Jan Thiele

Impact assessment revisited: improving the theoretical basis for management of invasive alien species

The theoretical underpinnings of the assessment of invasive alien species impacts need to be improved. At present most approaches are unreliable to quantify impact at regional scales and do not allow for comparison of different invasive species. There are four basic problems that need to be addressed: (1) Some impacted ecosystem traits are spatially not additive; (2) invader effects may increase non-linearly with abundance or there may be effect thresholds impairing estimates of linear impact models; (3) the abundance and impact of alien species will often co-vary with environmental variation; and (4) the total invaded range is an inappropriate measure for quantifying regional impact because the habitat area available for invasion can vary markedly among invasive species. Mathematical models and empirical data using an invasive alien plant species (Heracleum mantegazzianum) indicate that ignoring these issues leads to impact estimates almost an order of magnitude from the real values. Thus, we propose a habitat-sensitive formula for regional impact assessment that is unaffected by non-linearity. Furthermore, we make some statistical suggestions on how to assess invader effects properly and we discuss the quantification of the invaded range. These improvements are crucial for impact assessment with the overall aim of prioritizing management of invasive species.

Potential of GLMM in modelling invasive spread

Utilization of Generalized Linear Mixed Models (GLMM) in invasion biology has increased exponentially during the last 5-10 years. GLMM are useful tools that can handle data with various distributions as well as s patial or temporal dependence which are involved in many study designs. We review the c urrent state-of-the-art of GLMM with special focus on applications in invasion biol ogy. This review covers all steps of data analysis with GLMM. We address frequently encountered practical problems, such as failure of convergence, and put some emphasis on validation of model assumptions. Further, we point towards possibilities of analysin g zero-heavy data using combined GLMM. More detailed insight into practical applicat ions of GLMM is provided in three worked examples in the supplementary material. Regarding applications of GLMM in invasion biology, a literature analysis showed that random effects are mostly used to account for non-independence of observations due to study design, but rarely for estimation of random variation. There may be some potential in using random-effect estimation more consciously, like in some recent st udies of genetic variation of invasive species. Often, invasion biologists have to deal wi th count data or proportions. In such cases, several methods of parameter estimation are available, but their suitability depends on characteristics of the data at hand and, hence, they should be chosen carefully. Also repeated measures are common in invasion biology. In GLMM frameworks, the auto-correlation of such data can b e modelled by structured co-variance matrices. This opportunity, however, has seldom bee n used.

Genetic variation of inbreeding depression among floral and fitness traits in Silene nutans

The magnitude and variation of inbreeding depression (ID) within populations is important for the evolution and maintenance of mixed mating systems. We studied ID and its genetic variation in a range of floral and fitness traits in a small and large population of the perennial herb Silene nutans, using controlled pollinations in a fully factorial North Carolina II design. Floral traits and early fitness traits, that is seed mass and germination rate, were not much affected by inbreeding (δ<0.2). In contrast, ‘late’ fitness traits and multiplicative fitness suffered severely from inbreeding (δ>0.4). Lack of genetic correlations indicated that ID in floral, early and late traits is genetically decoupled. There was a trend that the smaller population was less affected by ID than the large one, although the differences were not significant for most traits. Hence, evidence for purging of deleterious alleles remains inconclusive in this study. Genetic variation in ID among paternal families was statistically significant in most floral and all seed traits, but not in late fitness traits. However, some paternal families had δ<0.5, even in the multiplicative fitness measure that suffered most from ID (δ=0.74), suggesting that the mixed mating system of S. nutans might be evolutionary stable.

Limited evidence for allelopathic effects of giant hogweed on germination of native herbs.

Invasive alien plants often occur in monospecific stands with high density in the invaded range. Production of bioactive secondary metabolites in such stands could have allelopathic effects on germination of native species. We tested this component of the novel weapon hypothesis for Heracleum mantegazzianum , a prominent invader in Europe, using seeds of eleven native herbs exposed to soil or soil extracts from invaded stands, moist seeds or seed extracts of Heracleum mantegazzianum . There was no effect of the various treatments on germination of most species, while germination was reduced in Urtica dioica on invaded soil, in Poa trivialis with Heracleum mantegazzianum seed extract, and negative effects of the essential oil bergapten were found in three species. In Poa trivialis the results of the seed extract were not supported by the experiment with added seeds of the invasive plant. Thus, there is limited evidence for allelopathic effects of the invasive Heracleum mantegazzianum on germination of co-occurring native herbs.

