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However, it has been shown that Mantel and partial Mantel tests may exhibit higher type-1 error rates than multivariate regression approaches (Balkenhol et al. 2009b) . ... data, and ever-growing computer power for conducting complex spatial analyses allow us to quantitatively link landscape features to the spatial distribution of neutral and adaptive genetic variation (Balkenhol ...doi:10.1007/s10980-013-9982-x fatcat:topvpkm7wncgbhcpmquwh467gi
configuration, and matrix quality on the spatial distribution of neutral and adaptive genetic variation and associated microevolutionary processes across natural populations (Holderegger and Wagner 2008; Balkenhol ... Rather than analyzing genetic data from a single landscape, Balkenhol et al. ... configuration, and matrix quality on the spatial distribution of neutral and adaptive genetic variation and associated microevolutionary processes across natural populations (Holderegger and Wagner 2008; Balkenhol ...doi:10.1007/s10592-013-0473-z fatcat:gb5vwnnt35b3vjqkidwnxuaivu
1. The successful movement of individuals is fundamental to life. Facilitating these movements by promoting ecological connectivity has become a central theme in ecology and conservation. Urban areas contain more than half of the world's human population, and their potential to support biodiversity and to connect their citizens to nature is increasingly recognized. Promoting ecological connectivity within these areas is essential to reaching this potential. However, our current understanding ofdoi:10.1111/1365-2435.12489 fatcat:5d7iitk4fvdo3m4qj77qfvfndq
more »... ecological connectivity within urban areas appears limited. 2. We reviewed the published scientific literature to assess the state-of-the-art of ecological connectivity research in urban areas, summarized trends in study attributes and highlighted knowledge gaps. 3. We found 174 papers that investigated ecological connectivity within urban areas. These papers addressed either structural (48) or functional connectivity (111), and some addressed both (15), but contained substantial geographic and taxonomic biases. These papers rarely defined the aspect of connectivity they were investigating and objective descriptions of the local urban context were uncommon. Formulated hypotheses or a priori predictions were typically unstated and many papers used suboptimal study designs and methods. 4. We suggest future studies explicitly consider and quantify the landscape within their analyses and make greater use of available and rapidly developing tools and methods for measuring functional connectivity (e.g. biotelemetry or landscape genetics). We also highlight the need for studies to clearly define how the terms 'urban' and 'connectivity' have been applied. 5. Knowledge gaps in ecological connectivity in urban areas remain, partly because the field is still in its infancy and partly because we must better capitalize on the state-of-the-art technological and analytical techniques that are increasingly available. Well-designed studies that employed high-resolution data and powerful analytical techniques highlight our abilities to quantify ecological connectivity in urban areas. These studies are exemplary, setting the standards for future research to facilitate data-driven and evidence-based biodiversity-friendly infrastructure planning in urban areas.
Balkenhol & Landguth 2011; Cushman 2014 Cushman , 2015 . ... These should find more consideration in future studies, as suggested by Wagner and Fortin (2013; Chapter 5), Murphy et al. (2010; Chapter 9) , and Pflüger and Balkenhol (2014) . ...doi:10.1002/9781118525258.ch14 fatcat:v3ctpzod3ng5djmoiwz53njkny
The ability to acquire water from the soil is a major driver in interspecific plant competition and it depends on several root functional traits. One of these traits is the excretion of gel-like compounds (mucilage) that modify physical soil properties. Mucilage secreted by roots becomes hydrophobic upon drying, impedes the rewetting of the soil close to the root, the so called rhizosphere, and reduces water availability to plants. The function of rhizosphere hydrophobicity is not easilydoi:10.1371/journal.pone.0182188 pmid:28753673 pmcid:PMC5533451 fatcat:csmaiznpmbf6bkfz4rkmratzmu
more »... andable when looking at a single plant, but it may constitute a competitive advantage at the ecosystem level. We hypothesize that by making the top soil hydrophobic, deep-rooted plants avoid competititon with shallow-rooted plants. To test this hypothesis we used an individual-based model to simulate water uptake and growth of two virtual plant species, one deep-rooted plant capable of making the soil hydrophobic and a shallow-rooted plant. We ran scenarios with different precipitation regimes ranging from dry to wet (350, 700, and 1400 mm total annual precipitation) and from high to low precipitation frequencies (1, 7, and 14 days). Plant species abundance and biomass were chosen as indicators for competitiveness of plant species. At constant precipitation frequency mucilage hydrophobicity lead to a benefit in biomass and abundance of the tap-rooted population. Under wet conditions this effect diminished and tap-rooted plants were less productive. Without this trait both species coexisted. The effect of root exudation trait remained constant under different precipitation frequencies. This study shows that mucilage secretion is a competitive trait