Jan Hjort

Why geodiversity matters in valuing nature's stage

Geodiversity--the variability of Earths surface materials, forms, and physical processes-is an integral part of nature and crucial for sustaining ecosystems and their services. It provides the substrates, landform mosaics, and dynamic physical processes for habitat development and maintenance. By determining the heterogeneity of the physical environment in conjunction with climate interactions, geodiversity has a crucial influence on biodiversity across a wide range of scales. From a literature review, we identified the diverse values of geodiversity; examined examples of the dependencies of biodiversity on geodiversity at a site-specific scale (for geosites <1 km(2) in area); and evaluated various human-induced threats to geosites and geodiversity. We found that geosites are important to biodiversity because they often support rare or unique biota adapted to distinctive environmental conditions or create a diversity of microenvironments that enhance species richness. Conservation of geodiversity in the face of a range of threats is critical both for effective management of natures stage and for its own particular values. This requires approaches to nature conservation that integrate climate, biodiversity, and geodiversity at all spatial scales.

Modelling patterned ground distribution in Finnish Lapland: an integration of topographical, ground and remote sensing information

Abstract New data technologies and modelling methods have gained more attention in the field of periglacial geomorphology during the last decade. In this paper we present a new modelling approach that integrates topographical, ground and remote sensing information in predictive geomorphological mapping using generalized additive modelling (GAM). First, we explored the roles of different environmental variable groups in determining the occurrence of non‐sorted and sorted patterned ground in a fell region of 100 km2 at the resolution of 1 ha in northern Finland. Second, we compared the predictive accuracy of ground‐topography‐ and remote‐sensing‐based models. The results indicate that non‐sorted patterned ground is more common at lower altitudes where the ground moisture and vegetation abundance is relatively high, whereas sorted patterned ground is dominant at higher altitudes with relatively high slope angle and sparse vegetation cover. All modelling results were from good to excellent in model evaluation data using the area under the curve (AUC) values, derived from receiver operating characteristic (ROC) plots. Generally, models built with remotely sensed data were better than ground‐topography‐based models and combination of all environmental variables improved the predictive ability of the models. This paper confirms the potential utility of remote sensing information for modelling patterned ground distribution in subarctic landscapes.

Inferring the effects of potential dispersal routes on the metacommunity structure of stream insects: as the crow flies, as the fish swims or as the fox runs?

1. Metacommunity research relies largely on proxies for inferring the effect of dispersal on local community structure. Overland and watercourse distances have been typically used as such proxies. A good proxy for dispersal should, however, take into account more complex landscape features that can affect an organisms movement and dispersal. The cost distance approach does just that, allowing determining the path of least resistance across a landscape. 2. Here, we examined the distance decay of assemblage similarity within a subarctic stream insect metacommunity. We tested whether overland, watercourse and cumulative cost distances performed differently as correlates of dissimilarity in assemblage composition between sites. We also investigated the effect of body size and dispersal mode on metacommunity organization. 3. We found that dissimilarities in assemblage composition correlated more strongly with environmental than physical distances between sites. Overland and watercourse distances showed similar correlations to assemblage dissimilarity between sites, being sometimes significantly correlated with biological variation of entire insect communities. In metacommunities deconstructed by body size or dispersal mode, contrary to our expectation, passive dispersers showed a slightly stronger correlation than active dispersers to environmental differences between sites, although passive dispersers also showed a stronger correlation than active dispersers to physical distances between sites. The strength of correlation between environmental distance and biological dissimilarity also varied slightly among the body size classes. 4. After controlling for environmental differences between sites, cumulative cost distances were slightly better correlates of biological dissimilarities than overland or watercourse distances between sites. However, quantitative differences in correlation coefficients were small between different physical distances. 5. Although environmental differences typically override physical distances as determinants of the composition of stream insect assemblages, correlations between environmental distances and biological dissimilarities are typically rather weak. This undetermined variation may be attributable to dispersal processes, which may be captured using better proxies for the process. We suggest that further modifying the measurement of cost distances may be a fruitful avenue, especially if complemented by more direct natural history information on insect dispersal behaviour and distances travelled by them.

