Lo empirico sobre comparar nichos

1. Niche Comparisons

4. ?
?

5. Requirements
• Decision regarding which space (G or E) will be
the arena for testing
• Careful elaboration of what is the null
hypothesis and what are critical values for
rejecting it
• Measure observed similarity
• Assess similarity expected at random
• Compare observed versus expected
similarities
• Make conclusions about the null hypothesis

6. Which Space?
• Geographic (e.g., Warren et al. 2008)
– Original niche comparisons were developed here
– Topology and such are easy to visualize and quite
simple to comprehend
– Representation of the niche and its full dimensions is
less clear
• Environmental (e.g., Broennimann et al. 2012)
– Corresponds more closely to the niche
– Topology, density, etc., are less well understood and
less easily visualized
– Correspondence to geographic space is not always
easy to understand

7. Null Hypothesis
• Null hypothesis will generally be “these two
niches are not more different than would be
expected by chance”
• As such, critical value will be the lower 5% of
similarity in the null distribution
SimilarityLow High
Frequency
5% 95%
REJECT   ACCEPT

8. Similarity Metric
• D, I, or any other metric of matching or
similarity between two 2-dimensional areas (G
space) or multidimensional forms (E space)
• Simply must respond to the question of how
similar or different are the two entities in the
chosen space

9. Warren et al. “Background” Similarity
• Cast random points within M of one species
• Build model based on the random “background”
points
• Measure similarity to the model of the other
species based on occurrence points
• Repeat many times to develop a distribution
summarizing “background similarity”
• Repeat entire process but switching the two
species
• Implemented within ENMTools and in R
• Disadvantage: requires models to be transferred
to the same area in order for comparisons to be
developed (consequence of G space choice)

10. Identity
Test
Warren et al.
(2008)

11. Background
Similarity
Test
Warren et al.
(2008)

12. Escobar-Qiao Squirrel Test
• Driven by desire to avoid model transfers
• Identify geographic areas of overlap in
environments between the two M areas
• These areas represent environments common to
the two accessible areas
• Any differences in use of environments within
those areas can be interpreted directly as niche
difference
– Can use between-group inertia
– Or could use randomization tests like Warren et al.
2008 test for identity

14. Figure 3. Top: visualization of similar overlapping
environments in native (blue) and invasive (red) areas.
Note the many unshared environments across the
native range (gray points). Axes are the first three
principal components derived from the original climate
layers. Bottom: geographic areas with similar
environments in the native (blue) and invaded (red)
ranges. Note the many unshared environments across
the native range (gray areas).

15. Figure 4. Gray Squirrel (Sciurus carolinensis) occurrences (yellow points) in invaded
(red) areas (A) and display of occurrences in corresponding environmental space
(B). Occurrences (green points) in overlapping-environment (red) between native
and invaded ranges (C) were compared in the environmental space (D). Notice the
occurrences from native areas (green ellipsoid) nested within the ellipsoid
corresponding to occurrences from the invaded areas (yellow ellipsoid) in the
environmental space. Background values are show as gray.

16. Broennimann et al. E-space Similarity
• Echoes Warren et al. in terms of testing identity
versus testing similarity of pairs of niches
• Moves the question and the analyses to
environmental space
• Uses kernel densities to take into account the
weird and uneven distribution of environmental
spaces
• Uses randomization tests to assess identity and
similarity

17. Europe North America
Identity
test
Similarity
tests

18. Fire
Ants

19. Conclusions
• Several options are available to researchers for
testing niche similarity and niche identity
• Niche-oriented questions MUST consider M in
some sense or in some way
• Crucial decisions: G versus E space, model
transfer, etc.