1. Population Ecology: Distribution & Abundance
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K. Harms photos from north of Manaus, Brazil

2. Population A group of individuals of a species that occupy
a given place at a given time
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Photo of members of a tadpole population from

3. Distribution Local distribution – generally patchy, not continuous
(which reflects patchy character of habitat, dispersal history, etc.)
Geographic distribution – the entire geographic range
Canyon wren (red)
Cerulean warbler (blue)
“X’s denote [Breeding Bird Surveys] on which the focal species were never detected over this period [ ], while filled circles indicate where the focal species
were detected.”
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Hurlbert, Allen H. & Ethan P. White Disparity between range map- and survey-based analyses of species richness: patterns, processes and implications. Ecology Letters 8:
“Figure 1 (a) Geographical ranges of the canyon wren (red) and cerulean warbler (blue) from Ridgely et al. (2003). Symbols represent Breeding Bird Survey (BBS) routes that have been surveyed every year from 1993 to X’s denote surveys on which the focal species were never detected over this period, while filled circles indicate where the focal species were detected. (b) Geographical pattern of avian richness based on the number of species ranges overlapping each point. (c) Geographical pattern of avian richness based on the number of species observed on each BBS route in (d) Geographical pattern of the ratio of survey to range map richness, showing the greatest concordance in the south-east and the greatest disparity in the west. Richness and concordance values in (b)–(d) are binned into quantiles. (e) Digital elevation model of North America at 1-km resolution. (f) Mean values for the normalized difference vegetation index in the month of June at 1-km resolution (see text).”
Hurlbert & White (2005) Ecology Letters, Fig. 1

4. Abundance Population size – the number of indivs. in the pop.
Population density – no. indivs. per unit area
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Photo from

5. Human population density – 1994
Abundance
Human population density – 1994
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Wikipedia “Population” page; 09/IX/2014
Image from Wikimedia Commons

6. Abundance & Geographic Range
Puma (previously Felis) concolor
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Wikipedia “Cougar” page; 07/IX/2014
Photo & geographic range map from Wikimedia Commons

7. Abundance & Geographic Range
Dionaea muscipula
Endemic to Carolinas; native range is within 60-mile radius of Wilmington, N. C.
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Wikipedia “Venus flytrap” page; 07/IX/2014
Video & geographic range map from Wikimedia Commons

8. Habitat (niche) breadth
Relative Abundance
Most species are rare and geographically restricted
Deborah Rabinowitz identified 7 forms of rarity
“Species in the upper left cube at the front exhibit no component of rarity. Those at the lower back right have all three components of rarity: small geographic range, narrow habitat breadth and low local density”
Habitat (niche) breadth
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See also Fig in your textbook.
Where would pumas and Venus flytraps fall on this figure?
Rabinowitz, Deborah Seven forms of rarity. Pp in H. Synge, ed. The Biological Aspects of Rare Plant Conservation, Wiley.
Ricklefs, Robert E Rarity and diversity in Amazonian forest trees. Trends in Ecology & Evolution 15:83-84.
“Fig. 1. Rabinowitz’s scheme illustrating the seven forms of rarity. Species in the upper left cube at the front exhibit no component of rarity. Those at the lower back right have all three components of rarity: small geographic range, narrow habitat breadth and low local density.”
Local
abundance
(somewhere common or everywhere rare – within the native geographic range)
Geographic
range
Image from Ricklefs (2000) TREE, based on original concept in Rabinowitz (1981)

9. What is an Invidividual?
Genets – single genetic indiv.; best focus for evolutionary questions
Ramets – actually or potentially independent members of a genet; clones;
best focus for how (semi-)independent physiological units compete
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Wikipedia “Stolon” page; 07/IX/2014
See pg. 26 of Harper (1977) – a genet comprises the various parts that trace back to a single “original zygote” (i.e., no intervening zygotes, e.g., through descent).
Photo of the many ramets of a single genet of a dune plant from Wikimedia Commons

10. Neither distributions nor abundances are static
Dynamics
Neither distributions nor abundances are static
Burmese python was introduced from Southeast Asia into South Florida; its North American range (as an exotic, non-native, invasive species) has been
expanding ever since
Native
range
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Even though the chosen example (Burmese python) is an anthropogenically-driven range expansion, dynamism occurs in all native and exotic populations.
Wikipedia “Burmese python” page; 09/IX/2014
Introduced
range
Maps & photo of American alligator consuming a Burmese python from Wikimedia Commons

11. Dynamics Neither distributions nor abundances are static
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Extinction is an “absorbing state.” Even though this chosen example (passenger pigeon) is an anthropogenically-driven range extinction, dynamism occurs in all native and exotic populations.
Passenger pigeons went extinct
when Martha (the very last individual)
died on Sept. 1, 1914
K. Harms photos taken at Smithsonian National Museum of Natural History, Washington, D. C.

