Butterfly dispersal in farmland: a replicated landscape genetics study on the meadow brown butterfly (<Emphasis Type="Italic">Maniola jurtina</Emphasis>)

Context

Anthropogenic activities readily result in the fragmentation of habitats such that species persistence increasingly depends on their ability to disperse. However, landscape features that enhance or limit individual dispersal are often poorly understood. Landscape genetics has recently provided innovative solutions to evaluate landscape resistance to dispersal.

Objectives

We studied the dispersal of the common meadow brown butterfly, Maniola jurtina, in agricultural landscapes, using a replicated study design and rigorous statistical analyses. Based on existing behavioral and life history research, we hypothesized that the meadow brown would preferentially disperse through its preferred grassy habitats (meadows and road verges) and avoid dispersing through woodlands and the agricultural matrix.

Methods

Samples were collected in 18 study landscapes of 5 × 5 km in three contrasting agricultural French regions. Using circuit theory, least cost path and transect-based methods, we analyzed the effect of the landscape on gene flow separately for each sex.

Results

Analysis of 1681 samples with 6 microsatellites loci revealed that landscape features weakly influence meadow brown butterfly gene flow. Gene flow in both sexes appeared to be weakly limited by forests and arable lands, whereas grasslands and grassy linear elements (road verges) were more likely to enhance gene flow.

Conclusion

Our results are consistent with the hypothesis of greater dispersal through landscape elements that are most similar to suitable habitat. Our spatially replicated landscape genetics study allowed us to detect subtle landscape effects on butterfly gene flow, and these findings were reinforced by consistent results across analytical methods.

     

Prey distribution,potential landscape supplementation,and urbanization affect occupancy dynamics of American mink in streams

Context

Land-use change can reduce and isolate suitable habitat generating spatial variation in resource availability. Improving species distribution models requires a multi-scale understanding of resource requirements and species’ sensitivities to novel landscapes.

Objectives

We investigated how the spatial distribution of supplementary habitats (permanent wetlands), urbanization, water depths, and distribution of a key prey species (muskrat; Ondatra zibethicus) influence occupancy dynamics of American mink (Neovison vison). Although mink are widespread across North America and a destructive invasive species in Europe, South America, and Asia, we have a limited understanding of factors affecting their spatial distribution.

Methods

We used 6 years of presence–absence data (2007–2012) to evaluate occupancy dynamics of mink at 58–90 stream sites along an urbanization gradient in Illinois, USA. We predicted negative relationships between stream occupancy and urban land cover and distance from permanent wetlands, and positive associations with muskrat presence, water depth, and riparian zone width.

Results

Contrary to our hypothesis, stream sites closer to permanent wetlands had lower occupancy and colonization rates for mink. Occupancy and colonization rates were higher at sites with deeper water, and colonization rates were related negatively to urbanization. Mink were more likely to leave stream habitat if muskrats were not present and permanent wetlands were nearby.

Conclusion

Factors interplaying across multiple scales influenced occupancy dynamics of mink in stream habitat in a highly modified landscape. Our results highlight the importance of considering both direct measures of prey availability and the spatial distribution of supplementary habitats to improve habitat-selection models for carnivores.

     

Applying fire connectivity and centrality measures to mitigate the cheatgrass-fire cycle in the arid West,USA

Context

Strategic placement of fuel treatments across large landscapes is an important step to mitigate the collective effects of fires interacting over broad spatial and temporal extents. On landscapes where highly invasive cheatgrass (Bromus tectorum) is increasing fire activity, such an approach could help maintain landscape resilience.

Objectives

Our objectives are to 1) model and map fire connectivity on a cheatgrass-invaded landscape, as well as the centrality of large cheatgrass patches, in order to inform a landscape fuel treatment (i.e., a network of greenstrips); and 2) evaluate the modeled greenstrip network based on changes to cheatgrass patch centrality.

Methods

Our analysis covers 485-km² on the Kaibab National Forest in Northern Arizona. We apply a circuit-theoretic model of fire connectivity between all pairs of large cheatgrass patches. Based on these results, we calculate a measure of centrality for each patch to inform fuel treatment placement. We evaluate the modeled greenstrip network by comparing the pre- and post-treatment centrality of each patch.

Results

After modeling fire connectivity across the landscape, we identify 25 of 68 large cheatgrass patches with relatively high centrality. When we simulate greenstrips around these focal patches, model results suggest that they are effective in reducing the centrality for at least 19 of the 25 patches.

