Niko Balkenhol

Bio: Niko Balkenhol is an academic researcher from University of Göttingen. The author has contributed to research in topics: Population & Landscape connectivity. The author has an hindex of 29, co-authored 81 publications receiving 3433 citations. Previous affiliations of Niko Balkenhol include Leibniz Association & University of Idaho.

Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis

Abstract: Measures of genetic structure among individuals or populations collected at different spatial locations across a landscape are commonly used as surrogate measures of functional (i.e. demographic or genetic) connectivity. In order to understand how landscape characteristics influence functional connectivity, resistance surfaces are typically created in a raster GIS environment. These resistance surfaces represent hypothesized relationships between landscape features and gene flow, and are based on underlying biological functions such as relative abundance or movement probabilities in different land cover types. The biggest challenge for calculating resistance surfaces is assignment of resistance values to different landscape features. Here, we first identify study objectives that are consistent with the use of resistance surfaces and critically review the various approaches that have been used to parameterize resistance surfaces and select optimal models in landscape genetics. We then discuss the biological assumptions and considerations that influence analyses using resistance surfaces, such as the relationship between gene flow and dispersal, how habitat suitability may influence animal movement, and how resistance surfaces can be translated into estimates of functional landscape connectivity. Finally, we outline novel approaches for creating optimal resistance surfaces using either simulation or computational methods, as well as alternatives to resistance surfaces (e.g. network and buffered paths). These approaches have the potential to improve landscape genetic analyses, but they also create new challenges. We conclude that no single way of using resistance surfaces is appropriate for every situation. We suggest that researchers carefully consider objectives, important biological assumptions and available parameterization and validation techniques when planning landscape genetic studies.

Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data

Abstract: The goal of landscape genetics is to detect and explain landscape effects on genetic diversity and structure. Despite the increasing popularity of landscape genetic approaches, the statistical methods for linking genetic and landscape data remain largely untested. This lack of method evaluation makes it difficult to compare studies utilizing different statistics, and compromises the future development and application of the field. To investigate the suitability and comparability of various statistical approaches used in landscape genetics, we simulated data sets corresponding to five landscape-genetic scenarios. We then analyzed these data with eleven methods, and compared the methods based on their statistical power, type-1 error rates, and their overall ability to lead researchers to accurate conclusions about landscape-genetic relationships. Results suggest that some of the most commonly applied techniques (e.g. Mantel and partial Mantel tests) have high type-1 error rates, and that multivariate, non-linear methods are better suited for landscape genetic data analysis. Furthermore, different methods generally show only moderate levels of agreement. Thus, analyzing a data set with only one method could yield method-dependent results, potentially leading to erroneous conclusions. Based on these findings, we give recommendations for choosing optimal combinations of statistical methods, and identify future research needs for landscape genetic data analyses.

Molecular road ecology: exploring the potential of genetics for investigating transportation impacts on wildlife

Abstract: Transportation infrastructures such as roads, railroads and canals can have major environmental impacts. Ecological road effects include the destruction and fragmentation of habitat, the interruption of ecological processes and increased erosion and pollution. Growing concern about these ecological road effects has led to the emergence of a new scientific discipline called road ecology. The goal of road ecology is to provide planners with scientific advice on how to avoid, minimize or mitigate negative environmental impacts of transportation. In this review, we explore the potential of molecular genetics to contribute to road ecology. First, we summarize general findings from road ecology and review studies that investigate road effects using genetic data. These studies generally focus only on barrier effects of roads on local genetic diversity and structure and only use a fraction of available molecular approaches. Thus, we propose additional molecular applications that can be used to evaluate road effects across multiple scales and dimensions of the biodiversity hierarchy. Finally, we make recommendations for future research questions and study designs that would advance molecular road ecology. Our review demonstrates that molecular approaches can substantially contribute to road ecology research and that interdisciplinary, long-term collaborations will be particularly important for realizing the full potential of molecular road ecology.

Identifying future research needs in landscape genetics: Where to from here?

Abstract: Landscape genetics is an emerging interdisciplinary field that combines methods and concepts from population genetics, landscape ecology, and spatial statistics. The interest in landscape genetics is steadily increasing, and the field is evolving rapidly. We here outline four major challenges for future landscape genetic research that were identified during an international landscape genetics workshop. These challenges include (1) the identification of appropriate spatial and temporal scales; (2) current analytical limitations; (3) the expansion of the current focus in landscape genetics; and (4) interdisciplinary communication and education. Addressing these research challenges will greatly improve landscape genetic applications, and positively contribute to the future growth of this promising field.

