Habitat configuration matters when evaluating habitat-area effects on host–parasitoid interactions

Citation: With, K. A., & Pavuk, D. M. (2019). Habitat configuration matters when evaluating habitat-area effects on host–parasitoid interactions. Ecosphere, 10(2), e02604. https://doi.org/10.1002/ecs2.2604Higher trophic levels tend to be more sensitive to habitat fragmentation than lower trophic levels, which is why parasitism rates should decline in fragmented landscapes. Habitat loss and fragmentation (the subdivision of habitat) are typically interrelated processes, and thus, their effects are confounded in most studies. To address this, we quantified parasitism rates in pea aphids (Acyrthosiphon pisum) within an experimental model landscape system, in which we independently controlled the amount vs. the fragmentation of habitat (red clover, Trifolium pratense) within individual landscape plots (16 × 16 m). Aphid densities were generally unaffected by landscape pattern, except at the local scale for interior habitat cells within fragmented landscapes, which had significantly lower aphid densities than all other cell types. Aphid parasitism rates averaged about 40% and were significantly—albeit weakly—correlated with aphid density. Habitat amount had the greatest overall effect on parasitism rates, but fragmentation effects were evident in a shift in parasitism at intermediate habitat levels: Parasitism rates were higher in fragmented landscapes with 50% habitat. Edge effects alone did not explain this shift in parasitism rates. Parasitism rates were uniformly high within edge habitat and fragmented landscapes, and thus, the shift in parasitism at intermediate habitat levels was driven by increasing parasitism rates within interior cells and clumped landscapes at higher habitat amounts. Habitat configuration is thus important for evaluating habitat-area effects on species interactions, as habitat amount only affected parasitism rates within less-fragmented landscapes in this system

Using Fractal Analysis To Assess How Species Perceive Landscape Structure

To develop a species-centereddefinition of `landscapes,' I suggest using a fractal analysis of movement pat- terns to identify the scales at which organisms are interacting with the patch structure of the landscape. Sig- nificant differences in the fractal dimensions of movement patterns of two species indicate that the species may be interacting with the patch structure at different scales. Fractal analysis therefore permits comparisons of `landscape perceptions' of different species within the same environment

Nordic Society Oikos Landscape Connectivity and Population Distributions in Heterogeneous Environments

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The Structure of a Forest Bird Community during Winter and Summer

Volume: 98Start Page: 214End Page: 23

Example landscape patterns.

<p>We independently varied four aspects of host pattern between two extreme values in order to mimic different pest and disease management strategies: abundance of host areas, <i>h</i> (−), from (<b>A</b>) a low proportion in the landscape (<i>h</i> = 0.005) to (<b>B</b>) a high proportion (<i>h</i> = 0.5); quality of host areas, as determined by the pest or pathogen population density per square meter of host area, <i>ρ</i> (no. m⁻²), from (<b>C</b>) a low density (<i>ρ</i>/10), pictured as blue, to (<b>D</b>) high density (<i>ρ</i>×10), pictured as red; matrix resistance, i.e., the ability of organisms to move through the intervening matrix of non-host area, where dispersal distances between host areas were multiplied by a factor <i>R</i> (−), from (<b>E</b>) <i>R</i> = 0.1 to represent a highly permeable, low-resistance matrix, pictured as blue, to (<b>F</b>) <i>R</i> = 10 for a highly resistant matrix, pictured as red; and aggregation of host areas, <i>H</i> (Hurst exponent; −), from (<b>G</b>) a random distribution (<i>H</i> = −0.5) to (<b>H</b>) a highly aggregated distribution (<i>H</i> = 1). In each scenario, the remaining landscape parameters were fixed at baseline values of: <i>h</i> = 0.05, <i>ρ</i> as given for each species, <i>R</i> = 1, and <i>H</i> = −0.5.</p

Interaction of grain size, host heterogeneity, and infestation.

<p>Magnitude of infestation, (no.), in landscapes where the spatial grain size (cell dimensions, m²) is systematically increased, under management scenarios that vary (across columns): host abundance, <i>h</i> (−); host quality, <i>ρ</i> (no. m⁻²); matrix resistance to movement, <i>R</i> (−); and aggregation of host areas, <i>H</i> (−). (<b>A</b>)–(<b>D</b>) Forest pest, the mountain pine beetle (<i>Dendroctonus ponderosae</i>); (<b>E</b>)–(<b>H</b>) plant pathogenic bacterium (<i>Pseudomonas syringae</i>); (<b>I</b>)–(<b>L</b>) mosquito disease vector (<i>Culex erraticus</i>); (<b>M</b>)–(<b>P</b>) spatiotemporal spread of the oomycete plant pathogen (<i>Phytophthora infestans</i>), the causal agent of potato late blight disease. Shaded regions indicate ±1 SE. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075892#pone-0075892-g002" target="_blank">Fig. 2</a> provides examples of dispersal patterns, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075892#pone-0075892-g003" target="_blank">Fig. 3</a> provides examples of landscape patterns.</p

Example output from the various classes of dispersal model used in complex landscapes.

<p>(<b>A</b>) A symmetric, two–dimensional probability density function (dispersal kernel). Dispersal probability ranges from red (highest) to blue (lowest). The Gaussian kernel shown here is used for dispersal of a forest insect pest, <i>Dendroctonus ponderosae</i>. (<b>B</b>) Splash dispersal of a plant pathogenic bacteria, <i>Pseudomonas syringae</i>, using random draws from a negative exponential distribution for droplet distance and random draws from a uniform distribution for droplet angle. (<b>C</b>) A Lévy flight model for simulating the individual flight paths of a mosquito disease vector, <i>Culex erraticus</i>. Lévy flights are a special class of random walk that is punctuated by occasional long steps, and here we show the path of a mosquito flight beginning at the green marker and ending at the red marker. (<b>D</b>) A Gaussian plume model from the meteorological sciences used to simulate the dispersion of <i>Phytophthora infestans</i> (an oomycete plant pathogen) sporangia by wind and turbulence. Dispersal probability ranges from red (highest) to blue (lowest). The source of inoculum is situated in the center of the y–axis and the wind direction is 225 degrees.</p