Maarten J. van Strien

A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models

Landscape genetics aims to assess the effect of the landscape on intraspecific genetic structure. To quantify interdeme landscape structure, landscape genetics primarily uses landscape resistance surfaces (RSs) and least‐cost paths or straight‐line transects. However, both approaches have drawbacks. Parameterization of RSs is a subjective process, and least‐cost paths represent a single migration route. A transect‐based approach might oversimplify migration patterns by assuming rectilinear migration. To overcome these limitations, we combined these two methods in a new landscape genetic approach: least‐cost transect analysis (LCTA). Habitat‐matrix RSs were used to create least‐cost paths, which were subsequently buffered to form transects in which the abundance of several landscape elements was quantified. To maintain objectivity, this analysis was repeated so that each landscape element was in turn regarded as migration habitat. The relationship between explanatory variables and genetic distances was then assessed following a mixed modelling approach to account for the nonindependence of values in distance matrices. Subsequently, the best fitting model was selected using the statistic. We applied LCTA and the mixed modelling approach to an empirical genetic dataset on the endangered damselfly, Coenagrion mercuriale. We compared the results to those obtained from traditional least‐cost, effective and resistance distance analysis. We showed that LCTA is an objective approach that identifies both the most probable migration habitat and landscape elements that either inhibit or facilitate gene flow. Although we believe the statistical approach to be an improvement for the analysis of distance matrices in landscape genetics, more stringent testing is needed.

How to make landscape genetics beneficial for conservation management

Many landscape genetic studies promise results that can be applied in conservation management. However, only few landscape genetic studies have been used by practitioners. Here, we identified scientific topics in landscape genetics that need to be addressed before results can more successfully be applied in conservation management. For each topic, weaknesses of common practice in landscape genetic analysis are described by presenting examples from current studies and further recommendations for improvements are outlined. First, we suggest matching the extent of the study area with those of conservation management units and the study species’ dispersal potential when designing landscape genetic studies. Second, the quality of the underlying statistical models should be optimised, and models should include variables that are useful for management implementation. Third, to further improve the applicability of landscape genetic studies, thresholds for landscape effects on gene flow should be identified. Fourth, landscape genetic models could be used for the development of conservation planning tools, which ideally also incorporate the above described thresholds. Fifth and as discussed in earlier studies, the use of multiple species and replication at the landscape scale is recommended. Although it appears that only few landscape genetic studies have been applied in practical management until now, examples presented in this article show that landscape genetic methods can provide important information to formulate concrete management implications. Thus, addressing the above-mentioned scientific topics in landscape genetic studies would enhance the benefits of their results for practitioners.

Landscape genetics as a tool for conservation planning: predicting the effects of landscape change on gene flow

For conservation managers, it is important to know whether landscape changes lead to increasing or decreasing gene flow. Although the discipline of landscape genetics assesses the influence of landscape elements on gene flow, no studies have yet used landscape-genetic models to predict gene flow resulting from landscape change. A species that has already been severely affected by landscape change is the large marsh grasshopper (Stethophyma grossum), which inhabits moist areas in fragmented agricultural landscapes in Switzerland. From transects drawn between all population pairs within maximum dispersal distance (< 3 km), we calculated several measures of landscape composition as well as some measures of habitat configuration. Additionally, a complete sampling of all populations in our study area allowed incorporating measures of population topology. These measures together with the landscape metrics formed the predictor variables in linear models with gene flow as response variable (F(ST) and mean pairwise assignment probability). With a modified leave-one-out cross-validation approach, we selected the model with the highest predictive accuracy. With this model, we predicted gene flow under several landscape-change scenarios, which simulated construction, rezoning or restoration projects, and the establishment of a new population. For some landscape-change scenarios, significant increase or decrease in gene flow was predicted, while for others little change was forecast. Furthermore, we found that the measures of population topology strongly increase model fit in landscape genetic analysis. This study demonstrates the use of predictive landscape-genetic models in conservation and landscape planning.

An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations.

Abstract Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer‐generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape‐level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class‐ and patch‐level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user‐defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.

Reducing the loss of agricultural productivity due to compact urban development in municipalities of Switzerland

Abstract Globally urban growth destroys fertile soils and endangers food security. Fertile soils are often located in the vicinity of existing urban areas. Thus, preserving high-quality soils can conflict with the objective of developing compact urban patterns. In this study, we assess the trade-off between compact urban patterns and urban patterns that can help reduce the loss of agricultural productivity by maintaining fertile agricultural soils. We assess the trade-offs for selected municipalities in Switzerland using a multi-objective evolutionary algorithm to create a front of non-dominated solutions. These results are used as a benchmark against which we compare simulations of a Business-As-Usual urban expansion in Switzerland to estimate the potential for reducing the loss of agricultural productivity. By analysing the front of non-dominated solutions, we show that there are areas of open land that can be converted into residential land without trading-off compactness against agricultural productivity. We show that there is a large potential for reducing the loss of agricultural productivity when optimizing the configuration of urban development. This potential strongly varies between municipalities and seems to depend primarily on the amount of demand for new urban land within each municipality. The proposed methodology of using multi-objective optimization, followed by a post-optimality analysis and including results from business-as-usual development can be used to support the decision-making processes in urban planning.

