Download Landscape effects on spider species richness and

Landscape effects on species richness and spillover of spiders in a coffee agroecosystem
Key Words: shade coffee, biodiversity, spiders, spatial autoregression, spillover
Background: Recent advances in our understanding of metapopulation dynamics have reframed
the debate over how to conserve biodiversity in the tropics1. The view that large swaths of land
should be purchased and then ‘protected’ from human use is giving way to a new paradigm
centered on improving the quality of managed landscapes. Low-input farming systems, such as
diverse, high-shade coffee polycultures (HSC), contribute to landscape quality by providing high
levels of biodiversity (both planned and associated) and structural complexity that improve interpatch connectivity within agricultural matrices2. Maintaining high levels of diversity in these
systems can improve delivery of ecosystem services such as pollination, pest suppression, and
nutrient cycling3,4 providing direct benefit to both farmers and conservationists. Because local
functional diversity is dependent upon species pools throughout the landscape, sustaining
ecosystem services for individual farms requires a landscape approach4.
High quality fragments within the matrix, such as forest remnants (FR) and HSC, support
diverse communities of functionally important invertebrate species. Research that colleagues and
I conducted in Costa Rica has shown that FRs within the matrix contain higher levels of natural
enemy (spider) diversity than the surrounding landscape5; in systems containing coffee, spider
community composition is similar between HSC and FR6. However, little is known about the
relative importance of HSC management in the context of the landscape. Two important
complementary research areas remain largely unexplored for invertebrate natural enemies in the
matrix: (a) the effects of landscape composition and arrangement on species richness; and (b) the
extent of species spillover from high-quality habitat into more intensively managed land. This
information is critical to determining recommendations for farmers, conservationists, and
landscape planners on designing multifunctional landscapes7.
The most functionally important natural enemies are likely generalist predators with high
dispersal abilities3. In this respect, spiders represent an excellent group of predators for study;
while almost all spiders are polyphageous predators, diverse hunting strategies among different
species result in multiple functional groups that capture specific agricultural pests8. The high
dispersal abilities of some spiders, such as ballooning spiders, should allow for the colonization
of intensively managed farms through inter-patch migration at relatively large scales. Despite
the potential for spiders to provide natural pest regulation, very little research on spider ecology
has been conducted at the landscape scale in tropical agroecosystems.
Hypotheses: (1) spider species richness increases with the proportion of HSC and FR within the
landscape; and (2) HSC and FR provide sources for spillover of spiders into more intensively
managed, technified coffee farms.
Methodology: The Soconusco region of Chiapas, Mexico, is composed of a mosaic of FR and
coffee farms (250 ha. – 350 ha. in size) employing a wide range of management techniques (HS,
shade monoculture, sun, etc). Field/Local: (1) 3m x 3m plots will be established within each
land-cover type at a density of 1 plot/15 ha. (17 – 23 plots/farm) at locations generated randomly
in a GIS. Samples will be collected bi-weekly for 4 months during wet and dry season for three
years. Species accumulation curves in a nearby region were saturated at 18 transects6 suggesting
that the plot density and collection frequency proposed should detect most of the species present.
Lidded funnel traps filled with ethylene glycol will be placed in the center of each plot and
collected after a 24-hour period. Manual collection of web-building spiders will be conducted
using web-misting within the plot just prior to collecting the funnel trap. Average leaf litter
depth, proportion of leaf litter cover, canopy openness, and plant species richness will be
recorded. (2) The same methods will be used as in (1), except that plot locations will form a
500m x 500m grid, with plot centroids spaced 100m apart (25 plots total). The grid extent will
encompass HSC, FR, and sun-coffee. Landscape: Land-cover classification has already been
conducted using IKONOS imagery9 (1) A 1500 m circular buffer will be created around each
plot (maximum scale of strongest correlation between spider abundance and landscape factors10),
and used as the neighborhood for landscape metric calculations (proportion and perimeter-area
ratio [P/A] of HSC and FR) from the land-cover data. Analysis: (1) Three Ordinary Least
Squares (OLS) regression models (local-only, landscape-only, and combined local-landscape)
will be fit using species richness as the response variable, and habitat characteristics (local,
landscape, or both) as dependent variables. Stepwise selection will be used to remove dependent
variables to increase model parsimony. (2) OLS models will be fit using square-root
transformed spider family abundance as the response variable, with the same dependent variables
as (1). Residuals from OLS models, which I expect to be spatially autocorrelated given the scale
of analysis, will be used to define neighborhood distances for spatially-lagged autoregressive
models11. Such models can be used when the response at a given location is thought to be
directly related to responses at neighboring locations (i.e., spillover).
Expected results: Because spider response to landscape effects is likely dependent upon a
combination of dispersal ability and habitat complexity, I expect to find that spider species
richness will be greatest within landscapes composed of a high proportion of structurally
complex habitat (e.g., HSC and FR). Additionally, landscapes with high P/A ratios of HSC and
FR should facilitate exchange between source habitats and high-intensity systems because more
high-quality edge habitat exists from which spiders can disperse. Some families of spiders are
known to prefer semi-degraded cultivated habitats and to respond more strongly to local
management (e.g., Lycosids). However, ballooning and web-building spiders should be more
responsive to landscape heterogeneity and therefore are more likely to colonize degraded habitat
from nearby sources during periods of increased resources (e.g., prey availability). Overall, the
contribution of spiders from the surrounding landscape should augment richness from species
reacting to local factors, particularly since the two groups may exploit different resources.
Further, I expect that HSC and FR edges facilitate spillover of diverse spider functional groups
into sun-coffee. The extent of spillover should vary by spider family because of differences in
dispersal ability and habitat use. Given the homogenous composition of sun-coffee agroforestry
systems, spillover extent should not be confounded by the spatial variation of habitat variables.
Broader Impacts: Improving matrix quality requires collaborative relationships between
farmers and scientists. By nature, such collaborations broaden the participation of
underrepresented groups from geographically underrepresented areas in applied science. Results
of my study will be the first to explore how HSC and FR contribute to spider species richness at
the landscape-scale in the matrix. The information will help guide efforts to preserve
biodiversity in the tropics while improving farmer livelihoods through natural pest regulation.
References: 1Perfecto, I and J. Vandermeer. 2008. Ann. NY. Acad. Sciences. 1134: 173-200.
2
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Ecol. Lett. 8(8): 857-874. 4Williams-Guillén, K. et al. 2008. Science. 320(5872): 70. 5Banks, J.E.
Sandvik, P., and L. Keesecker. 2007. Pan. Pac. Ent. 83(2):152-160. 6Pinkus-Rendón, M.A. et al.
2006. Div. & Dist. 12(1): 61 – 69. 7Tscharntke, T. et al. 2007. Bio. Control. 43:294-307. 8Marc,
P. and A. Canard. 1997. Agr., Eco. & Env. 62(2-3):229-235. 9Philpott, S. et al. 2008. Agr., Eco.
& Env. 128(1-2):12-20. 10Schmidt, M.H. et al. 2008. J. Biogeography. 35:157-166. 11Lichstein,
J.W. et al. 2002. Eco. Monographs. 72(3):445-463.