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We present regionally aggregated emissions of greenhouse gases (GHG) from five land cover categories in Finland: artificial surfaces, arable land, forest, waterbodies, and wetlands. Carbon (C) sequestration to managed forests and unmanaged wetlands was also assessed. Models FRES and ALas were applied for emissions (CO2, CH4, N2O) from artificial surfaces and agriculture, and PREBAS for forest growth and C balance. Empirical emission coefficients were used to estimate emissions from drained forested peatland (CH4, N2O), cropland (CO2), waterbodies (CH4, CO2), peat production sites and undrained mires (CH4, CO2, N2O). We calculated gross emissions of 147.2 ± 6.8 TgCO2eq yr-1 for 18 administrative units covering mainland Finland, using data representative of the period 2017-2025. Emissions from energy production, industrial processes, road traffic and other sources in artificial surfaces amounted to 45.7 ± 2.0 TgCO2eq yr-1. The loss of C in forest harvesting was the largest emission source in the LULUCF sector, in total 59.8 ± 3.3 TgCO2eq yr-1. Emissions from domestic livestock production, field cultivation and organic soils added up to 12.2 ± 3.5 TgCO2eq yr-1 from arable land. Rivers and lakes (13.4 ± 1.9 TgCO2eq yr-1) as well as undrained mires and peat production sites (14.7 ± 1.8 TgCO2eq yr-1) increased the total GHG fluxes. The C sequestration from the atmosphere was 93.2 ± 13.7 TgCO2eq yr-1. with the main sink in forest on mineral soil (79.9 ± 12.2 TgCO2eq yr-1). All sinks compensated 63% of total emissions and thus the net emissions were 53.9 ± 15.3 TgCO2eq yr-1, or a net GHG flux per capita of 9.8 MgCO2eq yr-1.
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The EU aims at reaching carbon neutrality by 2050 and Finland by 2035. We integrated results of three spatially distributed model systems (FRES, PREBAS, Zonation) to evaluate the potential to reach this goal at both national and regional scale in Finland, by simultaneously considering protection targets of the EU biodiversity (BD) strategy. Modelling of both anthropogenic emissions and forestry measures were carried out, and forested areas important for BD protection were identified based on spatial prioritization. We used scenarios until 2050 based on mitigation measures of the national climate and energy strategy, forestry policies and predicted climate change, and evaluated how implementation of these scenarios would affect greenhouse gas fluxes, carbon storages, and the possibility to reach the carbon neutrality target. Potential new forested areas for BD protection according to the EU 10% protection target provided a significant carbon storage (426-452 TgC) and sequestration potential (- 12 to - 17.5 TgCO2eq a-1) by 2050, indicating complementarity of emission mitigation and conservation measures. The results of the study can be utilized for integrating climate and BD policies, accounting of ecosystem services for climate regulation, and delimitation of areas for conservation.
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Data are currently being used, and reused, in ecological research at an unprecedented rate. To ensure appropriate reuse however, we need to ask the question: "Are aggregated databases currently providing the right information to enable effective and unbiased reuse?" We investigate this question, with a focus on designs that purposefully favor the selection of sampling locations (upweighting the probability of selection of some locations). These designs are common and examples are those designs that have uneven inclusion probabilities or are stratified. We perform a simulation experiment by creating data sets with progressively more uneven inclusion probabilities and examine the resulting estimates of the average number of individuals per unit area (density). The effect of ignoring the survey design can be profound, with biases of up to 250% in density estimates when naive analytical methods are used. This density estimation bias is not reduced by adding more data. Fortunately, the estimation bias can be mitigated by using an appropriate estimator or an appropriate model that incorporates the design information. These are only available however, when essential information about the survey design is available: the sample location selection process (e.g., inclusion probabilities), and/or covariates used in their specification. The results suggest that such information must be stored and served with the data to support meaningful inference and data reuse.
