Hierarchical relationships between landscape structure and temperature in a managed forest landscape

Management may influence abiotic environments differently across time and spatial scale, greatly influencing perceptions of fragmentation of the landscape. It is vital to consider a priori the spatial scales that are most relevant to an investigation, and to reflect on the influence that scale may have on conclusions. While the importance of scale in understanding ecological patterns and processes has been widely recognized, few researchers have investigated how the relationships between pattern and process change across spatial and temporal scales. We used wavelet analysis to examine the multiscale structure of surface and soil temperature, measured every 5 m across a 3820 m transect within a national forest in northern Wisconsin. Temperature functioned as an indicator – or end product – of processes associated with energy budget dynamics, such as radiative inputs, evapotranspiration and convective losses across the landscape. We hoped to determine whether functional relationships between landscape structure and temperature could be generalized, by examining patterns and relationships at multiple spatial scales and time periods during the day. The pattern of temperature varied between surface and soil temperature and among daily time periods. Wavelet variances indicated that no single scale dominated the pattern in temperature at any time, though values were highest at finest scales and at midday. Using general linear models, we explained 38% to 60% of the variation in temperature along the transect. Broad categorical variables describing the vegetation patch in which a point was located and the closest vegetation patch of a different type (landscape context) were important in models of both surface and soil temperature across time periods. Variables associated with slope and microtopography were more commonly incorporated into models explaining variation in soil temperature, whereas variables associated with vegetation or ground cover explained more variation in surface temperature. We examined correlations between wavelet transforms of temperature and vegetation (i.e., structural) pattern to determine whether these associations occurred at predictable scales or were consistent across time. Correlations between transforms characteristically had two peaks; one at finer scales of 100 to 150 m and one at broader scales of ^>300 m. These scales differed among times of day and between surface and soil temperatures. Our results indicate that temperature structure is distinct from vegetation structure and is spatially and temporally dynamic. There did not appear to be any single scale at which it was more relevant to study temperature or this pattern-process relationship, although the strongest relationships between vegetation structure and temperature occurred within a predictable range of scales. Forest managers and conservation biologists must recognize the dynamic relationship between temperature and structure across landscapes and incorporate the landscape elements created by temperature-structure interactions into management decisions.     

Modeling exurban development near Washington, DC, USA: comparison of a pattern-based model and a spatially-explicit econometric model

The development of private rural lands can significantly fragment landscapes, with potentially negative consequences on ecosystem services. Models of land-use trends beyond the urban fringe are therefore useful for developing policy to manage these environmental effects. However, land-use change models have been primarily applied in urban environments, and it is unclear whether they can adequately predict exurban growth. This study compared the ability of two urban growth models to project exurban development in north-central Virginia and western Maryland over a 24-year period. Pattern-based urban growth models (such as SLEUTH) are widely used, but largely mimic patterns that emerge from historic conditions rather than allowing landowner decision-making to project change. In contrast, spatially-explicit econometric models (such as the complementary log?Clog hazard assessed in this study) model landowner choices as profit-maximizing behavior subject to market and regulatory constraints. We evaluated the two raster-based models by comparing model predictions to observed exurban conversion at pixel and county scales. The SLEUTH model was more successful at matching the total amount of new growth at the county scale than it was at the pixel scale, suggesting its most appropriate use in exurban areas is as a blunt instrument to forewarn potential coarse-scale losses of natural resources. The econometric model performed significantly better than SLEUTH at both scales, although it was not completely successful in fulfilling its promise of projecting changes that were sensitive to policy. The lack of significance of some policy variables may have resulted from insufficient variation in drivers over our study area or time period, but also suggests that drivers of land use change in exurban environments may differ from those identified for urban areas.     

野生食用菌流通市场的资源整合与营销定位

总结野生食用菌流通模式、主体、渠道和市场结构分析结果,可知当前野生食用菌流通市场存在现货交易为主季节集中度高、交易规模小参与主体多、流通商品转化加工滞后与行政控制与完全市场化两级并存等问题。针对这些问题,进行野生食用菌流通市场的资源整合与营销定位,通过增强流通市场体系意识、推动流通市场主体的组织化发展、提升现有流通市场功能与完善流通市场规则等整合市场资源,优化资源配置,加强内控管理机制;利用野生食用菌流通市场营销中商品差异化、价格、销售渠道与促销等定位,树立市场外在优质形象,发觉商机,拓展市场。通过资源整合与营销定位的内外结合发展壮大野生食用菌流通市场。     

