Landscape heterogeneity metrics as indicators of bird diversity: Determining the optimal spatial scales in different landscapes

Species distribution models are often used to study the biodiversity of ecosystems. The modelling process uses a number of parameters to predict others, such as the occurrence of determinate species, population size, habitat suitability or biodiversity. It is well known that the heterogeneity of landscapes can lead to changes in species’ abundance and biodiversity. However, landscape metrics depend on maps and spatial scales when it comes to undertaking a GIS analysis.We explored the goodness of fit of several models using the metrics of landscape heterogeneity and altitude as predictors of bird diversity in different landscapes and spatial scales. Two variables were used to describe biodiversity: bird richness and trophic level diversity, both of which were obtained from a breeding bird survey by means of point counts. The relationships between biodiversity and landscape metrics were compared using multiple linear regressions. All of the analyses were repeated for 14 different spatial scales and for cultivated, forest and grassland environments to determine the optimal spatial scale for each landscape typology.Our results revealed that the relationships between species’ richness and landscape heterogeneity using 1:10,000 land cover maps were strongest when working on a spatial scale up to a radius of 125–250 m around the sampled point (circa 4.9–19.6 ha). Furthermore, the correlation between measures of landscape heterogeneity and bird diversity was greater in grasslands than in cultivated or forested areas. The multi-spatial scale approach is useful for (a) assessing the accuracy of surrogates of bird diversity in different landscapes and (b) optimizing spatial model procedures for biodiversity mapping, mainly over extensive areas.     

On the role of spatial stochastic models in understanding landscape indices in ecology

Spatial stochastic models play an important role in understanding and predicting the behaviour of complex systems. Such models may be implemented with explicit knowledge of only a limited number of parameters relating to spatial relationships among locations. Consequently, they are often used instead of deterministic‐mechanistic models, which may potentially require an unrealistically large number of parameters. Currently, in contrast to spatial stochastic models, the parameterization of the joint spatial distribution of objects in landscape models is more often implicit than explicit. Here, we investigate the similarities and differences between bona fide spatial stochastic models and landscape models by focusing mostly on the relationships between processes, their realizations (patterns), representation and measurement, and their use in exploratory as well as confirmatory data analysis. One of the most important outcomes of recognizing the importance of stochastic processes is the acknowledgement that the spatial pattern observed in a landscape is only one realization of that process. Hence, while ecologists have been using landscape pattern indices (LPIs) to characterize landscape heterogeneity and/or make inferences about processes shaping the landscape, no stochastic modelling framework has been developed for their proper statistical elucidation. Consequently, several (mis)uses of LPIs draw conclusions about landscapes which are suspect. We show that several reports about sensitivities of LPIs to measurements have common roots that can be made explicitly manageable by adopting stochastic models of spatial structure. The key parameters of these stochastic models are composition and configuration, which, in general, cannot be estimated independently from each other. We outline how to develop the stochastic framework to interpret observations and make some recommendations to practitioners about everyday usage. The conceptual linkages between patterns and processes are particularly important in light of recent efforts to bridge the static‐structural and the dynamic‐analytic traditions of ecology.     

基于TM影像的景观空间自相关分析——以北京昌平区为例

格局与尺度之间的关系是景观生态学的核心研究内容。景观格局发生在不同的尺度 ,而尺度又影响格局的研究 ,因而 ,在景观生态学研究中应用多种量化研究方法于一系列尺度来确定和特征化空间格局研究 ,并探求空间格局与生态学功能和生态学过程之间的关系是非常必要的。以北京昌平区为例 ,从 TM影像中选取 5个具有突出自然和社会经济背景差异的景观 ,即林地景观、农田景观、都市边缘景观、卫星城景观和灌丛景观为研究对象 ,基于归一化植被指数 (N DVI) ,采用常用空间自相关指数 ,即 Moran的 I系数和 Geary的 c系数进行一系列的空间自相关分析 ,旨在阐明 :变化的空间粒度如何影响空间分析 ?以及空间分析如何响应划区效应 ?此外 ,基于 N DVI和数字高程模型 (DEM)也探讨了对于不同的数据类型 ,格局的尺度依赖性如何变化。研究结果表明 :空间粒度的变化对于景观分析有着显著的影响 ,随着空间粒度的增加 ,空间自相关均呈下降趋势 ;不同景观类型对于空间粒度的变化有着不同的响应 ,人为干扰较多的景观具有较低的空间自相关 ,但对空间粒度的变化表现出较强的敏感性 ;对于不同的数据类型 ,格局分析对空间粒度变化的响应是不同的     

