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.

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.

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.

Context

Mediterranean forests have been fragmented intensively over time, thereby yielding small and isolated forest remnants. They host a rich variety of epiphytes, which may be affected by landscape structure. Previous studies have analyzed the influence of habitat quality on these epiphytic communities, but there is little knowledge of the effects of other fragment features.

Objectives

We evaluated the impacts of forest loss and fragmentation on epiphytic communities (lichens and bryophytes) at plot and fragment scales after controlling the variation in forest structure and management.

Methods

We considered 40 fragments of dense oak forests in a human-modified landscape. We quantified their spatial attributes (size and shape), the quality of the surrounding matrix and the forest stand structure. We modeled community traits, and the presence and abundance of species at fragment and plot scales.

Results

Fragment size, shape, and the quality of the surrounding matrix were key factors that affected epiphytic richness and diversity. Larger and more regularly shaped fragments hosted the richest and most diverse communities, possibly offering a larger core area and thus favoring the entry of typical forest species. A high-contrast matrix was only favorable in small fragments, probably allowing the arrival of propagules. The species-level response was highly variable.

Conclusions

Landscape structure provides powerful explanations of the richness and diversity losses among epiphytes. Forest management should ensure the retention of the largest possible continuous forests. The management strategy of the matrix will depend on the conservation goal, since we observed different effects related with quality and fragment size.

Context

The Brazilian Cerrado, a global biodiversity hotspot, is being converted to agricultural production. Amphibians in particular are susceptible to agricultural practices that threaten both their wetland and upland habitats. Although local site variables are important for determining species occurrence, site occupancy is also mediated by the broader landscape and management context in which the site occurs.

Objectives

Investigate the relative effects of broad-, intermediate-, and local-scale factors on species occurrence for pond-breeding anurans within different landscapes across an agricultural-disturbance gradient in the Cerrado.

Methods

Ponds were surveyed for adult anurans over 3 years within 18 landscapes (each 625 km²) that varied in their degree of agricultural land use (landscape context). We analyzed species distribution models for eight pond-breeding anurans, using hierarchical binomial generalized linear models.

Results

The broader landscape context had a significant effect on the incidence of pond-breeding anurans, even after accounting for variation in other environmental factors at more local (pond) or intermediate (1-km²) scales. The top-ranked models for most species included some combination of broad-, intermediate- and local-scale factors, however. These covariates influenced species occurrence in different ways, with the response to agricultural disturbance varying among species. Although some species were negatively affected, others appeared to benefit from agricultural activities that increased breeding habitat (e.g., impoundments to provide water for cattle).

Conclusions

Landscape context, the degree to which landscapes have been transformed by agricultural land use, has a major influence on the distribution of pond-breeding anurans in the Brazilian Cerrado.

Context

Spatial variation in abundance is influenced by local- and landscape-level environmental variables, but modeling landscape effects is challenging because the spatial scales of the relationships are unknown. Current approaches involve buffering survey locations with polygons of various sizes and using model selection to identify the best scale. The buffering approach does not acknowledge that the influence of surrounding landscape features should diminish with distance, and it does not yield an estimate of the unknown scale parameters.

Objectives

The purpose of this paper is to present an approach that allows for statistical inference about the scales at which landscape variables affect abundance.

Methods

Our method uses smoothing kernels to average landscape variables around focal sites and uses maximum likelihood to estimate the scale parameters of the kernels and the effects of the smoothed variables on abundance. We assessed model performance using a simulation study and an avian point count dataset.

Results

The simulation study demonstrated that estimators are unbiased and produce correct confidence interval coverage except in the rare case in which there is little spatial autocorrelation in the landscape variable. Canada warbler abundance was more highly correlated with site-level measures of NDVI than landscape-level NDVI, but the reverse was true for elevation. Canada warbler abundance was highest when elevation in the surrounding landscape, defined by an estimated Gaussian kernel, was between 1300 and 1400 m.

Conclusions

Our method provides a rigorous way of formally estimating the scales at which landscape variables affect abundance, and it can be embedded within most classes of statistical models.

Context

No single model can capture the complex species range dynamics under changing climates—hence the need for a combination approach that addresses management concerns.

Objective

A multistage approach is illustrated to manage forested landscapes under climate change. We combine a tree species habitat model—DISTRIB II, a species colonization model—SHIFT, and knowledge-based scoring system—MODFACs, to illustrate a decision support framework.

Methods

Using shortleaf pine (Pinus echinata) and sugar maple (Acer saccharum) as examples, we project suitable habitats under two future climate change scenarios (harsh, Hadley RCP8.5 and mild CCSM RCP4.5 at ~2100) at a resolution of 10 km and assess the colonization likelihood of the projected suitable habitats at a 1 km resolution; and score biological and disturbance factors for interpreting modeled outcomes.

Results

Shortleaf pine shows increased habitat northward by 2100, especially under the harsh scenario of climate change, and with higher possibility of natural migration confined to a narrow region close to the current species range boundary. Sugar maple shows decreased habitat and has negligible possibility of migration within the US due to a large portion of its range being north of the US border. Combination of suitable habitats with colonization likelihoods also allows for identification of potential locations appropriate for assisted migration, should that be deemed feasible.

Conclusion

The combination of these multiple components using diverse approaches leads to tools and products that may help managers make management decisions in the face of a changing climate.

Context

Organisms commonly respond to their environment across a range of scales, however many habitat selection studies still conduct selection analyses using a single-scale framework. The adoption of multi-scale modeling frameworks in habitat selection studies can improve the effectiveness of these studies and provide greater insights into scale-dependent relationships between species and specific habitat components.

Objectives

Our study assessed multi-scale nest/roost habitat selection of the federally “Threatened” Mexican spotted owl (Strix occidentalis lucida) in northern Arizona, USA in an effort to provide improved conservation and management strategies for this subspecies.

Methods

We conducted multi-scale habitat modeling to assess habitat selection by Mexican spotted owls using survey data collected by the USFS. Each selected covariate was included in multi-scale models at their “characteristic scale” and we used an all-subsets approach and model selection framework to assess habitat selection.

Results

The “characteristic scale” identified for each covariate varied considerably among covariates and results from multi-scale models indicated that percent canopy cover and slope were the most important covariates with respect to habitat selection by Mexican spotted owls. Multi-scale models consistently outperformed their analogous single-scale counterparts with respect to the proportion of deviance explained and model predictive performance.

Conclusions

Efficacy of future habitat selection studies will benefit by taking a multi-scale approach. In addition to potentially providing increased explanatory power and predictive capacity, multi-scale habitat models enhance our understanding of the scales at which species respond to their environment, which is critical knowledge required to implement effective conservation and management strategies.