Conclusions

Fire connectivity models provide robust network centrality measures, which can help generate multiple, landscape fuel treatment alternatives and facilitate on-the-ground decisions. The extension of these methods is well suited for landscape fuels management in other vegetation communities and ecosystems.

     

Net primary productivity (NPP) dynamics and associated urbanization driving forces in metropolitan areas: a case study in Beijing City,China

Context

Eco-environmental effects of urbanization are a focus in landscape ecology.

Objective

The influences of population, economic and spatial development during the urbanization process in Beijing City, China on net primary productivity (NPP) were analyzed. The responding mechanism of NPP in different urbanization stages was also examined to develop advice about eco-environmental sustainability of urban development.

Methods

Using the Carnegie Ames Stanford Approach model, we estimated NPP. Using linear regression and polynomial regression analysis, we analyzed NPP responses to stages of urbanization.

Results

High NPP areas were located in northeast Yanqing, northwest Miyun, northern Huairou and Pinggu. The distribution of NPP generally occurred in the following order from high NPP to low NPP: outer suburbs, inner suburbs, encircled city center, and inner city. Because of the heat island effect in winter, the estimated NPP in the encircled city center and inner city was higher in 2009 than in 2001. There was a negative correlation between NPP and both economic and spatial urbanization, but an increase in population did not necessarily lead to an immediate decrease in NPP. An analysis of NPP dynamics in five kinds of urban development zones showed that urbanization resulted in a lasting and observable loss of NPP over time and space, although there was some promotion of NPP in highly urbanized zones.

Conclusion

There are three stages in the response of NPP to urbanization: damage stage, antagonistic stage, and coordination stage. The stage threshold depends on local eco-environmental management and urban planning interventions.

     

Temporal and spatial variation of a winter soundscape in south-central Alaska

Context

Winter soundscapes are likely different from soundscapes in other seasons considering wildlife vocalizations (biophony) decrease, wind events (geophony) increase, and winter vehicle noise (technophony) occurs. The temporal variation and spatial relationships of soundscape components to the landscape in winter have not been quantified and described until now.

Objectives

Our objectives were to determine the temporal and spatial variation and acoustic–environmental relationships of a winter soundscape in south-central Alaska.

Methods

We recorded ambient sounds at 62 locations throughout Kenai National Wildlife Refuge (December 2011–April 2012). We calculated the normalized power spectral density in 59,597 recordings and used machine learning to determine acoustic–environmental relationships and produce spatial models of soundscape components.

Results

Geophony was the most prevalent component (84 %) followed by technophony (15 %), and biophony (1 %). Geophony occurred primarily at night, varied little by month, and was strongly associated with lakes. Technophony and biophony had similar temporal variation, peaking in April. Technophony occurred closer to urban areas and at locations with high snowmobile activity. Biophony occurred closer to rivers and was inversely related to snowmobile activity. Over 75 % of sample sites had >1 recordings of airplane or snowmobile noise, mainly in remote areas.

Conclusions

The soundscape displayed distinct patterns across 24-h and monthly timeframes. These patterns were strongly associated with land cover variables which demonstrate discrete acoustic–environmental relationships exhibiting distinct spatial patterns in the landscape. Despite the predominance of geophony, the presence of technophony in this winter soundscape may have significant negative effects to wildlife and wilderness quality.

     

Diversity–disturbance relationship in forest landscapes

Context

Despite decades of research, there is an intense debate about the consistency of the hump-shaped pattern describing the relationship between diversity and disturbance as predicted by the intermediate disturbance hypothesis (IDH). Previous meta-analyses have not explicitly considered interactive effects of disturbance frequency and intensity of disturbance on plant species diversity in terrestrial landscapes.

Objective

We conducted meta-analyses to test the applicability of IDH by simultaneously examining the relationship between species richness, disturbance frequency (quantified as time since last disturbance as originally proposed) and intensity of disturbance in forest landscapes.

Methods

The effects of disturbance frequency, intensity, and their interaction on species richness was evaluated using a mixed-effects model.

Results

We found that species richness peaks at intermediate frequency after both high and intermediate disturbance intensities, but the richness-frequency relationship differed between intensity classes.

Conclusions

Our study highlights the need to measure multiple disturbance components that could help reconcile conflicting empirical results on the effect of disturbance on plant species diversity.

