologically valuable keystone species, European aspen, which has a sparse and scattered occurrence in boreal forests. We compared the performance of three-dimensional convolutional neural networks (3D-CNNs) with the support vector machine, random forest, gradient boosting machine and artificial neural network in individual tree species classification from hyperspectral data with high spatial and spectral resolution. We collected hyperspectral and LiDAR data along with extensive ground reference data measurements of tree species from the 83 km2 study area located in the southern boreal zone in Finland. A LiDAR-derived canopy height model was used to match ground reference data to aerial imagery. The best performing 3D-CNN, utilizing 4 m image patches, was able to achieve an F1-score of 0.91 for aspen, an overall F1-score of 0.86 and an overall accuracy of 87%, while the lowest performing 3D-CNN utilizing 10 m image patches achieved an F1-score of 0.83 and an accuracy of 85%. In comparison, the support-vector machine achieved an F1-score of 0.82 and an accuracy of 82.4% and the artificial neural network achieved an F1-score of 0.82 and an accuracy of 81.7%. Compared to the reference models, 3D-CNNs were more efficient in distinguishing coniferous species from each other, with a concurrent high accuracy for aspen classification. Deep neural networks, being black box models, hide the information about how they reach their decision. We used both occlusion and saliency maps to interpret our models. Finally, we used the best performing 3D-CNN to produce a wall-to-wall tree species map for the full study area that can later be used as a reference prediction in, for instance, tree species mapping from multispectral satellite images. The improved tree species classification demonstrated by our study can benefit both sustainable forestry and biodiversity conservation.peerReviewe

A keystone species, European aspen (Populus tremula L.), in boreal forests : Ecological role, knowledge needs and mapping using remote sensing

European aspen (Populus tremula L.) is a keystone species in boreal forests that are dominated by coniferous tree species. Both living and dead aspen trees contribute significantly to the species diversity of forest landscapes. Thus, spatial and temporal continuity of aspen is a prerequisite for the long-term persistence of viable populations of numerous aspen-associated species. In this review, we collate existing knowledge on the ecological role of European aspen, assess the knowledge needs for aspen occurrence patterns and dynamics in boreal forests and discuss the potential of different remote sensing techniques in mapping aspen at various spatiotemporal scales. The role of aspen as a key ecological feature has received significant attention, and studies have recognised the negative effects of modern forest management methods and heavy browsing on aspen occurrence and regeneration. However, the spatial knowledge of occurrence, abundance and temporal dynamics of aspen is scarce and incomprehensive. The remote sensing studies reviewed here highlight particularly the potential of three-dimensional data derived from airborne laser scanning or photogrammetric point clouds and airborne imaging spectroscopy in mapping European aspen, quaking aspen (Populus tremuloides Michx.) and other Populus species. In addition to tree species discrimination, these methods can provide information on biophysical, biochemical properties and even genetic diversity of aspen trees. Major obstacles in aspen detection using remote sensing are the low proportion and scattered occurrence of European aspen in boreal forests and the overlap of spectral and/or structural properties of European aspen and quaking aspen with some other tree species. Furthermore, the suitability of remote sensing data for aspen mapping and monitoring depends on the geographical coverage of data, the availability of multitemporal data and the costs of data acquisition. Our review highlights that integration of ecological knowledge with spatiotemporal information acquired by remote sensing is key to understanding the current and future distribution patterns of aspen-related biodiversity.peerReviewe