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Discovery Day Mertes
- 1. Species-environment relationships of East African birds along a body size gradient K. Mertes Schwartz* and W. Jetz Department of Ecology and Evolutionary Biology Yale University
- 2. Spatial scale Spatial scale or grain: breaking up our knowledge of the environment into units of a known, equal size
- 3. Spatial scale is important to mobile animal species Home <10 km2 Acreage Security # bedrooms Size Security Nearby resources Home range <100 km2 Mix of required resources Mix of required resources Region <1000km2 Food Critical resources Family nearby Dispersal Mate-searching Seasonal resources
- 4. …but which environmental conditions are important at which scales?
- 5. Study species Von der Decken’s hornbill • 160-250g • omnivorous (may consume more fruit) • Forages on ground & in trees Fork-tailed drongo • 40-50g • Pursues insects from open perches • Highly vigilant Kori bustard • 8-18kg • Consumes insects, small animals (lizards, mammals), seeds, leaf material
- 6. Environmental data at different spatial scales
- 7. Predicted occurrence: Von der Decken’s hornbill 10m 100m 1000m
- 9. 10m Predicted occurrence: Kori bustard 1000m100m 1 4km2
- 10. Summary Large scales - Drongos: variable shrub density: minimal distance to roads - Von der Decken’s hornbills: restricted land cover classes - Kori bustards: restricted elevations and black cotton soils Small scales - Drongos selected areas with low shrub density & variable elevation and distance to roads - Von der Decken’s hornbills selected areas with complex vegetation and dense shrubs - Kori bustards selected areas with variable shrub density and soil types Species responses to environmental conditions were consistent with diet and body size Take-home messages: Habitat managers make decisions at several spatial scales: - land use planning issues at broad spatial scales - planning for stands, plots, and land cover types within properties Information on environmental patterns and species responses at multiple scales can be incorporated into land use planning to support the habitat requirements of multiple wildlife species.
- 11. With many thanks… Yale University Os Schmitz, Karen Seto, David Vasseur, Anne Trainor, Larry Bonneau, Giuseppe Amatulli, Adam Wilson, Frank LaSorte, Jennie Miller, Kevin McLean Max-Planck-Institut für Ornithologie, Radzolfell Martin Wikelski, Bruno Erne, Georg Heine Mpala Research Centre Margaret Kinnaird, Korir Elkana, Hellen Koech, Morgan Pecora-Saipe, Stephanie Siller Movebank Team
- 12. Questions?
- 14. Potential T. deckeni characteristic grains
- 16. Roads Water Clouds & shadow Bare soils Grass Shrubs Human use Rock 0162 0127 0170 119 347 345 0150 186
- 17. Study area: ecological zones Black cotton, tall grass & shrubs Black cotton, grass cover & trees Red soil, few shrubs, high grass cover Red soil, freq shrubs, high grass cover
Editor's Notes
- Thank you all for your continued attention! Today I’m going to share some results from ongoing research that explores how species responses to environmental conditions change with spatial scale, focusing on three East African bird species that vary in body size
- First, what do we mean by spatial scale? Usually, we’re talking about breaking up our knowledge of the environment – here, a map of the land cover or vegetation types across Mpala – into equal units of some known size, like 1m, 100m, 1km, etc.