河北省农产品流通体系效率测评与影响因素研究

河北省是农业大省,农产品流通效率对农业农村优先发展、农业增效和农民增收有重要影响,研究全省农产品流通规律、测评流通体系效率,对确保"菜篮子"和"米袋子"安全有重要的现实意义。该研究选取固定资产、劳动力、交通基础设施3个投入指标,采用数据包络分析法构建DEA-BCC模型,测算出2008—2017年河北省农产品流通体系投入产出效率的变化规律,为更好的反映河北省流通体系效率的主要制约因素,选择多元回归分析法,对影响农产品流通体系平稳运行的制约因素进行分析,按照注重技术投入与规模匹配的思路,提出了建立信息共享平台,健全食品检测系统、大力发展冷链物流、加强人才培训等提升河北省农产品流通体系效率的对策建议。     

规模化苹果专业户土地流转行为分析

土地流转一直是学术界和政府关注的重点问题,而土地流转对于专业化农户的规模化生产至关重要。为探究规模化苹果专业户的土地流转特征及其影响因素,该研究基于全国苹果主产区908个规模化苹果专业户微观数据,对规模化苹果专业户土地流转行为进行统计分析。结果表明:1)规模化苹果专业户更倾向于流入土地,且规模大小对土地流转发生率有正向促进作用;2)农业收入占比高的专业户相比于兼业化农户更倾向于土地流转;3)高技术专业户倾向于流入土地,而低技术专业户一般选择维持现有规模;4)清晰的产权制度、连片的土地、参加合作社、完善的关系网络对土地流转决策有正向影响。针对上述研究结论,提出了相关的政策建议,以期为更好地引导农户合理配置土地资源提供相关依据。     

Multi-scale prediction of landscape resistance for tiger dispersal in central India

Context

Connectivity models for animal movement frequently use resistance surfaces, but rarely incorporate actual movement data and multiple scale drivers of landscape resistance.

Objectives

Using GPS data, we developed a multi-scale model of landscape resistance for tiger (Panthera tigris) dispersal in central India and evaluated the performance, interpretation and predictions against single scale models.

Methods

Six dispersing tiger paths were subjected to a path level analysis with conditional logistic regression to parameterize a resistance surface. We evaluated for 21 scales of available habitat and selected the best scale for each variable. We derived a scale-optimized multivariate path selection function and predicted landscape resistance across the landscape.

Results

The tigers preferred to move along areas with forest cover at relatively high elevations along the ridges with rugged topography at broad scale, while avoiding areas with agriculture-village matrix at fine scale. We found that the scale that was most supported by Akaike’s information criterion was not always the scale that maximized the magnitude (effect size) of the relationship. Further, the multi-scale optimized model differed substantially from the single scale models in terms of variable importance, magnitude of coefficients and predictions of connectivity.

Conclusions

Our results demonstrate that the variables in landscape resistance models produce markedly different predictions of population connectivity depending on the scales of analyses and interpretation. Thus, scale optimization in parameterization is critical for appropriate inferences and sound management strategies.

     

The Importance of Spatial Scale in Habitat Models: Capercaillie in the Swiss Alps

The role of scale in ecology is widely recognized as being of vital importance for understanding ecological patterns and processes. The capercaillie (Tetrao urogallus) is a forest grouse species with large spatial requirements and highly specialized habitat preferences. Habitat models at the forest stand scale can only partly explain capercaillie occurrence, and some studies at the landscape scale have emphasized the role of large-scale effects. We hypothesized that both the ability of single variables and multivariate models to explain capercaillie occurrence would vary with the spatial scale of the analysis. To test this hypothesis, we varied the grain size of our analysis from 1 to just over 1100 hectares and built univariate and multivariate habitat suitability models for capercaillie in the Swiss Alps. The variance explained by the univariate models was found to vary among the predictors and with spatial scale. Within the multivariate models, the best single-scale model (using all predictor variables at the same scale) worked at a scale equivalent to a small annual home range. The multi-scale model, in which each predictor variable was entered at the scale at which it had performed best in the univariate model, did slightly better than the best single-scale model. Our results confirm that habitat variables should be included at different spatial scales when species-habitat relationships are investigated.     