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

Diversity of life

Abstract: It is clear that the above can lead to confusion when scientists of different countries are trying to communicate with each other. Another example is the burrowing rodent called a gopher found throughout the western United States. In the southeastern United States the term gopher refers to a burrowing turtle very similar to the desert tortoise found in the American southwest. One final example; two North American mammals known as the elk and the caribou are known in Europe as the reindeer and the elk. We never sing “Rudolph the Red-nosed elk”! Confused? This was the reason for creating an internationally recognized system of naming organisms. To avoid confusion, living organisms are assigned a scientific name based on Latin or Latinized words. The English sparrow is Passer domesticus or Passer domesticus (italics or underlining these two names is the official written representation of a scientific name). Using a uniform naming system allows scientists from all over the world to recognize exactly which life form a scientist is referring to. The naming process is called the binomial system of nomenclature. Passer is comparable to a surname and is called the genus, while domesticus is the specific or species name (like your given name) of the English sparrow. Now scientists can give all sparrow-like birds the genus Passer but the species name will vary. All similar genera (plural for genus) can be grouped into another, “higher” category (see below). Study the following for a more through understanding of taxonomy. Taxonomy Analogy Kingdom: Animalia Country

Refugia: identifying and understanding safe havens for biodiversity under climate change

Abstract: Aim Identifying and protecting refugia is a priority for conservation under projected anthropogenic climate change, because of their demonstrated ability to facilitate the survival of biota under adverse conditions. Refugia are habitats that components of biodiversity retreat to, persist in and can potentially expand from under changing environmental conditions. However, the study and discussion of refugia has often been ad hoc and descriptive in nature. We therefore: (1) provide a habitat-based concept of refugia, and (2) evaluate methods for the identification of refugia. Location Global. Methods We present a simple conceptual framework for refugia and examine the factors that describe them. We then demonstrate how different disciplines are contributing to our understanding of refugia, and the tools that they provide for identifying and quantifying refugia. Results Current understanding of refugia is largely based on Quaternary phylogeographic studies on organisms in North America and Europe during significant temperature fluctuations. This has resulted in gaps in our understanding of refugia, particularly when attempting to apply current theory to forecast anthropogenic climate change. Refugia are environmental habitats with space and time dimensions that operate on evolutionary time-scales and have facilitated the survival of biota under changing environmental conditions for millennia. Therefore, they offer the best chances for survival under climate change for many taxa, making their identification important for conservation under anthropogenic climate change. Several methods from various disciplines provide viable options for achieving this goal. Main conclusions The framework developed for refugia allows the identification and description of refugia in any environment. Various methods provide important contributions but each is limited in scope; urging a more integrated approach to identify, define and conserve refugia. Such an approach will facilitate better understanding of refugia and their capacity to act as safe havens under projected anthropogenic climate change.

SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses

Abstract: Summary 1. Species distribution models (SDMs) are broadly used in ecological and evolutionary studies. Almost all SDM methods require extensive data preparation in a geographic information system (GIS) prior to model building. Often, this step is cumbersome and, if not properly done, can lead to poorly parameterized models or in some cases, if too difficult, prevents the realization of SDMs. Further, for many studies, the creation of SDMs is not the final result and the post-modelling processing can be equally arduous as other steps. 2. SDMtoolbox is designed to facilitate many complicated pre- and post-processing steps commonly required for species distribution modelling and other geospatial analyses. SDMtoolbox consists of 59 Python script-based GIS tools developed and compiled into a single interface. 3. A large set of the tools were created to complement SDMs generated in Maxent or to improve the predictive performance of SDMs created in Maxent. However, SDMtoolbox is not limited to analyses of Maxent models, and many tools are also available for additional analyses or general geospatial processing: for example, assessing landscape connectivity of haplotype networks (using least-cost corridors or least-cost paths); correcting SDM over-prediction; quantifying distributional changes between current and future SDMs; or for calculating several biodiversity metrics, such as corrected weighted endemism. 4. SDMtoolbox is a free comprehensive python-based toolbox for macroecology, landscape genetic and evolutionary studies to be used in ArcGIS 10.1 (or higher) with the Spatial Analyst extension. The toolkit simplifies many GIS analyses required for species distribution modelling and other analyses, alleviating the need for repetitive and time-consuming climate data pre-processing and post-SDM analyses.