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

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.     

At what spatial scale(s) do mammals respond to urbanization?

Spatial scale is fundamental in understanding species–landscape relationships because species’ responses to landscape characteristics typically vary across scales. Nonetheless, such scales are often unidentified or unreliably predicted by theory. Many landscapes worldwide are urbanizing, yet the spatial scaling of species’ responses to urbanization is poorly understood. We investigated the spatial scaling of urbanization effects on a community of 15 mammal species using ~60 000 wildlife detections collected from a constellation of 207 camera traps across an extensive urban park system. We embedded a bivariate Gaussian kernel in hierarchical multi-species models to determine two scales of effect (a scale of maximal effect and a broader scale of cumulative landscape effect) for two biological responses (occupancy and site visit frequency) across two seasons (winter and summer) for each species. We then assessed whether scales of effect varied according to theoretical predictions associated with biological responses and species traits (body size and mobility). Scales of effect ranged from < 50 m to > 9000 m and varied among species, but not as predicted by theory. Species’ occupancy generally showed a weak response to urbanization and the scale of this effect was both highly uncertain and consistent across species. We did not detect any relationship between scales of effect and species’ body size or mobility, nor was there any evident pattern of scaling across biological response or seasons. These results imply that 1) urbanization effects on mammals manifest across a very broad spectrum of spatial scales, and 2) current theories that a priori predict the scale at which urbanization affects mammals may be of limited use within a given system. Overall, this study suggests that developing general theory regarding the scaling of species–landscape relationships requires additional empirical work conducted across multiple species, systems and timescales.     

生态学中的尺度问题：内涵与分析方法

尺度问题已成为现代生态学的核心问题之一.尺度问题主要涉及3个方面：尺度概念、尺度分析和尺度推绎.主要评述前两个方面.生态学尺度有三重概念：维数、种类和组分,其中每重概念又包含了多个定义,有必要进行澄清、分类和统一.尺度分析涉及尺度效应分析和多尺度空间格局分析.格局、过程及它们之间的关系,以及某些景观特性均表现出尺度效应,因此多尺度研究非常必要和重要.多尺度空间格局分析（尤其是特征尺度的识别）是进行尺度效应分析和跨尺度推绎的基础.多尺度分析需要特定的方法,景观指数法是最常用和最简单的方法,但也常产生误导;空间统计学方法（如半方差分析法、尺度方差分析法、空隙度指数法和小波分析法等）和分维分析法在最近十几年发展起来,并逐渐应用于生态学,在尺度分析上具有很大的应用潜力.各种方法在尺度分析上各有优势和不足,有必要同时使用两种或两种以上方法进行比较和评估.总之,有关尺度分析的研究需要进一步加强,从而为下一步的尺度推绎提供可靠的依据.     

