Butterfly community composition in fragmented habitats: effects of patch shape and spatial scale

Abstract:The relative importance of different bottom-up-mediated effects in shaping insect communities in tropical secondary forests are poorly understood. Here, we explore the roles of vegetation structure, forest age, local topography (valley vs. hill top) and soil variables in predicting fruit-feeding butterfly and tree community composition, and tree community composition in predicting fruit-feeding butterfly community composition, in different-aged naturally regenerating and primary forests of Kibale National Park, Uganda. We also examine which variables are best predictors of fruit-feeding butterfly species richness or diversity. Butterflies (88 species) were sampled with a banana-baited trap and trees (98 taxa) with a 40 × 20-m sampling plot at 80 sampling sites. The environmental variables explained 31% of the variation in the tree community composition, the best predictors being local topography, forest age and cover of Acanthus pubescens (a shrub possibly arresting succession). The fruit-feeding butterfly community composition was better predicted by tree community composition (explaining 10% of the variation) rather than vegetation structure, local topography or soil factors. Environmental variables and tree species richness (or diversity) were poor predictors of butterfly species richness (or diversity). Our results emphasize the importance of tree community to recovery of herbivorous insect communities in tropical secondary forests.

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Butterfly community structure in fragmented habitats

Ecology Letters ◽

10.1111/j.1461-0248.2000.00175.x ◽

2000 ◽

Vol 3 (5) ◽

pp. 449-456 ◽

Cited By ~ 180

Author(s):

I. Steffan-Dewenter ◽

T. Tscharntke

Keyword(s):

Community Structure ◽

Fragmented Habitats ◽

Butterfly Community

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Spatial scale of GIS‐derived categorical variables affects their ability to separate sites by community composition

Applied Vegetation Science ◽

10.3170/2008-7-18521 ◽

2008 ◽

Vol 11 (3) ◽

pp. 421-430 ◽

Cited By ~ 5

Author(s):

Emily A. Holt ◽

Bruce McCune ◽

Peter Neitlich

Keyword(s):

Spatial Scale ◽

Community Composition ◽

Categorical Variables

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Spatial scale structure soil bacterial communities across an Arctic landscape

Applied and Environmental Microbiology ◽

10.1128/aem.02220-20 ◽

2020 ◽

Author(s):

Lucie A Malard ◽

Muhammad Zohaib Anwar ◽

Carsten S Jacobsen ◽

David A Pearce

Keyword(s):

Bacterial Community ◽

Spatial Scale ◽

Community Composition ◽

Bacterial Communities ◽

Bacterial Community Composition ◽

Arctic Region ◽

Dispersal Limitation ◽

Sampling Strategies ◽

Autocorrelation Distance

Bacterial community composition is largely influenced by environmental factors, and this applies to the Arctic region. However, little is known about the role of spatial factors in structuring such communities. In this study, we evaluated the influence of spatial scale on bacterial community structure across an Arctic landscape. Our results showed that spatial factors accounted for approximately 10% of the variation at the landscape scale, equivalent to observations across the whole Arctic region, suggesting that while the role and magnitude of other processes involved in community structure may vary, the role of dispersal may be stable globally in the region. We assessed dispersal limitation by identifying the spatial autocorrelation distance, standing at approximately 60 m, which would be required in order to obtain fully independent samples and may inform future sampling strategies in the region. Finally, indicator taxa with strong statistical correlations with environment variables were identified. However, we showed that these strong taxa-environment associations may not always be reflected in the geographical distribution of these taxa. IMPORTANCE The significance of this study is threefold. It investigated the influence of spatial scale on the soil bacterial community composition across a typical Arctic landscape and demonstrated that conclusions reached when examining the influence of specific environmental variables on bacterial community composition are dependent upon the spatial scales over which they are investigated. This study identified a dispersal limitation (spatial autocorrelation) distance of approximately 60 m, required to obtain samples with fully independent bacterial communities, and therefore, should serve to inform future sampling strategies in the region and potentially elsewhere. The work also showed that strong taxa-environment statistical associations may not be reflected in the observed landscape distribution of the indicator taxa.

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Functional spatial scale of community composition change in response to windthrow disturbance in a deciduous temperate forest

Ecological Research ◽

10.1007/s11284-007-0372-1 ◽

2007 ◽

Vol 23 (2) ◽

pp. 249-258 ◽

Cited By ~ 8

Author(s):

Toshihide Hirao ◽

Masashi Murakami ◽

Hiroyuki Oguma

Keyword(s):

Spatial Scale ◽

Community Composition ◽

Temperate Forest ◽

Composition Change

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Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

10.1101/143669 ◽

2017 ◽

Author(s):

Luke Owen Frishkoff ◽

D. Luke Mahler ◽

Marie-Josée Fortin

Keyword(s):

Spatial Scale ◽

Community Composition ◽

Spatial Scales ◽

Error Rates ◽

Environmental Drivers ◽

Type I ◽

List Type ◽

Occupancy Models ◽

Species Detection ◽

Scale Of Response

AbstractSpecies abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet determining the spatial scale at which landscapes affect species remains a persistent challenge that hinders ecologists’ abilities to understand how environmental gradients influence species presence and shape entire communities, especially in the face of data deficient species and imperfect species detection.Here we present a Bayesian framework that allows uncertainty surrounding the ‘true’ spatial scale of species’ responses (i.e., changes in presence/absence) to be integrated directly into a community hierarchical model.This scale selecting multi-species occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct type I error rates across a broad range of simulation conditions. In contrast, ensembles of single species GLMs frequently fail to detect the correct spatial scale of response, and are often falsely confident in favoring the incorrect spatial scale, especially as species’ detection probabilities deviate from perfect.Integrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities.

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