Asunto(s)
Ecología , Proyectos de Investigación , Simulación por Computador , ProbabilidadRESUMEN
Metapopulation dynamics - patch occupancy, colonization and extinction - are the result of complex processes at both local (e.g. environmental conditions) and regional (e.g. spatial arrangement of habitat patches) scales. A large body of work has focused on habitat patch area and connectivity (area-isolation paradigm). However, these approaches often do not incorporate local environmental conditions or fully address how the spatial arrangement of habitat patches (and resulting connectivity) can influence metapopulation dynamics. Here, we utilize long-term data on a classic metapopulation system - the Glanville fritillary butterfly occupying a set of dry meadows and pastures in the Åland islands - to investigate the relative roles of local environmental conditions, geographic space and connectivity in capturing patch occupancy, colonization and extinction. We defined connectivity using traditional measures as well as graph-theoretic measures of centrality. Using boosted regression tree models, we find roughly comparable model performance among models trained on environmental conditions, geographic space or patch centrality. In models containing all of the covariates, we find strong and consistent evidence for the roles of resource abundance, longitude and centrality (i.e. connectivity) in predicting habitat patch occupancy and colonization, while patch centrality (connectivity) was relatively unimportant for predicting extinction. Relative variable importance did not change when geographic coordinates were not considered and models underwent spatially stratified cross-validation. Together, this suggests that the combination of regional-scale connectivity measures and local-scale environmental conditions is important for predicting metapopulation dynamics and that a stronger integration of ideas from network theory may provide insight into metapopulation processes.
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Mariposas Diurnas , Ecosistema , Animales , Finlandia , Modelos Biológicos , Dinámica PoblacionalRESUMEN
Habitat fragmentation and climate change are both prominent manifestations of global change, but there is little knowledge on the specific mechanisms of how climate change may modify the effects of habitat fragmentation, for example, by altering dynamics of spatially structured populations. The long-term viability of metapopulations is dependent on independent dynamics of local populations, because it mitigates fluctuations in the size of the metapopulation as a whole. Metapopulation viability will be compromised if climate change increases spatial synchrony in weather conditions associated with population growth rates. We studied a recently reported increase in metapopulation synchrony of the Glanville fritillary butterfly (Melitaea cinxia) in the Finnish archipelago, to see if it could be explained by an increase in synchrony of weather conditions. For this, we used 23 years of butterfly survey data together with monthly weather records for the same period. We first examined the associations between population growth rates within different regions of the metapopulation and weather conditions during different life-history stages of the butterfly. We then examined the association between the trends in the synchrony of the weather conditions and the synchrony of the butterfly metapopulation dynamics. We found that precipitation from spring to late summer are associated with the M. cinxia per capita growth rate, with early summer conditions being most important. We further found that the increase in metapopulation synchrony is paralleled by an increase in the synchrony of weather conditions. Alternative explanations for spatial synchrony, such as increased dispersal or trophic interactions with a specialist parasitoid, did not show paralleled trends and are not supported. The climate driven increase in M. cinxia metapopulation synchrony suggests that climate change can increase extinction risk of spatially structured populations living in fragmented landscapes by altering their dynamics.
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Mariposas Diurnas/fisiología , Cambio Climático , Animales , Mariposas Diurnas/crecimiento & desarrollo , Ecosistema , Finlandia , Larva/crecimiento & desarrollo , Larva/fisiología , Dinámica Poblacional , Estaciones del Año , Tiempo (Meteorología)RESUMEN
Ecologists are challenged to construct models of the biological consequences of habitat loss and fragmentation. Here, we use a metapopulation model to predict the distribution of the Glanville fritillary butterfly during 22 years across a large heterogeneous landscape with 4,415 small dry meadows. The majority (74%) of the 125 networks into which the meadows were clustered are below the extinction threshold for long-term persistence. Among the 33 networks above the threshold, spatial configuration and habitat quality rather than the pooled habitat area predict metapopulation size and persistence, but additionally allelic variation in a SNP in the gene Phosphoglucose isomerase (Pgi) explains 30% of variation in metapopulation size. The Pgi genotypes are associated with dispersal rate and hence with colonizations and extinctions. Associations between Pgi genotypes, population turnover and metapopulation size reflect eco-evolutionary dynamics, which may be a common feature in species inhabiting patch networks with unstable local dynamics.