Complex effects of scale on the relationships of landscape pattern versus avian species richness and community structure in a woodland savanna mosaic

Landscape pattern metrics are widely used for predicting habitat and species diversity. However, the relationship between landscape pattern and species diversity is typically measured at a single spatial scale, even though both landscape pattern, and species occurrence and community composition are scale‐dependent. While the effects of scale on landscape pattern are well documented, the effects of scale on the relationships between spatial pattern and species richness and composition are not well known. Here, our main goal was to quantify the effects of cartographic scale (spatial resolution and extent) on the relationships between spatial pattern and avian richness and community structure in a mosaic of grassland, woodland, and savanna in central Wisconsin. Our secondary goal was to evaluate the effectiveness of a newly developed tool for spatial pattern analysis, multiscale contextual spatial pattern analysis (MCSPA), compared to existing landscape metrics. Landscape metrics and avian species richness had quadratic, exponential, or logarithmic relationships, and these patterns were generally consistent across two spatial resolutions and six spatial extents. However, the magnitude of the relationships was affected by both resolution and extent. At the finer resolution (10‐m), edge density was consistently the best predictor of species richness, followed by an MCSPA metric that measures the standard deviation of woody cover across extents. At the coarser resolution (30‐m), NDVI was the best predictor of species richness by far, regardless of spatial extent. Another MCSPA metric that denotes the average woody cover across extents, together with percent of woody cover, were always the best predictors of variation in avian community structure. Spatial resolution and extent had varying effects on the relationships between spatial pattern and avian community structure. We therefore conclude that cartographic scale not only affects measures of landscape pattern per se, but also the relationships among spatial pattern, species richness, and community structure, often in complex ways, which reduces the efficacy of landscape metrics for predicting the richness and diversity of organisms.     

Landscape issues in plant ecology

In the last decade, we have seen the emergence and consolidation of a conceptual framework that recognizes the landscape as an ecological unit of interest. Plant ecologists have long emphasized landscape-scale issues, but there has been no recent attempt to define how landscape concepts are now integrated in vegetation studies. To help define common research paradigms in both landscape and plant ecology, we discuss issues related to three main landscape concepts in vegetation researches, reviewing theoretical influences and emphasizing recent developments. We first focus on environmental relationships, documenting how vegetation patterns emerge from the influence of local abiotic conditions. The landscape is the physical environment. Disturbances are then considered, with a particular attention to human-driven processes that often overrule natural dynamics. The landscape is a dynamic space. As environmental and historical processes generate heterogeneous patterns, we finally move on to stress current evidence relating spatial structure and vegetation dynamics. This relates to the concept of a landscape as a patch-corridor-matrix mosaic. Future challenges involve: 1) the capacity to evaluate the relative importance of multiple controlling processes at broad spatial scale; 2) better assessment of the real importance of the spatial configuration of landscape elements for plant species and finally; 3) the integration of natural and cultural processes and the recognition of their interdependence in relation to vegetation management issues in human landscapes.     

景观生态恢复与重建是区域生态安全格局构建的关键途径

生态恢复与重建是跨尺度、多等级的问题,其主要表现层次应是生态系统（生物群落）、景观,甚至区域,而不能仅仅局限于生态系统。景观的恢复与重建是针对景观退化而言,景观退化从表现形式上可分为景观结构退化与景观功能退化。景观结构退化即景观破碎化,是指景观中各生态系统之间的各种功能联系断裂或连接度(connectivity)减少的现象;而鲜受重视的景观聚集（aggregation）在很多情况下同样具有造成景观退化的负面效应。景观功能退化是指与前一状态相比,由于景观异质性的改变导致景观的稳定性与服务功能等的衰退现象。景观恢复是指恢复原生生态系统间被人类活动终止或破坏的相互联系;景观生态建设应以景观单元空间结构的调整和重新构建为基本手段,包括调整原有的景观格局,引进新的景观组分等,以改善受胁或受损生态系统的功能,提高其基本生产力和稳定性,将人类活动对于景观演化的影响导入良性循环。二者的综合,统称为景观生态恢复与重建,是构建安全的区域生态格局的关键途径。其目标是建立一种由结构合理、功能高效、关系协调的模式生态系统（model ecosystem）组成的模式景观（model landscape）,以实现生态系统健康、生态格局安全和景观服务功能持续,以3S(RS,GPS,GIS)技术为支撑的GAP(ageographic approach to protect biological diversity)分析将为大尺度景观恢复的诊断、评价、规划提供重要的手段。景观中某些关键性点、位置或关系的破坏对整个生态安全具有毁灭性的后果,研究景观层次上的生态恢复模式及恢复技术、选择恢复的关键位置、构筑生态安全格局已成为景观生态学家关注的焦点。     

