Transcript BDC321_L06 - Heterogeneity

BCB 322:
Landscape Ecology
Lecture 6:
Emerging patterns I:
Heterogeneity
Heterogeneity
• Patterns arise in any landscape as a result of the
underlying processes
• Disturbance and fragmentation are closely allied,
and have significant impacts on the environment
• Heterogeneity is the main pattern in any landscape,
& is inherent at all scales
• “The uneven, non-random
non-random distribution of objects”
(Forman, 1995)
• Since every pattern in a landscape results from, and
produces new processes, heterogeneity is important
for landscape function
• The analysis of heterogeneity is fundamental to the
understanding of landscape functions and spatial
ecological processes
Heterogeneity
• Three types to be
considered:
– Spatial: variation in space,
either horizontally (as under
human disturbance regimes)
or vertical (uneven vegetation
distribution above ground –
generally natural)
– Temporal: similar to spatial,
http://www.class.uidaho.edu/italy2004/ecology_files/patchSteppingStones.jpg
but implies variation for a
single point over time. Two areas may have the same spatial
variation, but differ in time.
– Functional: variation in distribution of communities/
populations. Linked to life history of organisms
• Soil composition is a strong driver of heterogeneity, & it
varies strongly from individual plots (1-10m) to the full
landscape (Becher, 1995). It can also vary vertically in
the soil profile
• The effect of geological heterogeneity is unpredictable,
and a probabilistic approach is used when modelling it
Heterogeneity
• By creating borders and edge effects, additional
processes are set up in the landscape, influencing the
flux of materials
• Since plant and animal species respond fairly rapidly to
changes in mosaic heterogeneity, minor variations can
often be observed with remote sensing techniques.
• Effectively, heterogeneity can exaggerate biologicalenvironment interactions (eg: skylarks avoid small
prairie spaces, even though they are functionally similar
to larger patches) (Farina, 1998)
• Heterogeneous landscapes can show several different
stable configurations, and can shift between them
rapidly given sufficient incitement.
• This is termed a polyclimactic state, and it is determined
by variations in deterministic & stochastic factors (wind,
climate extremes, climate change, edaphic conditions)
and internal factors (disease, predation, invasion)
Disturbance
• Hence disturbance can play a
role in maintaining
heterogeneity and preventing
stable equilibria developing.
• A moderate disturbance
regime can increase
heterogeneity, but it depends
on the initial conditions
• Homogeneous system:
heterogeneity increases to the
midpoint, then falls
• Highly heterogeneous: after
an initial gain in heterogeneity,
there is a rapid dropoff
(Kolasa & Rollo, 1991)
Disturbance
• Obviously, some disturbance can be useful for
maintaining heterogeneity.
• Likewise, heterogeneity can impact on the
disturbance regime:
– fires spread less readily in a mixed woodland than in a
pure coniferous matrix (reduced disturbance)
– predators (eg: foxes) in woodlots & parklands in an
agricultural environment can impact on livestock
(increased disturbance)
• Species distribution depend
on community heterogeneity:
large sample areas include
more species, and thus
should be more similar than
smaller ones with a limited
http://pelotes.jea.com/AnimalFact/Mammal/Fox01.jpg
selection of species.
Structure
• Three main types of
heterogeneity structure (Addicot et
al., 1987):
– Divided homogeneous (suitable
patches in an unsuitable matrix)
– Undivided heterogeneous (patches of
varying suitability)
– Divided heterogeneous (varying
suitability patches in an unsuitable
matrix
Scale
• Fire, grazing & the two in combination were tested in
grasslands (Glenn et al, 1992)
• Locally, burning seemed to have higher heterogeneity
than grazing, whilst the corollary was true at a regional
scale.
• Overall, untreated local plots had the most
heterogeneity, but regional responses varied to a large
degree, depending on season of burning (spring burning
then grazing increased heterogeneity, autumn burns
reduced it)
• This variation makes studies complicated – some
processes depend on patchiness, but not all.
• General, organismal responses are a good measure by
which to assess relevant scale. (neighbourhood scale)
• Neighbourhood for vagile species is obviously their
territory or resource area
• For sessile species it is more complex, but it can be
estimated according to the areas from which food,
predators and other foragers come.