- Next, let’s think about what spatial scale means to animals… Most species do not perceive the environment the same at all spatial grains Here’s an example using humans and birds: as humans, we select a region in which to live based on a set of features, such as proximity to family, economic opportunity, political stability, etc. Of all of the available towns or neighborhoods within this region, we spend most of our time within some easily traveled distance that contains critical resources like schools and places to obtain supplies…in animals, we call this a “home range” or “territory” Of all of the available houses within this region and home range, we then select a home based on characteristics like building size, land acreage, security, and possibly other amenities like a good view At each of these three different spatial scales, we are making decisions about where to spend our time based on different resources…and the same is true of animals, just with different environmental factors Each individual also occupies a home range selected from available areas within its regional context, again based on a distinct set of criteria that relate to certain environmental conditions. This processs is repeated at the regional context, given some dispersal limitations. So here we have 3 nested spatial grains, at each of which organisms are responding to distinct sets of environmental conditions, that together give rise to an observed distribution pattern. if inappropriate scales of analysis are used, key factors involved in habitat selection may not be detected. If we had examined settling patterns of female yellowheads only on the scale of individual territories, or even of several contiguous territories, we would not have detected the important role of food supply or the scale at which it operated Habitat managers must make decisions at a variety of scales: for example, when managing for forestry operations at the broadest scale, managers address issues of land use planning, such as designating parks and protected areas; at the intermediate scale, they manage for issues related to watershed or landscape planning, such as human access management and forest age class distribution; and at the finest scale, they address issues of stand-level management, such as clearcut size or tree species selection. Therefore, to provide for the habitat requirements of wildlife species, managers must understand the pattern and consequences across different scales. Animals themselves are not responding to different scales but rather as managers and/or ecologists we may observe biological processes differently when we conduct analyses at multiple scales (Hobbs 2003). For example, a habitat attribute that is highly selected at a fine scale might be of little value if it is not located within a landscape containing other life requisites. Furthermore, a habitat attribute that is beneficial at one scale may be detrimental at another scale. Thus, the scale at which we observe animal responses to the landscape may influence our management decisions (Hobbs 2003). Accordingly, habitat selection studies should be scale dependent (Addicott et al. 1987, Boyce 2006).
- Our understanding of habitat selection by mobile animal species is affected by the scale of observation management questions may be best addressed at specific scales of analysis Identifying the factors that influence movement at different spatial grains is critical for understanding species-environment relationships and accurately predicting the spatial distribution of biodiversity
- We collected two types of data for our study species: First, we placed miniaturized GPS tags constructed by the University of Konstanz’s Technical Workshop and the Max-Planck-Institut for Ornithologie in Radzolfell on adult males of each species And we’ve been also conducting timed surveys at specified locations across the study area at roughly monthly intervals since 2012; any individual encountered during these surveys is followed for 5-15 locations we record the position of the observed indiivuals using a handheld GPS unit, a laser rangefinder, and compass bearing, thus generating a very short-term movement path at the level of individual movement decisions The data collected using these methods are affected by multiple sources of spatial error, and typically have a total circular error region less than 10m in diameter; which is why the finest grain at which we constructed environmental variables was 10m
- We compiled data on environmental conditions thought to be biologically relevant for these species in this landscape from a number of sources I’m just going to highlight a few variables that are somewhat unique, particularly… the structural complexity index (SCI), a composite variable used more frequently in forest ecology, that was generated by summarizing vegetation structural measures taken in the field and modeling these across the study area using NDVI texture measures And the precipitation variable, which was generated from long-term measurements at local weather stations through kriging with topographical covariates All land cover and vegetation variables were derived from a high resolution Quckbird image, which we resampled to a finest grain of 10m based on some spatial error concerns that I’ll address in a minute We selected five additional grains of analysis that represent commonly available remote sensing data, i.e. 30m for Landsat up to 250-500m for MODIS, and 1km for AVHRR, and other global environmental data sets
- We used glmulti , an R package by Calcagno and Mazancourt (2010) , for automated multi-model selection to find the best subset of candidate environmental models supported by the data; this package is based on Akaike’s information criterion (AIC) ( Diniz-Filho et al. 2008 ). We applied the package’s default method (exhaustive screening of all candidate models) to find the best explanatory models from all possible unique models involving our list of environmental variables. Out of the total possible models obtained, we chose a subset of models where each predictor (or term) had a correlation of less than 0.8 from each other to reduce multicollinearity. From this subset, those models that differed by less than two AIC units from the model with the minimum AIC value were selected as the best subset of candidate models. The most parsimonious m Independently of the pattern of autocorrelation in the original (predictors and response) variables, if no spatial autocorrelation is found in the residuals it can be concluded that the model had taken into account all spatial structure in the original data and that there was no statistical bias in the overall statistical analysis (Diniz-Filho et al. 2003).del (the one with the minimum number of predictors) was selected from that subset of best candidate models as the “final” model for biological interpretation.