景观生态学在大气污染研究中的应用

快速城市化和工业化进程造成了一系列大气污染问题,亟需在宏观尺度上解析大气污染时空分布规律。景观生态学关注格局与过程耦合,景观"源汇"理论可对应解析大气污染物的源与汇效应,将景观生态学的理论与方法引入大气污染研究中已成为解决当前区域发展与大气污染权衡的有效途径。从景观生态学视角辨识了景观与大气污染物的源汇关系,系统梳理了景观格局与大气污染的定量关系,指出当前景观格局指标仍需进一步完善以表征大气污染时空分布特征,而高质量大气污染物时空数据的缺乏是限制景观格局与大气污染过程耦合分析的重要因素,拓展应用景观"源汇"理论,定量解析景观格局对大气污染的源汇效应,同时进一步研发遥感反演技术,实现大气污染物分布格局的精细刻画,将为区域景观规划提供重要支撑。强化大气污染研究中的景观生态学分析途径,将有助于深化景观生态学格局与过程耦合研究体系,也将为景观可持续管理提供有力的科学支撑。     

基于移动窗口法的岷江干旱河谷景观格局梯度分析

以岷江干旱河谷为研究区,基于GIS技术和移动窗口法对其景观格局梯度变化进行分析,以期为区域的景观格局优化和管理提供支持。根据研究区的形状特征,分别沿干流和支流设置4条样带;选取景观水平下的景观指数,利用FRAGSTATS3.3软件分别采取标准法和移动窗口法获得不同尺度下的景观指数值;综合利用景观指数粒度效应分析、区域面积信息守恒评价方法和景观指数幅度效应评价曲线确定研究区景观格局梯度分析移动窗口尺寸,并计算了此窗口尺度下4条样带上的景观指数,得到沿样带方向的景观梯度格局。结果表明:岷江干旱河谷的景观基质是灌木林地,面积占73.82%。有林地和草地的景观异质性低,居民地和耕地斑块破碎化程度较大。确定了50m的栅格大小,250m的移动窗口尺寸为研究区景观格局梯度分析的窗口尺度;4条样带上各景观指数均随景观类型变化出现不同幅度的上下波动特征,梯度特征明显。处于景观类型过渡地带的区域,景观多样性和异质性增加,破碎化程度高;干旱河谷景观格局梯度变化主要受地形、水热因子,以及堤坝建设和土地开发利用影响。研究作为一种有益的尝试,更精细地分析了研究区的景观格局,能够为河谷地区景观格局量化分析提供参考,但也存在一定不足,需要在今后工作中继续深入研究。     

生态学中的尺度问题——尺度上推

尺度推绎是生态学理论和应用的核心。如何在一个异质景观中进行尺度推绎仍然是一个悬而未决的科学难题,是对当今生态学家在全球变化背景下研究环境问题的重大挑战。就目前的研究,一般可分为四大类尺度推绎途径:空间分析法(如分维分析法和小波分析法)、基于相似性的尺度上推方法、基于局域动态模型的尺度上推方法、随机(模型)法。基于相似性的尺度上推方法来源于生物学上的异量关联,可将其思想延伸至空间上,研究物种丰富度、自然河网、地形特征、生态学格局或过程变量和景观指数等。基于局域动态模型的尺度上推方法需要首先确定是否进行跨尺度推绎,以及是否考虑空间单元之间的水平相互作用和反馈,然后再应用具体的方法或途径,如简单聚合法、有效值外推法、直接外推法、期望值外推、显式积分法和空间相互作用模拟法等。随机(模型)法以其它尺度上推方法为基础,根据研究的是单个景观,还是多个景观,采用不同的途径。理解、定量和降低尺度推绎结果的不确定性已经变得越来越重要,但相关研究仍然极少。以上所有有关尺度推绎的方法、途径和结果分析共同构成了尺度推绎的概念框架。     