Surface metrics for landscape ecology: a comparison of landscape models across ecoregions and scales

Context

The patch-mosaic model is lauded for its conceptual simplicity and ease with which conventional landscape metrics can be computed from categorical maps, yet many argue it is inconsistent with ecological theory. Gradient surface models (GSMs) are an alternative for representing landscapes, but adoption of surface metrics for analyzing spatial patterns in GSMs is hindered by several factors including a lack of meaningful interpretations.

Objectives

We investigate the performance and applicability of surface metrics across a range of ecoregions and scales to strengthen theoretical foundations for their adoption in landscape ecology.

Methods

We examine metric clustering across scales and ecoregions, test correlations with patch-based metrics, and provide ecological interpretations for a variety of surface metrics with respect to forest cover to support the basis for selecting surface metrics for ecological analyses.

Results

We identify several factors complicating the interpretation of surface metrics from a landscape perspective. First, not all surface metrics are appropriate for landscape analyses. Second, true analogs between surface metrics and patch-based, landscape metrics are rare. Researchers should focus instead on how surface measures can uniquely measure spatial patterns. Lastly, scale dependencies exist for surface metrics, but relationships between metrics do not appear to change considerably with scale.

Conclusions

Incorporating gradient surfaces into landscape ecological analyses is challenging, and many surface metrics may not have patch analogs or be ecologically relevant. For this reason, surface metrics should be considered in terms of the set of pattern elements they represent that can then be linked to landscape characteristics.

     

Spatial scaling of wildland fuels for six forest and rangeland ecosystems of the northern Rocky Mountains, USA

Wildland fuels are important to fire managers because they can be manipulated to achieve management goals, such as restoring ecosystems, decreasing fire intensity, minimizing plant mortality, and reducing erosion. However, it is difficult to accurately measure, describe, and map wildland fuels because of the great variability of wildland fuelbed properties over space and time. Few have quantified the scale of this variability across space to understand its effect on fire spread, burning intensity, and ecological effects. This study investigated the spatial variability of loading (biomass) across major surface and canopy fuel components in low elevation northern Rocky Mountain forest and rangeland ecosystems to determine the inherent scale of surface fuel and canopy fuel distributions. Biomass loadings (kg?m^?2) were measured for seven surface fuel components??four downed dead woody fuel size classes (0?C6?mm, 6?C25?mm, 25?C75?mm, and 75?+?mm), duff plus litter, shrub, and herb??using a spatially nested plot sampling design within a 1?km² square sampling grid installed at six sites in the northern US Rocky Mountains. Bulk density, biomass, and cover of the forest canopy were also measured for each plot in the grid. Surface fuel loadings were estimated using a combination of photoload and destructive collection methods at many distances within the grid. We quantified spatial variability of fuel component loading using spatial variograms, and found that each fuel component had its own inherent scale with fine fuels varying at scales of 1?C5?m, coarse fuels at 10?C150?m, and canopy fuels from 100 to 500?m. Using regression analyses, we computed a scaling factor of 4.6?m for fuel particle diameter (4.6?m increase in scale with each cm increase in particle diameter). Findings from this study can be used to design fuel sampling projects, classify fuelbeds, and map fuel characteristics, such as loading, to account for the inherent scale of fuel distributions to get more accurate fuel loading estimations.     

Historic landscape change and habitat loss: the case of black grouse in Lower Saxony,Germany

The declines of many specialist bird species in the agricultural landscapes of Central Europe have resulted in small and isolated populations. In the case of the black grouse, a ground-nesting bird species with large spatial requirements, empiric evidence about underlying landscape changes is scarce. In this study, we examined land cover and land cover changes in a farmland-forest mosaic in eastern Lower Saxony, Germany and how they affect occurrence and persistence of black grouse. Spatial information came from historic topographic maps from 1958 to 1975. The results show profound conversions of habitat to forest and farmland but also an increase in settlement area. Habitat conversions and suburbanization were negative correlates of black grouse persistence. Habitat models from before and after a decline period differed in some of the predictors and suggest black grouse habitat to be more diverse before the land cover changes. Our study confirms that land use factors at a landscape scale extent contribute to explain black grouse occurrence and thus can complement important small scale factors like the quality and size of individual habitat patches. Results also show that landscape factors affect black grouse distribution predominantly from an area much greater than an individual black grouse home range. Our models may be further evaluated on present-day landscapes and might be used to evaluate large-scale habitat availability for black grouse.     