for the acquisition of water. This advantage is achieved by the modification of the soil hydraulic properties and specifically by inducing water repellency in soil regions which are shared with other species. for water the root system provides the plant-infrastructure for water uptake and below-ground interactions. Relevant functional root traits include the morphological structure and configuration of the root system (e.g., root length, architecture). The root system architecture is characterized by spatial configuration, depth, branching, and length of roots  . It determines the amount, density, and distribution of active root-surfaces. This configuration shapes the hydraulic conductivity of the plant-soil system, which determines the ease of water-transport from the soil to the shoot. Plants can manipulate the hydraulic conductivity by directed root growth or by actively manipulating the soil properties. The latter is, for instance, due to the exudation of photoassimilates into the soil by plant roots. These root exudates typically consist of sugars, amino acids, organic acids and lipids  . They play an important role for nutrient uptake  and they actively shape soil-microbiological composition [5, 7] . In the context of plant water uptake, attention has been drawn on the hydraulic properties of root exudates-soil complexes  . Especially mucilage plays a significant role here. Mucilage is a gel-like root-secret which consists of high-molecular weighted compounds like polysaccharides and a small fraction of lipids. It is released at the root tips into the rhizosphere, a small volume of soil around the roots. Mucilage is able to store a high amount of water and, thus, maintains the rhizosphere wet and hydraulically conductive [9, 10]. On the other hand, mucilage becomes hydrophobic upon drying, causing a zone of water repellent soil in the vicinity of roots or rhizosphere hydrophobicity [11, 12] . Note that other origins of soil water repellency exist, such as decomposition of wax-rich plant litter or condensation of long-chained organic compounds after burning , but we focus in this study only on soil hydrophobicity caused by rhizodeposition and particulary mucilage secretion. Although a wide range of plant species excrete mucilage  , the resulting degree of hydrophobicity could vary. For instance, mucilage from maize (Zea mays) and lupins (Lupinus albus) is more hydrophobic than the one of barley (Hordeum vulgare or wheat (Triticum aestivum) [15, 16] . Beside this variability in mucilage water repellency, the ecological function of the rhizosphere hydrophobicity is unclear. At the single plant scale, in controlled laboratory experiments, rhizosphere hydrophobicity was found to limit water uptake after drying/wetting cycles for lupins [17, 18] . But why do plant roots invest energy in making the soil in their vicinity water repellent and thereby reducing their ability to take up water? Our hypothesis is that rhizosphere hydrophobicity, in some circumstances, provides an advantage in the competition for water. We hypothesize that by making the topsoil hydrophobic, plants with deep roots avoid the competition with shallow-rooted plants that have no access to water stored in the subsoil. To fully understand such plant-soil and plant-plant interactions laboratory experiments with single plants (such as the experiments proving that the rhizosphere of selected plants turns hydrophobic upon drying) are not sufficient. Instead, analyses at the plant community level are required. Since large-scale field experiments are logistically challenging and face an increased complexity of the study system (e.g., genetic variability, above-ground interactions, competition for other resources such as nutrients, etc.), we took a different approach using an individual-based model (IBM). Specifically, we acquired information from laboratory experiments about root architecture and rhizosphere traits and included them in a spatially explicit individual-based population model. Individual-based simulation models gained importance as an experimental system to investigate complex patterns and processes in ecology  . Their particular strength lies in upscaling small-scale interactions, for example among and between abiotic and biotic entities like soil and plants, to the population scale. This is done by a 'bottom-up' approach in which an algorithm determines intra-and inter-specific behavior as well Rhizosphere hydrophobicity in the competition for water PLOS ONE | https://doi.org/10.1371/journal.pone.0182188 July 28, 2017 2 / 18 Fig 2. Root systems at different developmental stages. (A) Fibrous roots at intermediate stage show seven short adventitious root branches. (B) At final root growth stage, fibrous-rooted plants have 13 rootbranches. (C) Tap-roots after seven days show a principal vertical root reaching final depth already. Secondary root branches are restricted to one pair next to the surface. (D) The fully developed tap root system consists of the principal vertical root and seven pairs of lateral branches with a higher density in the topsoil. The root/soil system is capped by a impermeable barrier for water (red line) with one raster cell exit to the shoot/above ground system. For the first root growth stage (1-6 days) no water uptake was assumed and, hence, no sketch was drawn. https://doi.org/10.1371/journal.pone.0182188.g002 Rhizosphere hydrophobicity in the competition for water PLOS ONE | https://doi.