Effect of woody vegetation at the landscape scale on the abundance of natural enemies in Australian vineyards

Undisturbed vegetation within agricultural areas, especially woody vegetation, has been documented to enhance natural invertebrate enemies within adjacent crops, particularly in northern Europe. To test this idea within the context of Australian vineyards, we considered 44 landscapes from two regions, and sampled invertebrates in vineyards central to each landscape five times at monthly intervals using canopy sticky traps. Landscape composition was characterized at 11 spatial scales from 95 m to 3 km radius. We found only weak relationships between woody vegetation and the abundance of invertebrate groups including coccinellids at any spatial scale, regardless of whether the contribution of each scale was considered independently or together using a multiple regression approach. The only consistent pattern was that several families of parasitoids were influenced by woody vegetation at the landscape scale; the abundance of Eulophidae increased with woody vegetation in the landscape, while two families of smaller parasitoids, Trichogrammatidae and Mymaridae, were negatively affected by woody vegetation. We discuss possible reasons for these apparent contrasting patterns between Australian and European studies.     

A Framework for Evaluating Heterogeneity and Landscape-Level Impacts of Non-native Aquatic Species

Non-native species are a major component of global environmental change, and aquatic systems are especially vulnerable to non-native species impacts. Much of the research on aquatic non-native species impact has occurred at the local or site level. In reality, non-native species impacts play out across multiple spatial scales on heterogeneous landscapes. How can we ‘scale up’ our understanding of site-level impacts to the broader landscape scale? To address this disconnect, we synthesize our current understanding of key components of landscape-scale non-native species impacts: geographic range, abundance, and local impacts. Most aquatic non-native species have small ranges, while a few have large ranges. However, aquatic non-native species are often far from saturated on landscapes, and occurrence records are often woefully incomplete. Aquatic non-native species are often at low abundances where they are present, reaching high abundance in a small number of locations. Finally, local-scale impact can be estimated from abundance, but this requires knowledge of the abundance–impact relationship. Considering these multiple components enables understanding of non-native species impacts at broader spatial scales. Although the landscape-level impacts of aquatic non-native species may be high, the spatial distribution of site-level impacts is uneven, and highly impacted sites may be relatively uncommon. This heterogeneity in impacts provides an opportunity to optimize and prioritize non-native species management and prevention efforts.     

Energy barriers, pathways, and dynamics during folding of large, multidomain RNAs

Large, multidomain RNA molecules are generally thought to fold following multiple pathways down rugged landscapes populated with intermediates and traps. A challenge to understanding RNA folding reactions is the complex relationships that exist between the structure of the RNA and its folding landscape. The identification of intermediate species that populate folding landscapes and characterization of elements of their structures are the key components to solving the RNA folding problem. This review explores recent studies that characterize the dominant pathways by which RNA folds, structural and dynamic features of intermediates that populate the folding landscape, and the energy barriers that separate the distinct steps of the folding process.     

Fine-scale analysis reveals cryptic landscape genetic structure in desert tortoises

Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise (Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers (power lines, roads). We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately.     

渭河源流域源汇景观演变对径流泥沙的影响

探讨源汇景观演变特征与径流泥沙的关系对定量识别源汇景观功能对生态过程的影响具有重要的科学意义。以位于黄土高原和西秦岭山地交错区域的渭河源为例,分析了流域源汇景观格局演变与降雨耦合驱动对径流和泥沙的影响,结果表明:(1)研究区1982—2017年汇景观比例逐渐增加并超过源景观,源汇景观功能演变主要发生在黄土丘陵区,源汇景观负荷比指数呈逐年下降趋势;(2)影响流域径流和泥沙的主要因素不同,洪峰流量与含沙量主要受源景观面积比例和源汇景观负荷比的影响,呈显著的下降趋势,而径流量仅与降水量存在相关性;(3)基于信息理论方法的相对重要性分析表明,降雨对流域径流量的相对重要性高于源汇景观负荷比,而源汇景观负荷比对洪峰流量和含沙量的相对重要性较高,传输距离和海拔分布对径流泥沙和洪峰流量具有重要影响。在长时间尺度上,源汇景观格局演变影响了径流及泥沙特征,并且对泥沙影响的相对重要性高于径流。源汇景观合理的时空分布可以有效调控水土流失过程,对黄土高原生态可持续发展具有重要意义。     

