Miska Luoto

Methods and uncertainties in bioclimatic envelope modelling under climate change

Potential impacts of projected climate change on biodiversity are often assessed using single-species bioclimatic ‘envelope’models. Such models are a special case of species distribution models in which the current geographical distribution of species is related to climatic variables so to enable projections of distributions under future climate change scenarios. This work reviews a number of critical methodological issues that may lead to uncertainty in predictions from bioclimatic modelling. Particular attention is paid to recent developments of bioclimatic modelling that address some of these issues as well as to the topics where more progress needs to be made. Developing and applying bioclimatic models in a informative way requires good understanding of a wide range of methodologies, including the choice of modelling technique, model validation, collinearity, autocorrelation, biased sampling of explanatory variables, scaling and impacts of non-climatic factors. A key challenge for future research is integrating factors such as land cover, direct CO2 effects, biotic interactions and dispersal mechanisms into species-climate models. We conclude that, although bioclimatic envelope models have a number of important advantages, they need to be applied only when users of models have a thorough understanding of their limitations and uncertainties.

Remotely Sensed Spectral Heterogeneity As a Proxy of Species Diversity: Recent Advances and Open Challenges

Abstract Environmental heterogeneity is considered to be one of the main factors associated with biodiversity given that areas with highly heterogeneous environments can host more species due to their higher number of available niches. In this view, spatial variability extracted from remotely sensed images has been used as a proxy of species diversity, as these data provide an inexpensive means of deriving environmental information for large areas in a consistent and regular manner. The aim of this review is to provide an overview of the state of the art in the use of spectral heterogeneity for estimating species diversity. We will examine a number of issues related to this theme, dealing with: i) the main sensors used for biodiversity monitoring, ii) scale matching problems between remotely sensed and field diversity data, iii) spectral heterogeneity measurement techniques, iv) types of species taxonomic diversity measures and how they influence the relationship between spectral and species diversity, v) spectral versus genetic diversity, and vi) modeling procedures for relating spectral and species diversity. Our review suggests that remotely sensed spectral heterogeneity information provides a crucial baseline for rapid estimation or prediction of biodiversity attributes and hotspots in space and time.

Loss of plant species richness and habitat connectivity in grasslands associated with agricultural change in Finland.

Abstract The drastic loss of seminatural grasslands and the decrease in species diversity in Europe during the 20th century are closely linked to social-economic factors. Development in agricultural production drives land-use changes, and thus controls the capacity of landscapes to maintain biodiversity. In this study, we link agricultural production changes to landscape fragmentation and species diversity. Our results show that the termination of grazing on seminatural grassland caused significant changes in landscape structure and a decline in the number of vascular plant species. The decline of grazed grasslands has been driven mainly by farm-level economic efficiency and profitability interests, which have been connected with agricultural policy measures. Since 1995, when Finland joined the European Union, the area of grazed patches in our study area has again increased as a result of a support scheme for the management of seminatural grasslands.

Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe

Recent studies from mountainous areas of small spatial extent (<2500 km(2) ) suggest that fine-grained thermal variability over tens or hundreds of metres exceeds much of the climate warming expected for the coming decades. Such variability in temperature provides buffering to mitigate climate-change impacts. Is this local spatial buffering restricted to topographically complex terrains? To answer this, we here study fine-grained thermal variability across a 2500-km wide latitudinal gradient in Northern Europe encompassing a large array of topographic complexities. We first combined plant community data, Ellenberg temperature indicator values, locally measured temperatures (LmT) and globally interpolated temperatures (GiT) in a modelling framework to infer biologically relevant temperature conditions from plant assemblages within <1000-m(2) units (community-inferred temperatures: CiT). We then assessed: (1) CiT range (thermal variability) within 1-km(2) units; (2) the relationship between CiT range and topographically and geographically derived predictors at 1-km resolution; and (3) whether spatial turnover in CiT is greater than spatial turnover in GiT within 100-km(2) units. Ellenberg temperature indicator values in combination with plant assemblages explained 46-72% of variation in LmT and 92-96% of variation in GiT during the growing season (June, July, August). Growing-season CiT range within 1-km(2) units peaked at 60-65°N and increased with terrain roughness, averaging 1.97 °C (SD = 0.84 °C) and 2.68 °C (SD = 1.26 °C) within the flattest and roughest units respectively. Complex interactions between topography-related variables and latitude explained 35% of variation in growing-season CiT range when accounting for sampling effort and residual spatial autocorrelation. Spatial turnover in growing-season CiT within 100-km(2) units was, on average, 1.8 times greater (0.32 °C km(-1) ) than spatial turnover in growing-season GiT (0.18 °C km(-1) ). We conclude that thermal variability within 1-km(2) units strongly increases local spatial buffering of future climate warming across Northern Europe, even in the flattest terrains.

Prediction of total and rare plant species richness in agricultural landscapes from satellite images and topographic data

The diversity of future landscapes might depend on our ability to predict their potential species richness. The predictability of patterns of vascular plant species richness in a Finnish agricultural river landscape was studied using generalized linear modeling, floristic records from fifty-three0.25-km grid squares in the “core” study area, and environmental variables derived from Landsat TM images and a digital elevation model. We built multiple regression models for the total number of plant species and the number of rarities, and validated the accuracy of the derived models with a test set of 52 grid squares. We tentatively extrapolated the models from the core study area to the whole study area of 601 km2 and produced species richness probability maps using GIS techniques. The results suggest that the local ‘hotspots’ of total flora (grid squares with > 200species) are mainly found in river valleys, where habitat diversity is high and a semi-open agricultural-forest mosaic occurs. The ‘hotspots’ of rare species (grid squares with > 4 rare species) are also found in river valleys, in sites where extensive semi-natural grasslands and herb-rich deciduous forests occur on steep slopes. We conclude that environmental variables derived from satellite images and topographic data can be used as approximate surrogates of plant species diversity in agricultural landscapes. Modeling of biological diversity based on satellite images and GIS can provide useful information needed in land use planning. However, due to the potential pitfalls in processing satellite imagery and model-building procedures, the results of predictive models should be carefully interpreted.