•
Animals
Spatial heterogeneity is one of
the main factors determining bird
species diversity in the landscape
(MacArthur et al., 1962)
• Bird diversity was also higher in
shrubby areas because shrubs
have higher heterogeneity than
tree areas (even though trees
have more variation in canopy
structure)
• Animals also drive heterogeneity: http://www.rangemagazine.com/features/spring05/pics/1HWANGE1badland.jpg
grazing animals at high densities can permanently alter vegetation
cover by compacting soil and removing vegetation (eg: rural goat
grazing in Zimbabwe)
• Animals can even alter geomorphology, & all sizes can impact
heterogeneity:
– Insects (ants & termites shift soil, dung beetles enrich soil)
– Small mammals (moles & rabbits digging soil, other rodents distributing
seed)
– Medium mammals (beaver dams, pigs rooting)
– Large mammals (elephants/buffalo opening up cover)
– All sizes of birds affect seed distribution & enrich soil with droppings
Foraging
• Species-level heterogeneity requires individuals to modify their
foraging habits – in larger patches they can afford to spend less
time foraging
• Time spent within a patch varies as the square of the linear
dimension of the patch, whilst travel time between them varies
linearly (ie): large patches are preferable and used in a more
specialised way than small patches. (MacArthur & Pianka, 1966)
• For large species, it has been found that they respond to
landscape-level heterogeneity, but not locally (bison, Wallace et al., 1995)
• Thus, they move in a determined manner (non-randomly) between
patches, but within patches move randomly, minimising energy use
(optimal foraging)
• Similar observations of sheep grazing showed that they generally
moved directly to the nearest plant, whilst occasionally moving
between plants
• It is possible that heterogeneity therefore plays a role in determining
optimal foraging strategies for species, and may even enhance the
efficiency
Animal movements
• Certainly, movement within a
matrix is determined by
suitable/unsuitable patches,
preventing animals from
moving in straight lines
(Johnson et al., 1992)
• It may also play a role in the
recollection of routes in a
home range (homing ability)
• Bumble bees’ foraging routes are generally longer in
uniform stands, whilst in a varied stand they tend to
backtrack more often
• Certainly digger wasps (small range) use landmarks to
find their way about, and are prepared to fly further in
a varied landscape
Animal movements
• Bees exhibit a similar
behaviour
• Homing ability drops off fairly
quickly in flat or uniform
landscapes (Plowright & Galen, 1985)
• In comparison, in
mountainous areas they can
return from as far off as 9km,
due to increased landmarks
Metrics of heterogeneity
• We can measure the heterogeneity of a landscape in
terms of any resource (soil structure, plant diversity,
biomass, thicket structure, animal distributions…)
• The variation in structure hence reflects changing
functions and processes in the landscape
• In order to assess this, we use several different
metrics, each of which considers different aspects of
the structure.
– Fractal dimension (measures the complexity of edges)
– Contagion (the extent of aggregation of patches)
– Evenness (measures number of different patch types & their
proportions in the landscape)
– Patchiness (contrasts neighbouring patches in a matrix)
• Li & Reynolds (1994) measured these different
metrics against four components of heterogeneity
(Li & Reynolds, 1994)
Summary
• Heterogeneity is the principal characteristic of any
landscape
• It varies due to underlying processes, and affects these
processes in turn, by initiating or amplifying biological
interactions
• Can be considered in terms of temporal, spatial or
functional components
• Affects disturbance, although in both a positive & a
negative manner
• Heterogeneity affects animal processes (eg, grazing
efficiency of ungulates) and is likewise affected by the
interactions of animals with landscape functional
components
• Measured using different indices, including contagion,
fractal dimension, evenness & patchiness
References
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Addicot, J.F., Aho, J.M. & Antolin, M.F. (1987) Ecological neighbourhoods: scaling
environmental patterns. Oikos 49: 340-346
Becher, H.H. (1995) On the importance of soil homogeneity when evaluating field
trials. Journal of Agronomy & Crop Science 74: 33-40
Farina, A. (1998) Principles and Methods in Landscape Ecology. Chapman and Hall,
London, UK
Forman, R.T.T. (1995) Land Mosaics. The ecology of landscapes and regions.
Cambridge University Press, Cambridge.
Glenn, S.M., Collins, S.L. & Gibson, D.J. (1992) Disturbance in tallgrass prairies:
local and regional effects on community heterogeneity. Landscape Ecology. 7: 243251.
Johnson, A.R., Wiens, J.A., Milne, B.T. & Crist, T.O. (1992)Animal movements and
population dynamics in heterogeneous landscapes. Landscape Ecology 7: 47-58
Kolasa, J. & Rollo, C.D. (1991) Introduction: the heterogeneity of heterogeneity: a
glossary. In: Kolasa, J. and Pickett, S.T.A. Ecological heterogeneity. Springer-Verlag,
New York, pp 1-23
Li, H. & Reynolds, J.F. (1994) A simualtion experiment to quantify spatial
heterogeneity in categorical maps. Ecology 75:2446-2455.
MacArthur, R.H., MacArthur, J.W. & Preer, J. (1962) On bird species diversity. II.
Prediction of bird census from habitat measurements, American Naturalist 96: 167174
MacArthur, R.H. & Pianka, E.R. (1966) On optimal use of patchy environment.
American Naturalist 100: 603-610
Plowright, R.C. & Galen, C/ (1985) Landmarks or obstacles: the effect of spatial
heterogeneity on bumblebee foraging behaviour. Oikos 44: 459-464