The landscape context of cereal aphid-parasitoid interactions

Analyses at multiple spatial scales may show how important ecosystem services such as biological control are determined by processes acting on the landscape scale. We examined cereal aphid-parasitoid interactions in wheat fields in agricultural landscapes differing in structural complexity (32-100% arable land). Complex landscapes were associated with increased aphid mortality resulting from parasitism, but also with higher aphid colonization, thereby counterbalancing possible biological control by parasitoids and lastly resulting in similar aphid densities across landscapes. Thus, undisturbed perennial habitats appeared to enhance both pests and natural enemies. Analyses at multiple spatial scales (landscape sectors of 0.5-6 km diameter) showed that correlations between parasitism and percentage of arable land were significant at scales of 0.5-2 km, whereas aphid densities responded to percentage of arable land at scales of 1-6 km diameter. Hence, the higher trophic level populations appeared to be determined by smaller landscape sectors owing to dispersal limitation, showing the 'functional spatial scale' for species-specific landscape management.     

Multiscale and surface pattern analysis of the effect of landscape pattern on carabid beetles distribution

It has been often shown that spatial distribution of species can be related to the main characteristics of their habitat. Usually, such relationships refer to point pattern analysis and try to determine whether the distribution of species is conditioned by their surroundings or not. They often seek correlations at a given place, date and scale and neglect the information potentially extracted from detailed surface pattern analyses that would require extended data. This study investigated the impact of landscape pattern and landscape context (of habitats) on carabid beetles assemblages in each place and on a continuous range of scales of a landscape. For this purpose, we develop a tool able to visualize and quantify the spatial variations of indices commonly used in point pattern analyses, such as contagion (heterogeneity) of a landscape and abundance (counts) of species. Using moving windows, we plotted contagion maps of two Brittany sites (France) and cross-correlation maps between the latter and carabid beetles spatial distributions, in order to study their local relationships. Associated landscape and insect index scaling profiles helped to interpret the correlations found. A rather good agreement with landscape ecology predictions over both sites has been found: significant relationships between land cover contagion and total carabid abundance on one hand, between agricultural intensification (mainly maize fields) and carabid body size on the other hand, have been observed. Nevertheless, detailed surface pattern analyses over a wide range of scales show quite high deviations from empirical results with no correlation, or even negative correlation, in some places between otherwise correlated indices.     

Landscape genetics and limiting factors

Population connectivity is mediated by the movement of organisms or propagules through landscapes. However, little is known about how variation in the pattern of landscape mosaics affects the detectability of landscape genetic relationships. The goal of this paper is to explore the impacts of limiting factors on landscape genetic processes using simulation modeling. We used spatially explicit, individual-based simulation modeling to quantify the effects of habitat area, fragmentation and the contrast in resistance between habitat and non-habitat on the apparent strength and statistical detectability of landscape genetic relationships. We found that landscape genetic effects are often not detectable when habitat is highly connected. In such situations landscape structure does not limit gene flow. We also found that contrast in resistance values between habitat and non-habitat interacts with habitat extensiveness and fragmentation to affect detectability of landscape genetic relationships. Thus, the influence of landscape features critical to providing connectivity may not be detectable if gene flow is not limited by spatial patterns or resistance contrast of these features. We developed regression equations that reliably predict whether or not isolation by resistance will be detected independently of isolation by distance as a function of habitat fragmentation and contrast in resistance between habitat and non-habitat.