Mitigating surface urban heat island by a tree protection policy: A case study of The Woodland,Texas, USA

The Woodlands Township, TX, has a tree protection policy that consists of tree removal permits and minimum tree and shrub cover regulations. This paper examined the effect of The Woodlands’ tree protection policy on surface urban heat island (SUHI) at the neighborhood scale by comparing the mean land surface temperatures (LSTs) derived from 37 thermal infrared bands of Landsat TM images between The Woodlands’ neighborhoods and nearby control neighborhoods without such a policy. To rule out the effect of confounding factors that may influence LSTs, the control neighborhoods were selected to be similar in physical and socioeconomic status to The Woodlands’ neighborhoods. LSTs of The Woodlands’ neighborhoods were, on average, 1.5–3.9 °C lower than those of the control neighborhoods. The cooling effect of The Woodlands’ tree protection policy was more prominent in summer when SUHI mitigation was mostly needed. Based on these findings, it can be concluded that a local tree protection policy is effective in mitigating SUHI at the neighborhood scale.     

The Mediterranean vegetation: what if the atmospheric CO2 increased?

The atmospheric CO₂ content is expected to continue to increase and probably induce warming at the global scale during the next century. The impact of such an increase will affect the composition and distribution of ecosystems on the same scale. To predict the integrated whole-ecosystem response to the CO₂ increase in the Mediterranean region we used a vegetation biogeochemical model. This model (BIOME3) integrates monthly temperature and precipitation, some soil characteristics, cloudiness and CO₂ concentration as inputs to simulate the vegetation in terms of biomes.First we demonstrate the ability of the model to simulate past vegetation when tested versus pollen data. Second we use the vegetation model for different climate scenarios and report results of future changes in the Mediterranean vegetation.These simulations indicate that an increase of the atmospheric CO₂ to 500 ppmv, jointly with an increase of about 2 °C of the mean annual temperature, as simulated by several atmospheric general circulation models, should be accompanied by a severe reduction (more than 30%) of the present annual precipitation to change significantly the present vegetation surrounding the Mediterranean. When precipitation is maintained at its present-day level, an evergreen forest spreads in the eastern Mediterranean and a conifer forest in Turkey. In NW Africa, a woody xerophytic vegetation occupies a more extensive territory than today and replaces part of the present steppe area.     

Landscape patterns as habitat predictors: building and testing models for cavity-nesting birds in the Uinta Mountains of Utah,USA

The ability to predict species occurrences quickly is often crucial for managers and conservation biologists with limited time and funds. We used measured associations with landscape patterns to build accurate predictive habitat models that were quickly and easily applied (i.e., required no additional data collection in the field to make predictions). We used classification trees (a nonparametric alternative to discriminant function analysis, logistic regression, and other generalized linear models) to model nesting habitat of red-naped sapsuckers (Sphyrapicus nuchalis), northern flickers (Colaptes auratus),tree swallows (Tachycineta bicolor), and mountain chickadees (Parus gambeli) in the Uinta Mountains of northeastern Utah, USA. We then tested the predictive capability of the models with independent data collected in the field the following year. The models built for the northern flicker, red-naped sapsucker, and tree swallow were relatively accurate (84%, 80%, and 75% nests correctly classified,respectively)compared to the models for the mountain chickadee (50% nests correctly classified). All four models were more selective than a null model that predicted habitat based solely on a gross association with aspen forests. We conclude that associations with landscape patterns can be used to build relatively accurate, easy to use, predictive models for some species. Our results stress, however, that both selecting the proper scale at which to assess landscape associations and empirically testing the models derived from those associations are crucial for building useful predictive models. This revised version was published online in July 2006 with corrections to the Cover Date.     

Integrating local breeding pond, landcover, and climate factors in predicting amphibian distributions

Species distributions are influenced by many processes operating over varying spatial scales. The development of species distribution models (SDMs), also known as ecological niche models, has afforded the opportunity to predict the distributions of diverse taxa across broad geographic areas and identify variables that are potentially important in regulating these distributions. However, the integration of site-specific habitat data with broad scale climate and landcover data has received limited attention in an SDM framework. We investigate whether SDMs developed with breeding pond, landcover, and climate variables can accurately predict the distributions of nine pond-breeding amphibians in eastern Missouri, USA. Additionally we investigate the relative influences of each environmental variable on the distribution predictions for each study species, and whether the most influential variables are shared among multiple taxa. Boosted regression tree (BRT) SDMs were developed for each species with 38 abiotic and biotic environmental variables, including data from the breeding ponds, surrounding landcover, and climate. To test the models, field surveys were performed in 2007 and 2008 at 103 ponds for nine amphibian species. BRT models developed with breeding pond, landcover, and climate data accurately predicted the occurrences of six of nine species across the study area. Furthermore, the presence of each species was best predicted by a unique combination of environmental variables. Results also suggest that landcover and climate factors may be more influential for species near the edge of their geographic ranges, while local breeding pond factors may be more important for species nearer to the center of their ranges.     