12. Dispersal Links Populations
Natal dispersal
Other dispersal (among breeding sites, foraging patches, etc.)
Migration
Migratory
black-throated blue warbler
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Wikipedia “Black-throated blue warbler” page; 07/IX/2014
Rubenstein, D. R. et al Linking breeding and wintering ranges of a migratory songbird using stable isotopes. Science 295:
“Fig. 1. Temperate North American breeding range and Greater Antillean wintering range of the black-throated blue warbler. A few individuals also winter in the Florida everglades, on the coast of the Yucatan Peninsula, and in Belize (12). Sampling locations in the breeding grounds and wintering grounds are indicated by black circles. In the Greater Antilles, samples were collected from individuals wintering in
Jamaica (six sites in central and western portions), in Cuba (one site on a small cay, Cayo
Coco, 15 km off the north-central coast), in Hispaniola (two sites in the Dominican Republic
near the border with Haiti), and in Puerto Rico (three sites near the eastern end).”
“Fig. 2. Population means (SE) of d13C and dD in black-throated blue warbler feathers at different
breeding latitudes and wintering longitudes. Sample sizes are indicated next to each site. (A)
d13C values from the temperate North America breeding range decrease with increasing breeding latitude [F1,8 = 7.67, P = 0.024, r2 = 0.49, latitude = (d13C)]. (B) dD values from the temperate
North America breeding range decrease with increasing breeding latitude [F1,7 = 7.17, P = 0.032, r2 = 0.51, latitude=1.32 – 2.02(dD)]. (C) d13C values from the Greater Antillean wintering range decrease with increasing wintering longitude [F1,9 = 6.07, P = 0.036, r2 = 0.40, longitude =
0.07(d13C)]. (D) dD values from the Greater Antillean wintering range decrease with increasing
wintering longitude [F1,7 = 22.93, P = 0.002, r2 = 0.77, longitude = (dD)].”
Photo from Wikimedia Commons; Rubenstein et al. (2002) Science, Figs. 1 & 2

13. Distribution & Abundance are limited by
Habitat Suitability, History & Dispersal
Why are there no camelids in the Rain Forest Biome?
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Wikipedia “Camelid” page; 09/IX/2014
Habitat suitability probably limits modern camelids from occupying Rain Forests; camelids are grassland, shrubland, and hot desert organisms.
For a good discussion of these ideas, see:
Hubbell & Foster (1986) Biology, chance & history.
Range map & photos of extant camelids from Wikimedia Commons

14. Distribution & Abundance are limited by
Habitat Suitability, History & Dispersal
Why are there no camelids in North America?
Eocene Epoch – 56 to 33.9 mya
Examples of N. Am. Pleistocene Epoch megafauna (incl. Camelops)
that went extinct ~ 10,000 yr ago
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Wikipedia “Camelid” page; 09/IX/2014
Extinction history and limited dispersal probably resulted in a lack of camelids in modern N. Am.
For a good discussion of these ideas, see:
Hubbell & Foster (1986) Biology, chance & history.
Map from Wikimedia Commons; image from

15. cause each of these patterns?
Dispersion Patterns
What mechanisms could
cause each of these patterns?
Regular
(over-dispersed)
Clumped
Random
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Mapped individuals in 3 plots; in this case there is exactly the same density in each plot.
Each individual is represented by a dot.

16. Index of Dispersion (Variance-to-Mean Ratio)
Dispersion Patterns
Index of Dispersion (Variance-to-Mean Ratio)
 2
D = 
D > 1
D  1
D < 1
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Count number of individuals per quadrat and calculate variance-to-mean ratio.
R code to produce D for each figure:
> C=c(5,1,6,1,0,1,7,0,7)
> var(C)/mean(C)
[1]
> R=c(5,2,4,3,1,5,1,5,2)
> var(R)/mean(R)
[1]
> O=c(4,3,3,3,3,3,3,3,3)
> var(O)/mean(O)
[1]

17. Dispersion Patterns Scale of Focus At smaller scale D > 1
At larger scale
D < 1
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In this case – at the larger scale of focus, the pattern becomes regular! So, the pattern of dispersion is very often scale-dependent.