Heterogeneous environments shape invader impacts: integrating environmental, structural and functional effects by isoscapes and remote sensing

Spatial heterogeneity of ecosystems crucially influences plant performance, while in return plant feedbacks on their environment may increase heterogeneous patterns. This is of particular relevance for exotic plant invaders that transform native ecosystems, yet, approaches integrating geospatial information of environmental heterogeneity and plant-plant interaction are lacking. Here, we combined remotely sensed information of site topography and vegetation cover with a functional tracer of the N cycle, δ15N. Based on the case study of the invasion of an N2-fixing acacia in a nutrient-poor dune ecosystem, we present the first model that can successfully predict (R2 = 0.6) small-scale spatial variation of foliar δ15N in a non-fixing native species from observed geospatial data. Thereby, the generalized additive mixed model revealed modulating effects of heterogeneous environments on invader impacts. Hence, linking remote sensing techniques with tracers of biological processes will advance our understanding of the dynamics and functioning of spatially structured heterogeneous systems from small to large spatial scales.

Open-Source Processing and Analysis of Aerial Imagery Acquired with a Low-Cost Unmanned Aerial System to Support Invasive Plant Management

Remote sensing by Unmanned Aerial Systems (UAS) is a dynamic evolving technology. UAS are particularly useful in environmental monitoring and management because they have the capability to provide data at high temporal and spatial resolutions. Moreover, data acquisition costs are lower than those of conventional methods such as extensive ground sampling, manned airplanes, or satellites. Small fixed-wing UAS in particular offer further potential benefits as they extend the operational coverage of the area under study at lower operator risks and accelerate data deployment times. Taking these aspects into account, UAS might be an effective tool to support management of invasive plant based on early detection and regular monitoring. A straightforward UAS approach to map invasive plant species is presented in this study with the intention of providing ready-to-use field maps essential for action-oriented management. Our UAS utilizes low-cost sensors, free-of-charge software for mission planning and an affordable, commercial aerial platform to reduce operational costs, reducing expenses with personnel while increasing overall efficiency. We illustrate our approach using a real example of invasion by Acacia mangium in a Brazilian Savanna ecosystem. A. mangium was correctly identified with an overall accuracy of 82.7% from the analysis of imagery. This approach provides land management authorities and practitioners with new prospects for environmental restoration in areas where invasive plant species are present.

Interactive impacts of a herbivore and a pathogen on two resistance types of Barbarea vulgaris (Brassicaceae)

It is well known that pathogens and arthropod herbivores attacking the same host plant may affect each other. Little is known, however, about their combined impact on plant fitness, which may differ from simple additive expectations. In a 2-year common garden field experiment, we tested whether the pathogen Albugo sp. (white blister rust) and the herbivorous flea beetle Phyllotreta nemorum affected each other’s performance on two resistance types (G-type and P-type) of the crucifer Barbarea vulgaris ssp. arcuata, and whether biomass, reproduction and survival of the plants were affected by interactive impacts of the antagonists. Most of the insect-resistant G-plants were severely affected by white rust, which reduced biomass and reproductive potential compared to the controls. However, when also exposed to flea beetles, biomass loss was mitigated in G-plants, even though apparent disease symptoms were not reduced. Most of the insect-susceptible P-plants were resistant to white rust; however, the number of flea beetle mines tended to increase in plants also exposed to Albugo, and biomass at the last harvest was slightly lower in the combined treatment. Thus, interactive impacts of the herbivore and pathogen differed between the two resistance types, with an antagonistic combined impact in G-plants, which lasted surprisingly long, and a slight synergistic impact in P-plants.