Does the scale of our observational window affect our conclusions about correlations between endangered salmon populations and their habitat?

Differences in the strength of species-habitat relationships across scales provide insights into the mechanisms that drive these relationships and guidance for designing in situ monitoring programs, conservation efforts and mechanistic studies. The scale of our observation can also impact the strength of perceived relationships between animals and habitat conditions. We examined the relationship between geographic information system (GIS)-based landscape data and Endangered Species Act-listed anadromous Pacific salmon (Oncorhynchus spp.) populations in three subbasins of the Columbia River basin, USA. We characterized the landscape data and ran our models at three spatial scales: local (stream reach), intermediate (6th field hydrologic units directly in contact with a given reach) and catchment (entire drainage basin). We addressed three questions about the effect of scale on relationships between salmon and GIS representations of landscape conditions: (1) at which scale does each predictor best correlate with salmon redd density, (2) at which scale is overall model fit maximized, and (3) how does a mixed-scale model compare with single scale models (mixed-scale meaning models that contain variables characterized at different spatial scales)? We developed mixed models to identify relationships between redd density and candidate explanatory variables at each of these spatial scales. Predictor variables had the strongest relationships with redd density when they were summarized over the catchment scale. Meanwhile strong models could be developed using landscape variables summarized at only the local scale. Model performance did not improve when we used suites of potential predictors summarized over multiple scales. Relationships between species abundance and land use or intrinsic habitat suitability detected at one scale cannot necessarily be extrapolated to other scales. Therefore, habitat restoration efforts should take place in the context of conditions found in the associated watershed or landscape.     

Identifying significant scale-specific spatial boundaries using wavelets and null models: spruce budworm defoliation in Ontario,Canada as a case study

We combine wavelet analysis and multiple null models to identify significant spatial scales of pattern and spatial boundaries in historical spruce budworm defoliation in Ontario, Canada. Previous analyses of budworm defoliation in Ontario over the last two outbreaks have suggested three distinct zones of defoliation. We asked the following three questions: (1) is there statistical support for the existence of these three zones? (2) Are the locations of these boundaries consistent between outbreak periods? And (3) how does boundary identification depend on the spatial null model used? Defoliation data for the two outbreak periods (1941–1965 and 1966–2001), and the combined period (1941–2001) were analyzed using a 1D continuous wavelet transform. Boundaries were identified through comparison of wavelet power spectra of each outbreak period to reference distributions based on three different spatial null models: (1) a complete spatial randomness model, (2) an autoregressive model, and (3) a Gaussian random field model. The Gaussian random field model identified coarser scales of pattern than the autoregressive model. Locally, the Gaussian random field model found significant boundaries similar to those previously described, whereas the autoregressive model only did so for the first outbreak. These results indicate that the coarse scale spatial factors that influenced defoliation were more consistent between outbreaks relative to fine scale factors, and that previously described boundaries were strongly driven by the first outbreak. Wavelet analysis combined with spatial null models provides a powerful tool for identifying non-arbitrary scales of structure and significant spatial boundaries in non-stationary ecological data.     

A positive response of mountain pine beetle to pine forest-clearcut edges at the landscape scale in British Columbia,Canada

In British Columbia, large-scale salvage harvesting has been underway to recover timber value from forest stands infested by mountain pine beetle during the current outbreak. Understanding the response of beetles to clearcut edges particularly at the landscape scale is crucial to understanding the impacts of increased habitat fragmentation due to salvage harvesting on the spread of the beetle infestations. A novel proximity analysis approach based on null models of complete spatial randomness with three different spatial extents was developed to examine the spatial patterns of infestations in relation to cutblocks. Inhomogeneous Poisson point process models were fitted to predict how intensities of infestations varied with distances to the nearest cutblocks. Marked Poisson point process models were also fitted to evaluate the effects of the variables associated with the nearest cutblocks and adjacent infested pine stands on the edge response of beetles. The results clearly illustrated a significant positive edge response of beetles at the landscape scale. The intensities of infestations decreased non-linearly with distances to the nearest cutblocks. The results also suggested that the quality and distribution of key habitat resources could not fully explain the fundamental mechanisms underlying the edge response. The behavioural change of beetle dispersal at edges may also be an important factor contributing to a positive edge response. The results from this study may be useful in improving the efficacy of mountain pine beetle management efforts.     