18. Methods Area-based counts – random or stratified random placement
of many replicate plots, quadrats or transects;
(average count/area) * total area = population estimate
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Photo of random quadrat placement from

19. Methods
Distance methods – employ detection probability functions (one for each species or habitat) to weight observations & calculate population estimates
E.g., line transect
of length, L d2 d1 d3
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Each distance observation is weighted by the detection probability function.
E.g., point sampling
for a period of time, t d2 d3 d1

20. Mark-recapture studies
Methods
Mark-recapture studies M1
= # of individuals caught & marked on 1st occasion N
= # of unknown individuals in the population R
= # of marked individuals caught on 2nd occasion M2
= # of individuals caught on 2nd occasion
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Wikimedia Commons file = “File:Wing tag Great Frigatebird.jpg”
M1 / N = R / M2
N * (M1 / N) = N * (R / M2)
M1 = (N * R) / M2
M2 * M1 = M2 * (N * R) / M2
M1 * M2 = N * R
(M1 * M2) / R = (N * R) / R
(M1 * M2) / R = N
M1 / N = R / M2
N = (M1  M2) / R
Photo of wing-tagged frigatebird from Wikimedia Commons

21. Ecological niche-modeling
Methods
Ecological niche-modeling
An analysis with a sense of humor:
ENMs for Bigfoot / Sasquatch
551 reported sightings & auditory detections;
95 reported footprints
Maximum entropy niche modeling approach implemented in software MAXENT
Environmental data layers for 9 BIOCLIM variables in WORLDCLIM data set:
annual mean temp.; mean diurnal range; isothermality (mean diurnal range / annual range); temp. annual range; mean temp. of wettest quarter; mean temp. of driest quarter; precip. seasonality; precip. of warmest quarter; precip. of coldest quarter
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Note: Bigfoot / Sasquatch does not exist. The paper was written to point out how mis-identification can mislead researchers who blindly employ ENMs.
Lozier et al Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modeling. Journal of Biogeography 36:
“Figure 1. Map of Bigfoot encounters from Washington, Oregon and California used in the analyses. Points represent visual/auditory detection, and foot symbols represent coordinates where footprint data were available. Shading indicates topography, with lighter values representing lower elevations.”
Lozier et al. (2009) J. Biogeogr.; Fig. 1

22. Ecological niche-modeling
Methods
Ecological niche-modeling
Predicted range under
current climate
Predicted range under doubled
[CO2]
“convincing environmentally predicted distributions… can be generated from questionable
site-occurrence data”
(Lozier et al. 2009)
Please do not use the images in these PowerPoint slides without permission.
Note: Bigfoot / Sasquatch does not exist. The paper was written to point out how mis-identification can mislead researchers who blindly employ ENMs.
Lozier et al Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modeling. Journal of Biogeography 36:
“Figure 2. Predicted distributions of Bigfoot constructed from all available encounter data using maxent (a) for the present climate and (b) under a possible climate change scenario involving a doubling of atmospheric CO2 levels. Results are presented for logistic probabilities of occurrence ranging continuously from low (white) to high (black). Differences between (a) and (b) are shown in (c), with whiter values reflecting a decline in logistic probability of occurrence under climate change, darker values reflecting a gain, and grey reflecting no change. A predicted distribution of Ursus americanus in western North America under a present-day climate is also shown (d). White points indicate sampling localities in California, Oregon and Washington taken from GBIF (n = 113 for training, 28 for testing; compare with Fig. 1) used for the maxent model with shading as in (a) and (b); black points indicate additional known records not included in the model.”
Lozier et al. (2009) J. Biogeogr.; Fig. 2

23. Ecological niche-modeling
Methods
Ecological niche-modeling
Predicted range of American black bear using the same procedure
Predicted range under
current climate
“many [Bigfoot] sightings… may be cases of mistaken identity”
(Lozier et al. 2009)
Please do not use the images in these PowerPoint slides without permission.
Note: Bigfoot / Sasquatch does not exist. The paper was written to point out how mis-identification can mislead researchers who blindly employ ENMs.
Lozier et al Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modeling. Journal of Biogeography 36:
“Figure 2. Predicted distributions of Bigfoot constructed from all available encounter data using maxent (a) for the present climate and (b) under a possible climate change scenario involving a doubling of atmospheric CO2 levels. Results are presented for logistic probabilities of occurrence ranging continuously from low (white) to high (black). Differences between (a) and (b) are shown in (c), with whiter values reflecting a decline in logistic probability of occurrence under climate change, darker values reflecting a gain, and grey reflecting no change. A predicted distribution of Ursus americanus in western North America under a present-day climate is also shown (d). White points indicate sampling localities in California, Oregon and Washington taken from GBIF (n = 113 for training, 28 for testing; compare with Fig. 1) used for the maxent model with shading as in (a) and (b); black points indicate additional known records not included in the model.”
Lozier et al. (2009) J. Biogeogr.; Fig. 2