Decline of landscape-scale habitat and species diversity after the end of cattle grazing

Abstract A decrease of habitat and species diversity in agricultural landscapes, mainly as a result of the decline of semi-natural grasslands, has been shown in several studies. However, no studies have linked the effects of decrease of grassland management with landscape structure and plant and bird species diversity on the landscape scale in a spatial grid system. In this study we examined the differences in the present habitat and species diversity (number of total and rare plant and bird species) among agricultural landscapes differing in their management history. We compared areas of 0.25 km2 (n = 34) with different grazing history in the Rekijoki river valley, SW Finland. The grazed area decreased to one fifth over 30 years (1960–1990) in our study area. The earlier interconnected network of grazed patches was disrupted, resulting in an isolated grazing pattern. There were statistical differences in the habitat structure and plant species diversity between the landscapes with different management histories, but no difference in bird diversity was observed. The number of rare plant species/0.25 km2 was 45% less in areas of 20–40 years of abandonment compared to squares with continuously grazed patches. The results address the importance of grazing management for maintaining heterogeneous habitat mosaics and plant diversity on the landscape scale.

An Empirical Test of a Diffusion Model: Predicting Clouded Apollo Movements in a Novel Environment

Functional connectivity is a fundamental concept in conservation biology because it sets the level of migration and gene flow among local populations. However, functional connectivity is difficult to measure, largely because it is hard to acquire and analyze movement data from heterogeneous landscapes. Here we apply a Bayesian state‐space framework to parameterize a diffusion‐based movement model using capture‐recapture data on the endangered clouded apollo butterfly. We test whether the model is able to disentangle the inherent movement behavior of the species from landscape structure and sampling artifacts, which is a necessity if the model is to be used to examine how movements depend on landscape structure. We show that this is the case by demonstrating that the model, parameterized with data from a reference landscape, correctly predicts movements in a structurally different landscape. In particular, the model helps to explain why a movement corridor that was constructed as a management measure failed to increase movement among local populations. We illustrate how the parameterized model can be used to derive biologically relevant measures of functional connectivity, thus linking movement data with models of spatial population dynamics.

Loss of palsa mires in Europe and biological consequences

Palsa mires are northern mire complexes with permanently frozen peat hummocks. These are degrading throughout their distribution range, probably because of regional climatic warming. This review of the current understanding of the geographical, climatic and biological characteristics of palsa mires focuses on Europe. Recent studies have reported a drastic decrease in the extent of palsa mires in Fennoscandia; in Finland, the distribution of palsas was formerly about three times that at present. With continued or accelerated warming, as predicted for high latitudes, further extensive degradation or the wholesale disappearance of palsa mires seems inevitable. Palsa mires are known to be biologically heterogeneous environments with a rich diversity of bird species, and they are listed as a priority habitat type by the European Union. However, their role as habitats for other organisms is still poorly understood. There is urgent need for research and monitoring to assess the ecological and biological consequences of the decline of palsa mires in Europe.

Predicting range expansion of the map butterfly in Northern Europe using bioclimatic models

The two main goals of this study are: (i) to examine the range shifts of a currently northwards expanding species, the map butterfly (Araschnia levana), in relation to annual variation in weather, and (ii) to test the capability of a bioclimatic envelope model, based on broad-scale European distribution data, to predict recent distributional changes (2000–2004) of the species in Finland. A significant relationship between annual maximum dispersal distance of the species and late summer temperature was detected. This suggests that the map butterfly has dispersed more actively in warmer rather than cooler summers, the most notable dispersal events being promoted by periods of exceptionally warm weather and southerly winds. The accuracy of the broad-scale bioclimatic model built for the species with European data using Generalized Additive Models (GAM) was good based on split-sample evaluation for a single period. However, the model’s performance was poor when applied to predict range shifts in Finland. Among the many potential explanations for the poor success of the transferred bioclimatic model, is the fact that bioclimatic envelope models do not generally account for species dispersal. This and other uncertainties support the view that bioclimatic models should be applied with caution when they are used to project future range shifts of species.

Soil moisture's underestimated role in climate change impact modelling in low‐energy systems

Shifts in precipitation regimes are an inherent component of climate change, but in low-energy systems are often assumed to be less important than changes in temperature. Because soil moisture is the hydrological variable most proximally linked to plant performance during the growing season in arctic-alpine habitats, it may offer the most useful perspective on the influence of changes in precipitation on vegetation. Here we quantify the influence of soil moisture for multiple vegetation properties at fine spatial scales, to determine the potential importance of soil moisture under changing climatic conditions. A fine-scale data set, comprising vascular species cover and field-quantified ecologically relevant environmental parameters, was analysed to determine the influence of soil moisture relative to other key abiotic predictors. Soil moisture was strongly related to community composition, species richness and the occurrence patterns of individual species, having a similar or greater influence than soil temperature, pH and solar radiation. Soil moisture varied considerably over short distances, and this fine-scale heterogeneity may contribute to offsetting the ecological impacts of changes in precipitation for species not limited to extreme soil moisture conditions. In conclusion, soil moisture is a key driver of vegetation properties, both at the species and community level, even in this low-energy system. Soil moisture conditions represent an important mechanism through which changing climatic conditions impact vegetation, and advancing our predictive capability will therefore require a better understanding of how soil moisture mediates the effects of climate change on biota.