Estimates of home-range size are frequently used to compare areal requirements of animals over time or space. Comparative studies of home-range estimators have highlighted extreme differences among general classes of methods (e.g., polygon-based and kernel density-based estimators) and sensitivity to the choice of various tuning parameters (e.g., amount of smoothing). These studies, however, have largely failed to consider how estimates of home-range size are typically used in applied research.doi:10.1186/s40317-015-0051-x fatcat:oqejnmxqard45gwy4tghkzvtae
more »... We illustrate simulation-based methods for comparing estimators, which focus on relative differences in home-range size (over time or space), rather than their absolute magnitude. We also consider Global Positioning Technology (GPS) location data from a black bear (Ursus americanus) from northwestern Minnesota, USA, to illustrate the relevance to real-world data applications. Results: In our examples, estimates of home-range size often differed considerably in absolute magnitude. Yet, for relative differences, the choice of home-range estimator was often negligible. Furthermore, choosing the right estimator was less important than other aspects of study design (e.g., number of animals followed). Conclusion: Many questions in ecology focus on changes in space-use patterns (over space or time). For these types of questions, home-range estimators should be evaluated in terms of their ability to detect these spatial and temporal patterns. More importantly, home-range estimation should be seen as a means to an end-i.e., estimators provide indices useful for addressing interesting biological questions or hypotheses-rather than as an end to itself.
Anthropogenic influences such as deforestation, increased infrastructure, and general urbanization has led to a continuous loss in biodiversity. Amphibians are especially affected by these landscape changes. This study focuses on the population genetics of the endangered yellow-bellied toad (Bombina variegata) in the northern Weser Hills of Germany. Additionally, a landscape genetic analysis was conducted to evaluate the impact of eight different landscape elements on the genetic connectivitydoi:10.3390/d13120623 fatcat:pytqyqi5bvgktlkyyypoqj2ctq
more »... the subpopulations in this area. Multiple individuals from 15 study sites were genotyped using 10 highly polymorphic species-specific microsatellites. Four genetic clusters were detected, with only two of them having considerable genetic exchange. The average genetic differentiation between populations was moderate (global FST = 0.1). The analyzed landscape elements showed significant correlations with the migration rates and genetic distances between populations. Overall, anthropogenic structures had the greatest negative impact on gene flow, whereas wetlands, grasslands, and forests imposed minimal barriers in the landscape. The most remarkable finding was the positive impact of the underpasses of the motorway A2. This element seems to be the reason why some study sites on either site of the A2 showed little genetic distance even though their habitat has been separated by a strong dispersal barrier.
Increased availability of high-resolution movement data has led to the development of numerous methods for studying changes in animal movement behavior. Path segmentation methods provide basics for detecting movement changes and the behavioral mechanisms driving them. However, available path segmentation methods differ vastly with respect to underlying statistical assumptions and output produced. Consequently, it is currently difficult for researchers new to path segmentation to gain andoi:10.1186/s40462-016-0086-5 pmid:27595001 pmcid:PMC5010771 fatcat:4mmp6jcpsjdmbch6shy5uuadbi
more »... of the different methods, and choose one that is appropriate for their data and research questions. Here, we provide an overview of different methods for segmenting movement paths according to potential changes in underlying behavior. To structure our overview, we outline three broad types of research questions that are commonly addressed through path segmentation: 1) the quantitative description of movement patterns, 2) the detection of significant change-points, and 3) the identification of underlying processes or 'hidden states'. We discuss advantages and limitations of different approaches for addressing these research questions using path-level movement data, and present general guidelines for choosing methods based on data characteristics and questions. Our overview illustrates the large diversity of available path segmentation approaches, highlights the need for studies that compare the utility of different methods, and identifies opportunities for future developments in path-level data analysis.
