Given the results of cpr_rand_test(), classifies phylogenetic endemism
according to CANAPE scheme of Mishler et al. 2014.
Arguments
- df
Input data frame. Must have the following columns:
pe_obs_p_upper: Upper p-value comparing observed phylogenetic endemism to random valuespe_alt_obs_p_upper: Upper p-value comparing observed phylogenetic endemism on alternate tree to random valuesrpe_obs_p_upper: Upper p-value comparing observed relative phylogenetic endemism to random values
Value
Object of class data.frame with column endem_type (character)
added. Values of endem_type type include paleo (paleoendemic), neo
(neoendemic), not significant (what it says), mixed (mixed endemism),
and super (super-endemic; both pe_obs and pe_obs_alt are highly
significant).
Details
For a summary of the classification scheme, see: http://biodiverse-analysis-software.blogspot.com/2014/11/canape-categorical-analysis-of-palaeo.html # nolint
References
Mishler, B., Knerr, N., González-Orozco, C. et al. (2014) Phylogenetic measures of biodiversity and neo- and paleo-endemism in Australian Acacia. Nat Commun, 5: 4473. doi:10.1038/ncomms5473
Examples
# \donttest{
set.seed(12345)
data(phylocom)
rand_test <- cpr_rand_test(
phylocom$comm, phylocom$phy,
null_model = "curveball", metrics = c("pe", "rpe"), n_reps = 10
)
#> Warning: Abundance data detected. Results will be the same as if using presence/absence data (no abundance weighting is used).
#> Warning: Dropping tips from the tree because they are not present in the community data:
#> sp16, sp23, sp27, sp28, sp30, sp31, sp32
cpr_classify_endem(rand_test)
#> pe_obs pe_rand_mean pe_rand_sd pe_obs_z pe_obs_c_upper
#> clump1 0.1333333 0.1622013 0.02479597 -1.1642182 1
#> clump2a 0.1081761 0.1563208 0.02319925 -2.0752680 1
#> clump2b 0.1286164 0.1727358 0.02557568 -1.7250565 0
#> clump4 0.1411950 0.1686164 0.02835495 -0.9670758 2
#> even 0.2506289 0.1752201 0.02225572 3.3882887 10
#> random 0.2380503 0.1649057 0.01987926 3.6794464 10
#> pe_obs_c_lower pe_obs_q pe_obs_p_upper pe_obs_p_lower pe_alt_obs
#> clump1 9 10 0.1 0.9 0.1472222
#> clump2a 9 10 0.1 0.9 0.1194444
#> clump2b 10 10 0.0 1.0 0.1420139
#> clump4 8 10 0.2 0.8 0.1454861
#> even 0 10 1.0 0.0 0.2246528
#> random 0 10 1.0 0.0 0.2211806
#> pe_alt_rand_mean pe_alt_rand_sd pe_alt_obs_z pe_alt_obs_c_upper
#> clump1 0.1596528 0.01541291 -0.8065027 2
#> clump2a 0.1583681 0.02159578 -1.8023714 1
#> clump2b 0.1712847 0.01951462 -1.4999435 0
#> clump4 0.1705556 0.01641871 -1.5268825 0
#> even 0.1761111 0.01130450 4.2940129 10
#> random 0.1640278 0.02422894 2.3588646 10
#> pe_alt_obs_c_lower pe_alt_obs_q pe_alt_obs_p_upper pe_alt_obs_p_lower
#> clump1 8 10 0.2 0.8
#> clump2a 9 10 0.1 0.9
#> clump2b 10 10 0.0 1.0
#> clump4 10 10 0.0 1.0
#> even 0 10 1.0 0.0
#> random 0 10 1.0 0.0
#> rpe_obs rpe_rand_mean rpe_rand_sd rpe_obs_z rpe_obs_c_upper
#> clump1 0.9056604 1.0167654 0.12496739 -0.8890724 0
#> clump2a 0.9056604 0.9881408 0.08895808 -0.9271831 0
#> clump2b 0.9056604 1.0064227 0.05648438 -1.7838966 0
#> clump4 0.9705048 0.9852723 0.09957128 -0.1483107 6
#> even 1.1156280 0.9946438 0.10702965 1.1303804 9
#> random 1.0762714 1.0140246 0.11480252 0.5422078 7
#> rpe_obs_c_lower rpe_obs_q rpe_obs_p_upper rpe_obs_p_lower
#> clump1 10 10 0.0 1.0
#> clump2a 10 10 0.0 1.0
#> clump2b 9 10 0.0 0.9
#> clump4 4 10 0.6 0.4
#> even 1 10 0.9 0.1
#> random 3 10 0.7 0.3
#> endem_type
#> clump1 not significant
#> clump2a not significant
#> clump2b not significant
#> clump4 not significant
#> even super
#> random super
# }