Refine
Year of publication
Document Type
- Article (132)
- Postprint (12)
- Conference Proceeding (4)
- Review (3)
- Other (2)
Keywords
- Individual-based model (5)
- Individual-based models (5)
- climate change (5)
- plant functional trait (5)
- Climate change (3)
- hoverflies (3)
- intraspecific trait variation (3)
- landscape homogenization (3)
- syrphids (3)
- wild bees (3)
Institute
- Institut für Biochemie und Biologie (153) (remove)
Plants located adjacent to agricultural fields are important for maintaining biodiversity in semi-natural landscapes. To avoid undesired impacts on these plants due to herbicide application on the arable fields, regulatory risk assessments are conducted prior to registration to ensure proposed uses of plant protection products do not present an unacceptable risk. The current risk assessment approach for these non-target terrestrial plants (NTTPs) examines impacts at the individual-level as a surrogate approach for protecting the plant community due to the inherent difficulties of directly assessing population or community level impacts. However, modelling approaches are suitable higher tier tools to upscale individual-level effects to community level. IBC-grass is a sophisticated plant community model, which has already been applied in several studies. However, as it is a console application software, it was not deemed sufficiently user-friendly for risk managers and assessors to be conveniently operated without prior expertise in ecological models. Here, we present a user-friendly and open source graphical user interface (GUI) for the application of IBC-grass in regulatory herbicide risk assessment. It facilitates the use of the plant community model for predicting long-term impacts of herbicide applications on NTTP communities. The GUI offers two options to integrate herbicide impacts: (1) dose responses based on current standard experiments (acc. to testing guidelines) and (2) based on specific effect intensities. Both options represent suitable higher tier options for future risk assessments of NTTPs as well as for research on the ecological relevance of effects.
Environmental heterogeneity is a major determinant of plant population dynamics. In semi-arid Kalahari savannas, heterogeneity is created by savanna structure, i.e. by the spatial arrangement and temporal dynamics of woody plant and open grassland microsites. We formulate a conceptual model describing the effects of savanna dynamics on the population dynamics of the animal-dispersed shrub Grewia flava. From empirical results we derive model rules describing effects of savanna structure on several processes in Grewia's life cycle. By formulating the model, we summarise existing information on Grewia demography and identify gaps in this knowledge. Despite a number of such gaps, the model can be used to make certain quantitative predictions. As an example, we apply the model to investigate the role of seed dispersal in Grewia encroachment on rangelands. Model results show that cattle promote encroachment by depositing substantial numbers of seeds in open areas, where Grewia is otherwise dispersal-limited. Finally, we draw some general conclusions about Grewia's life history and population dynamics. Under natural conditions, concentrated seed deposition under woody plants appears to be a key process causing the observed association between Grewia and other woody plants. Furthermore, low rates of recruitment and high adult survival result in slow-motion dynamics of Grewia populations. As a consequence, Grewia populations interact with savanna dynamics on long temporal and short to intermediate spatial scales.
Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N̂ area
) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂ area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂ area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂ area >1,000, where 30% had an N̂ area <30. In this frequently encountered scenario of small N̂ area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.