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 variousdoi:10.1111/j.1600-0587.2009.05807.x fatcat:or2ialou25exdajflueqb44tcq
more »... stical 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.
more rigorous methods to parameterize resistance models by inferring the influences of landscape on realized population connectivity (Spear et al. 2005 , Cushman et al. 2006 , Storfer et al. 2007 , Balkenhol ... strongly influenced by many other biological and ecological factors acting locally, including sex-specific space-use behavior, local population density, survival, or reproductive success (Pflü ger and Balkenhol ...doi:10.1890/es14-00387.1 fatcat:qsrh5eufjnhhxmfxlftj4z77be
linear relationship should not necessarily be assumed, as critical thresholds can exist (With & Crist 1995) , and non-linear relationships sometimes are a better fit for the data (Cushman et al. 2006; Balkenhol ...doi:10.1111/j.1365-294x.2010.04657.x pmid:20723064 fatcat:m4ig5y5w7jgoxhw6xyzotrtgqi
We used STRU CTU RE in a hierarchical framework by re-running the clustering algorithm for each of the detected genetic clusters in the previous analysis (Coulon et al. 2008; Balkenhol et al. 2014 ). ... isolation of multiple, potentially connected populations, and assume that the existence of large or densely populated neighboring AMUs is important for explaining observed population structure (e.g., Balkenhol ...doi:10.1007/s10592-020-01248-8 fatcat:st6bhbhyzzh7tfkjod2e5zxjmq
To understand how landscape characteristics affect gene flow in species with diverging ecological traits, it is important to analyze taxonomically related sympatric species in the same landscape using identical methods. Here, we present such a comparative landscape genetic study involving three closely related Hesperid butterflies of the genus Thymelicus that represent a gradient of diverging ecological traits. We analyzed landscape effects on their gene flow by deriving interpopulationdoi:10.1371/journal.pone.0106526 pmid:25184414 pmcid:PMC4153614 fatcat:tb57orhihbezbkajiblr7vd7r4
more »... vity estimates based on different species distribution models (SDMs), which were calculated from multiple landscape parameters. We then used SDM output maps to calculate circuit-theoretic connectivity estimates and statistically compared these estimates to actual genetic differentiation in each species. We based our inferences on two different analytical methods and two metrics of genetic differentiation. Results indicate that land use patterns influence population connectivity in the least mobile specialist T. acteon. In contrast, populations of the highly mobile generalist T. lineola were panmictic, lacking any landscape related effect on genetic differentiation. In the species with ecological traits in between those of the congeners, T. sylvestris, climate has a strong impact on inter-population connectivity. However, the relative importance of different landscape factors for connectivity varies when using different metrics of genetic differentiation in this species. Our results show that closely related species representing a gradient of ecological traits also show genetic structures and landscape genetic relationships that gradually change from a geographical macro-to microscale. Thus, the type and magnitude of landscape effects on gene flow can differ strongly even among closely related species inhabiting the same landscape, and depend on their relative degree of specialization. In addition, the use of different genetic differentiation metrics makes it possible to detect recent changes in the relative importance of landscape factors affecting gene flow, which likely change as a result of contemporary habitat alterations.
1. Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently-occupied range and potential future range.doi:10.5281/zenodo.5502315 fatcat:4emfosl5qvctfjosu4e4p6dfee
more »... realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an integrated SDM database (iSDMdb). 2. The iSDMdb is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility, we used the endangered New Zealand sea lion (Phocarctos hookeri). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management. 3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the iSDMdb's additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected. 4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting spec [...]
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