     

Diverse landscapes have a higher abundance and species richness of spring wild bees by providing complementary floral resources over bees’ foraging periods

Context

Anthropogenic landscape simplification and natural habitat loss can negatively affect wild bees. Alternatively, anthropogenic land-use change may diversify landscapes, creating complementary habitats that maintain overall resource continuity and diversity.

Objectives

We examined the effects of landscape composition, including land-cover diversity and percent semi-natural habitat, on wild bee abundance and species richness within apples, a pollinator-dependent crop. We also explored whether different habitats within diverse landscapes can provide complementary floral resources for bees across space and time.

Methods

We sampled bees during apple bloom over 2 years within 35 orchards varying in surrounding landscape diversity and percent woodland (the dominant semi-natural habitat) at 1 km radii. To assess habitat complementarity in resource diversity and temporal continuity, we sampled flowers and bees within four unique habitats, including orchards, woodlands, semi-natural grasslands, and annual croplands, over three periods from April–June.

Results

Surrounding landscape diversity positively affected both wild bee abundance and richness within orchards during bloom. Habitats in diverse landscapes had different flower communities with varying phenologies; flowers were most abundant within orchards and woodlands in mid-spring, but then declined over time, while flowers within grasslands marginally increased throughout spring. Furthermore, bee communities were significantly different between the closed-canopy habitats, orchards and woodlands, and the open habitats, grasslands and annual croplands.

Conclusions

Our results suggest that diverse landscapes, such as ones with both open (grassland) and closed (woodland) semi-natural habitats, support spring wild bees by providing flowers throughout the entire foraging period and diverse niches to meet different species’ requirements.

     

Integrating land cover structure and functioning to predict biodiversity patterns: a hierarchical modelling framework designed for ecosystem management

Context

Land-use/land-cover (LU/LC) dynamics is one of the main drivers of global environmental change. In the last years, aerial and satellite imagery have been increasingly used to monitor the spatial extent of changes in LU/LC, deriving relevant biophysical parameters (i.e. primary productivity, climate and habitat structure) that have clear implications in determining spatial and temporal patterns of biodiversity, landscape composition and ecosystem services.

Objectives

An innovative hierarchical modelling framework was developed in order to address the influence of nested attributes of LU/LC on community-based ecological indicators.

Methods

Founded in the principles of the spatially explicit stochastic dynamic methodology (StDM), the proposed methodological advances are supported by the added value of integrating bottom-up interactions between multi-scaled drivers.

Results

The dynamics of biophysical multi-attributes of fine-scale subsystem properties are incorporated to inform dynamic patterns at upper hierarchical levels. Since the most relevant trends associated with LU/LC changes are explicitly modelled within the StDM framework, the ecological indicators’ response can be predicted under different social-economic scenarios and site-specific management actions. A demonstrative application is described to illustrate the framework methodological steps, supporting the theoretic principles previously presented.

Conclusions

We outline the proposed multi-model framework as a promising tool to integrate relevant biophysical information to support ecosystem management and decision-making.

     

What determines the spatial extent of landscape effects on species?

Context

Landscape ecologists are often interested in measuring the effects of an environmental variable on a biological response; however, the strength and direction of effect depend on the size of the area within which the environmental variable is measured. Thus a central objective is to identify the optimal spatial extent within which to measure the environmental variable, i.e. the “scale of effect”.

Objectives

Our objectives are (1) to provide a comprehensive summary of the hypotheses concerning what determines the scale of effect, (2) to provide predictions that can be tested in empirical studies, and (3) to show, with a review of the literature, that most of these predictions have so far been inadequately tested.

Methods

We propose 14 predictions derived from five hypotheses explaining what determines the scale of effect, and review the literature (if any) supporting each prediction. These predictions involve five types of factors: (A) species traits, (B) landscape variables, (C) biological responses (e.g. abundance vs. occurrence), (D) indirect influences, and (E) regional context of the study. We identify methodological issues that hinder estimation of the scale of effect.

Results

Of the 14 predictions, only nine have been tested empirically and only five have received some empirical support. Most support is from simulation studies. Empirical evidence usually does not support predictions.

Conclusions

The study of the spatial scale at which landscape variables influence biological outcomes is in its infancy. We provide directions for future research by clarifying predictions concerning the determinants of the scale of effect.