Importance of agricultural landscapes to nesting burrowing owls in the Northern Great Plains, USA

Anthropogenic habitat loss and fragmentation are the principle factors causing declines of grassland birds. Declines in burrowing owl (Athene cunicularia) populations have been extensive and have been linked to habitat loss, primarily the decline of black-tailed prairie dog (Cynomys ludovicianus) colonies. Development of habitat use models is a research priority and will aid conservation of owls inhabiting human-altered landscapes. From 2001 to 2004 we located 160 burrowing owl nests on prairie dog colonies on the Little Missouri National Grassland in North Dakota. We used multiple linear regression and Akaike’s Information Criterion to estimate the relationship between cover type characteristics surrounding prairie dog colonies and (1) number of owl pairs per colony and (2) reproductive success. Models were developed for two spatial scales, within 600 m and 2,000 m radii of nests for cropland, crested wheatgrass (Agropyron cristatum), grassland, and prairie dog colonies. We also included number of patches as a metric of landscape fragmentation. Annually, fewer than 30% of prairie dog colonies were occupied by owls. None of the models at the 600 m scale explained variation in number of owl pairs or reproductive success. However, models at the 2,000 m scale did explain number of owl pairs and reproductive success. Models included cropland, crested wheatgrass, and prairie dog colonies. Grasslands were not included in any of the models and had low importance values, although percentage grassland surrounding colonies was high. Management that protects prairie dog colonies bordering cropland and crested wheatgrass should be implemented to maintain nesting habitat of burrowing owls.     

The effect of street trees and amenity grass on urban surface water runoff in Manchester,UK

It is well known that the process of urbanization alters the hydrological performance of an area, reducing the ability of urban areas to cope with heavy rainfall events. Previous investigations into the role that trees can play in reducing surface runoff have suggested they have low impact at a city wide scale, though these studies have often only considered the interception value of trees.This study assessed the impact of trees upon urban surface water runoff by measuring the runoff from 9 m² plots covered by grass, asphalt, and asphalt with a tree planted in the centre. It was found that, while grass almost totally eliminated surface runoff, trees and their associated tree pits, reduced runoff from asphalt by as much as 62%. The reduction was more than interception alone could have produced, and relative to the canopy area was much more than estimated by many previous studies. This was probably because of infiltration into the tree pit, which would considerably increase the value of urban trees in reducing surface water runoff.     

Thresholds in seascape connectivity: influence of mobility, habitat distribution, and current strength on fish movement

Assessing connectivity of the marine environment is a fundamental challenge for marine conservation and planning, yet conceptual development in habitat connectivity has been based on terrestrial examples rather than marine ecosystems. Here, we explore differences in marine environments that could affect localized movement of marine organisms and demonstrate the importance of incorporating them into seascape models. We link a fish-based cost surface model to simulated seascapes to test hypotheses about the effects of fish mobility, water current strength, and their interactions on functional connectivity of a seascape. Our models predict that sedentary fish should be more sensitive to habitat change than more mobile fish. Furthermore, highly mobile fish should be more sensitive to water currents than habitat change. In our models, the cost of swimming against a current (of any strength) exceeded its benefits, resulting in overall decreases in connectivity with increasing current strengths. We further hypothesized that thresholds in functional connectivity will be affected by both fish mobility and water current strength. Connectivity thresholds in the models occurred when 10–50 % of benthic habitat was favourable; below these thresholds there was a rapid increase in path cost. Thresholds were influenced by the interaction of relative habitat costs (simulated fish mobility) and habitat fragmentation: thresholds for less mobile fish (higher relative cost) were reached at lower habitat abundance when habitat was fragmented, while thresholds for mobile fish were less affected by fragmentation. Our approach suggests mobility and water current are useful indicators of connectivity in marine environments and should be incorporated in seascape models.