Statistical consensus methods for improving predictive geomorphology maps

A variety of predictive models is currently used to map the spatial distribution of earth surface processes and landforms. In this study, we tested statistical consensus methods in order to improve the predictive accuracy of geomorphological models. The distributions of 12 geomorphological formations were recorded at a resolution of 25ha in a sub-arctic landscape in northern Finland. Nine environmental variables were used to predict probabilities of occurrence of the formations using eight state-of-the-art modelling techniques. The probability values of the models were combined using four different consensus methods. The accuracy of the models was calculated using spatially independent test data by the area under the curve (AUC) of a receiver-operating characteristic (ROC) plot. The mean AUC values of the geomorphological models varied between 0.711 and 0.755 based on single-model techniques, whereas the corresponding values based on consensus methods ranged from 0.752 to 0.782. The weighted average consensus method had the highest predictive performance of all methods. It improved the accuracy of 11 predictions out of 12. The results of this study suggest that the consensus methods have clear advantages over single-model predictions. The simplicity of the consensus methods makes it straightforward to implement them in predictive modelling studies in geomorphology.

Land use impacts on trace metal concentrations of suburban stream sediments in the Helsinki region, Finland

The purpose of this study was to statistically analyze the effect of different suburban land use types on trace metal contamination of suburban streams. Acid extractable metal concentrations (Cu, Zn, Pb, Cd) of stream bed sediments (<63 μm) and high-flow suspended sediments were determined for 61 suburban and six baseline catchments in the Helsinki metropolitan region, Finland. Our results showed that the average suburban metal concentrations were 3-5-fold compared to baseline values for stream bed sediments and 2-9-fold for suspended sediments. Correlation analyses revealed moderate relationships between the land use parameters of the contributing catchment and the metal concentrations. Metals, particularly Zn and Cu, were most strongly correlated with proportions of dense suburban land use and imperviousness. In addition, industrial land use appeared to be particularly important for describing the variations of suspended sediment metal concentrations. Our findings present statistical evidence that the intensity of urbanization and industrial land use provides an indication of metal contamination even within the suburban environment.

Assessing spatial uncertainty in predictive geomorphological mapping: A multi-modelling approach

Maps of earth surface processes and the potential distribution of landforms make an important contribution to theoretical and applied geomorphology. Because decision making often depends on information based on spatial models, there is a great need to develop methodology to evaluate the spatial uncertainty resulting from those models. In this study we developed a new method to produce maps of the uncertainty of predictions provided by ten state-of-the-art modelling techniques for sorted (SP) and non-sorted (NSP) patterned ground in subarctic Finland at a 1.0-ha resolution. Six uncertainty classes represent the modelling agreement between the different modelling techniques. The resulting uncertainty maps reflect the reliability of the estimates for the studied periglacial landforms in the modelled area. Our results showed a significant negative correlation between the degree of uncertainty and the accuracy of the modelling techniques. On average, when all ten models agreed, the mean area under the curve (AUC) values were 0.904 (NSP) and 0.896 (SP), these values decreased to 0.416 (NSP) and 0.518 (SP), respectively, when only five models agreed. Mapping of the uncertainty of predictions in geomorphology can help scientists to improve the reliability of their data and modelling results. The predictive maps can be interpreted simultaneously with the uncertainty information, improving understanding of the potential pitfalls of the modelling.

Fine-Scale Exposure to Allergenic Pollen in the Urban Environment: Evaluation of Land Use Regression Approach

Background: Despite the recent developments in physically and chemically based analysis of atmospheric particles, no models exist for resolving the spatial variability of pollen concentration at urban scale. Objectives: We developed a land use regression (LUR) approach for predicting spatial fine-scale allergenic pollen concentrations in the Helsinki metropolitan area, Finland, and evaluated the performance of the models against available empirical data. Methods: We used grass pollen data monitored at 16 sites in an urban area during the peak pollen season and geospatial environmental data. The main statistical method was generalized linear model (GLM). Results: GLM-based LURs explained 79% of the spatial variation in the grass pollen data based on all samples, and 47% of the variation when samples from two sites with very high concentrations were excluded. In model evaluation, prediction errors ranged from 6% to 26% of the observed range of grass pollen concentrations. Our findings support the use of geospatial data–based statistical models to predict the spatial variation of allergenic grass pollen concentrations at intra-urban scales. A remote sensing–based vegetation index was the strongest predictor of pollen concentrations for exposure assessments at local scales. Conclusions: The LUR approach provides new opportunities to estimate the relations between environmental determinants and allergenic pollen concentration in human-modified environments at fine spatial scales. This approach could potentially be applied to estimate retrospectively pollen concentrations to be used for long-term exposure assessments. Citation: Hjort J, Hugg TT, Antikainen H, Rusanen J, Sofiev M, Kukkonen J, Jaakkola MS, Jaakkola JJ. 2016. Fine-scale exposure to allergenic pollen in the urban environment: evaluation of land use regression approach. Environ Health Perspect 124:619–626; http://dx.doi.org/10.1289/ehp.1509761