Using resistance distance from circuit theory to model dispersal through habitat corridors

Aims Resistance distance (RD), based on circuit theory, is a promising metric for modelling effects of landscape configuration on dispersal of organisms and the resulting population and community patterns. The values of RD reflect the likelihood of a random walker to reach from a source to a certain destination in the landscape. Although it has successfully been used to model genetic structures of animal populations, where it most often outperforms other isolation metrics, there are hardly any applications to plants and, in particular, to plant community data. Our aims were to test if RD was a suitable metric for studying dispersal processes of plants in narrow habitat corridors (linear landscape elements [LLE]). This would be the case, if dispersal processes (seed dispersal and migration) resembled random walks. Further, we compared the model performance of RD against least‐cost distance (LCD) and Euclidean distance (ED). Finally, we tested the suitability of different cost surfaces for calculations of LCD and RD. Methods We used data from 50 vegetation plots located on semi‐natural LLE (field margins, ditches, road verges) in eight agricultural landscapes of Northwest Germany. We mapped LLE, including hedges and tree rows, from aerial images in a Geographic Information System, converted the maps into raster layers, and assigned resistance values to the raster cells, where all cells outside of LLE received infinite resistance and, thus, represented barriers to dispersal. For all pairs of plots within study areas, we calculated Jaccard similarity assuming that it was a proxy (or correlate) of dispersal events between plots. Further, we calculated RD and LCD of the network of LLE and ED between the plots. We modelled the effects of distance metrics on community similarity using binomial generalized linear mixed models. Important Findings ED was clearly the least suitable isolation metrics. Further, we found that RD performed better than LCD at modelling Jaccard similarity. Predictions varied markedly between the two distance metrics suggesting that RD comprises additional information about the landscape beyond spatial distance, such as the possible presence of multiple pathways between plots. Cost surfaces with equal cell‐level resistances for all types of LLE performed better than more complex ones with habitat‐specific resistances. We conclude that RD is a highly suitable measure of isolation or, inversely, connectivity for studying dispersal processes of plants within habitat corridors. It is likely also suitable for assessing landscape permeability in other landscape types with areal habitats instead of narrow corridors. RD holds the potential to improve assessments of isolation (or connectivity) for models of regional population and meta‐community dynamics.

Modelling invasion probability of giant hogweed (Heracleum mantegazzianum) with logistic GLMM.

Introduction In this worked example we model the invasion probability of an invasive plant species, giant hogweed (Heracleum mantegazzianum), based on field surveys of 20 study areas of 1 km2 which were situated in areas of Germany most invaded by this species (see Thiele et al. 2008 for more details). The statistical analyses will be conducted in R (R Development Core Team 2011) using the packages lme4 (version 0.999375-41; Bates et al. 2011), gof (0.7-6; Holst 2011), glmmML (0.81-8; Brostrom and Holmberg 2011), lattice (0.19-17; Sarkar 2008), and MuMIn (1.0.0; Barton 2011).

Soil pH and plant diversity shape soil bacterial community structure in the active layer across the latitudinal gradients in continuous permafrost region of Northeastern China

In the permafrost region of northeastern China, vegetation and soil environment have showed response to permafrost degradation triggered by global warming, but the corresponding variation of the soil microbial communities remains poorly investigated. Here, a field investigation in the continuous permafrost region was conducted to collect 63 soil samples from 21 sites along a latitudinal gradient to assess the distribution pattern of microbial communities and their correlation with environmental factors. High-throughput Illumina sequencing revealed that bacterial communities were dominated by Proteobacteria, Acidobacteria, Bacteroidetes and Actinobacteria. Both microbial richness and phylogenetic diversity decreased initially and then increased as the latitude increased. UniFrac analysis of microbial communities detected significant differences among latitudes. Variation partitioning analysis and structural equation models revealed that environmental variables, including geographic factors, plant-community factors and soil physicochemical factors, all played non-negligible roles in affecting the microbial community structures directly or indirectly. Redundancy analysis and boosted regression tree analysis further highlighted the influences of soil pH and plant richness on microbial community compositions and diversity patterns. Taken together, these results suggest that the distribution pattern of soil microbial communities shows distinct changes along the latitudinal gradients in northeastern China and is predominantly mediated by soil pH and plant diversity.