Improving the performance of genetic algorithms for land-use allocation problems

ABSTRACT Multi-objective optimization can be used to solve land-use allocation problems involving multiple conflicting objectives. In this paper, we show how genetic algorithms can be improved in order to effectively and efficiently solve multi-objective land-use allocation problems. Our focus lies on improving crossover and mutation operators of the genetic algorithms. We tested a range of different approaches either based on the literature or proposed for the first time. We applied them to a land-use allocation problem in Switzerland including two conflicting objectives: ensuring compact urban development and reducing the loss of agricultural productivity. We compared all approaches by calculating hypervolumes and by analysing the spread of the produced non-dominated fronts. Our results suggest that a combination of different mutation operators, of which at least one includes spatial heuristics, can help to find well-distributed fronts of non-dominated solutions. The tested modified crossover operators did not significantly improve the results. These findings provide a benchmark for multi-objective optimization of land-use allocation problems with promising prospectives for solving complex spatial planning problems.

Influence of parameter settings in automated scoring of AFLPs on population genetic analysis

The use of procedures for the automated scoring of amplified fragment length polymorphisms (AFLP) fragments has recently increased. Corresponding software does not only automatically score the presence or absence of AFLP fragments, but also allows an evaluation of how different settings of scoring parameters influence subsequent population genetic analyses. In this study, we used the automated scoring package rawgeno to evaluate how five scoring parameters influence the number of polymorphic bins and estimates of pairwise genetic differentiation between populations (Fst). Steps were implemented in r to automatically run the scoring process in rawgeno for a set of different parameter combinations. While we found the scoring parameters minimum bin width and minimum number of samples per bin to have only weak influence on pairwise Fst values, maximum bin width and bin reproducibility had much stronger effects. The minimum average bin fluorescence scoring parameter affected Fst values in an only moderate way. At a range of scoring parameters around the default settings of rawgeno, the number of polymorphic bins as well as pairwise Fst values stayed rather constant. This study thus shows the particularities of AFLP scoring, be it either manual or automatical, can have profound effects on subsequent population genetic analysis.

Consequences of population topology for studying gene flow using link‐based landscape genetic methods

Abstract Many landscape genetic studies aim to determine the effect of landscape on gene flow between populations. These studies frequently employ link‐based methods that relate pairwise measures of historical gene flow to measures of the landscape and the geographical distance between populations. However, apart from landscape and distance, there is a third important factor that can influence historical gene flow, that is, population topology (i.e., the arrangement of populations throughout a landscape). As the population topology is determined in part by the landscape configuration, I argue that it should play a more prominent role in landscape genetics. Making use of existing literature and theoretical examples, I discuss how population topology can influence results in landscape genetic studies and how it can be taken into account to improve the accuracy of these results. In support of my arguments, I have performed a literature review of landscape genetic studies published during the first half of 2015 as well as several computer simulations of gene flow between populations. First, I argue why one should carefully consider which population pairs should be included in link‐based analyses. Second, I discuss several ways in which the population topology can be incorporated in response and explanatory variables. Third, I outline why it is important to sample populations in such a way that a good representation of the population topology is obtained. Fourth, I discuss how statistical testing for link‐based approaches could be influenced by the population topology. I conclude the article with six recommendations geared toward better incorporating population topology in link‐based landscape genetic studies.

Models of Coupled Settlement and Habitat Networks for Biodiversity Conservation: Conceptual Framework, Implementation and Potential Applications

Worldwide, the expansion of settlement and transport infrastructure is one of the most important proximate as well as ultimate causes of biodiversity loss. As much as every modern human society depends on a network of settlements that is well-connected by transport infrastructure (i.e. settlement network), animal and plant species depend on networks of habitats between which they can move (i.e. habitat networks). However, changes to a settlement network in a region often threaten the integrity of the region’s habitat networks. Determining plans and policy to prevent these threats is made difficult by the numerous interactions and feedbacks that exist between and within the settlement and habitat networks. Mathematical models of coupled settlement and habitat networks can help us understand the dynamics of this social-ecological system. Yet, few attempts have been made to develop such mathematical models. In this paper, we promote the development of models of coupled settlement and habitat networks for biodiversity conservation. First, we present a conceptual framework of key variables that are ideally considered when operationalising the coupling of settlement and habitat networks. In this framework, we first describe important network-internal interactions by differentiating between the structural (i.e. relating to purely physical conditions determining the suitability of a location for living or movement) and functional (i.e. relating to the actual presence, abundance or movement of people or other organisms) properties of either network. We then describe the main one-way influences that a settlement network can exert on the habitat networks and vice versa. Second, we give several recommendations for the mathematical modelling of coupled settlement and habitat networks and present several existing modelling approaches (e.g. habitat network models and land-use transport interaction models) that could be used for this purpose. Lastly, we elaborate on potential application of models of coupled settlement and habitat networks in the development of complex network theory, in the assessment of system resilience and in conservation, transport and urban planning. The development of coupled settlement and habitat network models is important to gain a better system-level understanding of biodiversity conservation under a rapidly urbanising and growing human population.

Short versus long-term urban planning using multi-objective optimization

In this paper, we consider the short-term versus long-term urban planning problem as a bi-objective optimization problem. Two conflicting objectives considered are (i) maximization of compact urban development and (ii) minimization of good quality agricultural soil. In such problems, decision-making becomes an important task, which we highlight. Such problems usually involve an astronomically large search space, which must be negotiated well by an optimization algorithm. In this paper, we discuss the importance of using optimization and decision-making procedures in urban planning task in Switzerland and a future paper will demonstrate the results obtained.