Hierarchical decomposition of trait patterns of macroinvertebrate communities in subarctic streams

Ecological research based on both species and their traits helps us understand the mechanisms structuring ecological communities. Our aim was to dismantle the effects of environmental variables measured at multiple spatial scales on the taxonomic and functional trait composition of benthic macroinvertebrate communities and to clarify the relationship between the environment and communities in high-latitude streams. Traits were combined into unique trait combinations (site-by-traits matrix), called hereafter the overall trait composition of macroinvertebrate communities, and then the matrix was decomposed into progressively smaller parts of individual traits (site-by-individual trait matrix). The effects of variables from different spatial scales on the variation in the overall trait matrix, decomposed trait matrices, and taxonomic data were analyzed using redundancy analysis and partial linear regression modeling. Our analyses indicated that: 1) the taxonomic composition of communities was more closely associated with factors measured at larger spatial scales, and the trait composition of communities was more closely associated with factors measured at smaller spatial scales, even within 1 drainage basin; 2) decomposing overall trait composition to its individual components of single traits revealed important patterns related to the potential causal factors; and 3) the abundances of organisms exhibiting different traits may be linked strongly to different environmental variables operating at different spatial scales. Our findings highlight the benefits of describing both the taxonomic and trait composition of communities when exploring the drivers of community composition. They also have direct applications in monitoring the vulnerability of high-latitude streams to future environmental changes.

Extreme urban–rural temperatures in the coastal city of Turku, Finland: Quantification and visualization based on a generalized additive model

Fundamental knowledge on the determinants of air temperatures across spatial and temporal scales is essential in climate change mitigation and adaptation. Spatial-based statistical modelling provides an efficient approach for the analysis and prediction of air temperatures in human-modified environments at high spatial accuracy. The aim of the study was firstly, to analyse the environmental factors affecting extreme air temperature conditions in a coastal high-latitude city and secondly, to explore the applicability of generalized additive model (GAM) in the study of urban-rural temperatures. We utilized air temperature data from 50 permanent temperature logger stations and extensive geospatial environmental data on different scales from Turku, SW Finland. We selected five temperature situations (cases) and altogether 12 urban and natural explanatory variables for the analyses. The results displayed that (i) water bodies and topographical conditions were often more important than urban variables in controlling the spatial variability of extreme air temperatures, (ii) case specificity of the explanatory variables and their scales should be considered in the analyses and (iii) GAM was highly suitable in quantifying and visualizing the relations between urban-rural temperatures and environmental determinants at local scales. The results promote the use of GAMs in spatial-based statistical modelling of air temperature in future.

Predicting occupancy and abundance by niche position, niche breadth and body size in stream organisms

The regional occupancy and local abundance of species are thought to be strongly correlated to their body size, niche breadth and niche position. The strength of the relationships among these variables can also differ between different organismal groups. Here, we analyzed data on stream diatoms and insects from a high-latitude drainage basin to investigate these relationships. To generate measures of niche position and niche breadth for each species, we used sets of local environmental and catchment variables separately, applying the outlying mean index analysis. Beta regression and negative binomial generalized linear models were run to predict regional occupancy and mean local abundance, respectively. We found a positive occupancy–abundance relationship in both diatoms and insects, and that niche-based variables were the main predictors of variation in regional occupancy and local abundance. This finding was mainly due to local environmental niche position, whereas the effects of niche breadth on regional occupancy and local abundance were less important. We also found a relationship between body size and local abundance or regional occupancy of diatoms. Our results thus add to current macroecological research by emphasizing the strong importance of niche position rather than niche breadth and body size for regional occupancy and local abundance in rarely studied organisms (e.g., diatoms and insects) and ecosystems (i.e., wilderness streams).