Indices

Indices available in Biodiverse

Generated GMT Fri Nov 25 07:18:43 2022 using build_indices_table.pl, Biodiverse version 4.0.

This is a listing of the indices available in Biodiverse, ordered by the calculations used to generate them. It is generated from the system metadata and contains all the information visible in the GUI, plus some additional details.

Most of the headings are self-explanatory. For the others:

The Subroutine is the name of the subroutine used to call the function if you are using Biodiverse through a script.
The Index is the name of the index in the SPATIAL_RESULTS list, or if it is its own list then this will be its name. These lists can contain a variety of values, but are usually lists of labels with some value, for example the weights used in an endemism calculation. The names of such lists typically end in "LIST", "ARRAY", "HASH", "LABELS" or "STATS".
Grouping? states whether or not the index can be used to define the grouping for a cluster or region grower analysis. A blank value means it cannot be used for either.
The Minimum number of neighbour sets dictates whether or not a calculation or index will be run. If you specify only one neighbour set then all those calculations that require two sets will be dropped from the analysis. (This is always the case for calculations applied to cluster nodes as there is only one neighbour set, defined by the set of groups linked to the terminal nodes below a cluster node). Note that many of the calculations lump neighbour sets 1 and 2 together. See the SpatialConditions page for more details on neighbour sets.

Note that calculations can provide different numbers of indices depending on the nature of the BaseData set used. This currently applies to the hierarchically partitioned endemism calculations (both central and whole) and hierarchical labels.

Indices available in Biodiverse:

Element Properties
Endemism
Hierarchical Labels
- Ratios of hierarchical labels
Lists and Counts
Matrix
Numeric Labels
PhyloCom Indices
Phylogenetic Endemism Indices
Phylogenetic Indices
Phylogenetic Indices (relative)
Phylogenetic Turnover
Rarity
Richness estimators
- ACE
- Chao1
- Chao2
- ICE
Taxonomic Dissimilarity and Comparison

Element Properties

Group property Gi* statistics

Description: List of Getis-Ord Gi* statistics for each group property across both neighbour sets

Subroutine: calc_gpprop_gistar

Reference: Getis and Ord (1992) Geographical Analysis. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
1	GPPROP_GISTAR_LIST	List of Gi* scores		1

Group property data

Description: Lists of the groups and their property values used in the group properties calculations

Subroutine: calc_gpprop_lists

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
2	GPPROP_STATS_GPROP1_DATA	List of values for property GPROP1		1
3	GPPROP_STATS_GPROP2_DATA	List of values for property GPROP2		1

Group property hashes

Description: Hashes of the groups and their property values used in the group properties calculations. Hash keys are the property values, hash values are the property value frequencies.

Subroutine: calc_gpprop_hashes

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
4	GPPROP_STATS_GPROP1_HASH	Hash of values for property GPROP1		1
5	GPPROP_STATS_GPROP2_HASH	Hash of values for property GPROP2		1

Group property quantiles

Description: Quantiles for each group property across both neighbour sets

Subroutine: calc_gpprop_quantiles

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
6	GPPROP_QUANTILE_LIST	List of quantiles for the label properties (05 10 20 30 40 50 60 70 80 90 95)		1

Group property summary stats

Description: List of summary statistics for each group property across both neighbour sets

Subroutine: calc_gpprop_stats

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
7	GPPROP_STATS_LIST	List of summary statistics (count mean min max median sum sd iqr)		1

Label property Gi* statistics

Description: List of Getis-Ord Gi* statistic for each label property across both neighbour sets

Subroutine: calc_lbprop_gistar

Reference: Getis and Ord (1992) Geographical Analysis. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
8	LBPROP_GISTAR_LIST	List of Gi* scores		1

Label property Gi* statistics (local range weighted)

Description: List of Getis-Ord Gi* statistic for each label property across both neighbour sets (local range weighted)

Subroutine: calc_lbprop_gistar_abc2

Reference: Getis and Ord (1992) Geographical Analysis. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
9	LBPROP_GISTAR_LIST_ABC2	List of Gi* scores		1

Label property data

Description: Lists of the labels and their property values used in the label properties calculations

Subroutine: calc_lbprop_data

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
10	LBPROP_STATS_EXPROP1_DATA	List of data for property EXPROP1		1
11	LBPROP_STATS_EXPROP2_DATA	List of data for property EXPROP2		1

Label property hashes

Description: Hashes of the labels and their property values used in the label properties calculations. Hash keys are the property values, hash values are the property value frequencies.

Subroutine: calc_lbprop_hashes

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
12	LBPROP_STATS_EXPROP1_HASH	Hash of values for property EXPROP1		1
13	LBPROP_STATS_EXPROP2_HASH	Hash of values for property EXPROP2		1

Label property hashes (local range weighted)

Description: Hashes of the labels and their property values used in the local range weighted label properties calculations. Hash keys are the property values, hash values are the property value frequencies.

Subroutine: calc_lbprop_hashes_abc2

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
14	LBPROP_STATS_EXPROP1_HASH2	Hash of values for property EXPROP1		1
15	LBPROP_STATS_EXPROP2_HASH2	Hash of values for property EXPROP2		1

Label property lists

Description: Lists of the labels and their property values within the neighbour sets

Subroutine: calc_lbprop_lists

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
16	LBPROP_LIST_EXPROP1	List of data for property EXPROP1		1
17	LBPROP_LIST_EXPROP2	List of data for property EXPROP2		1

Label property quantiles

Description: List of quantiles for each label property across both neighbour sets

Subroutine: calc_lbprop_quantiles

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
18	LBPROP_QUANTILES	List of quantiles for the label properties: (05 10 20 30 40 50 60 70 80 90 95)		1

Label property quantiles (local range weighted)

Description: List of quantiles for each label property across both neighbour sets (local range weighted)

Subroutine: calc_lbprop_quantiles_abc2

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
19	LBPROP_QUANTILES_ABC2	List of quantiles for the label properties: (05 10 20 30 40 50 60 70 80 90 95)		1

Label property summary stats

Description: List of summary statistics for each label property across both neighbour sets

Subroutine: calc_lbprop_stats

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
20	LBPROP_STATS	List of summary statistics (count mean min max median sum skewness kurtosis sd iqr)		1

Label property summary stats (local range weighted)

Description: List of summary statistics for each label property across both neighbour sets, weighted by local ranges

Subroutine: calc_lbprop_stats_abc2

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
21	LBPROP_STATS_ABC2	List of summary statistics (count mean min max median sum skewness kurtosis sd iqr)		1

Endemism

Absolute endemism

Description: Absolute endemism scores.

Subroutine: calc_endemism_absolute

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
22	END_ABS1	Count of labels entirely endemic to neighbour set 1	region grower	1
23	END_ABS1_P	Proportion of labels entirely endemic to neighbour set 1	region grower	1
24	END_ABS2	Count of labels entirely endemic to neighbour set 2	region grower	1
25	END_ABS2_P	Proportion of labels entirely endemic to neighbour set 2	region grower	1
26	END_ABS_ALL	Count of labels entirely endemic to neighbour sets 1 and 2 combined	region grower	1
27	END_ABS_ALL_P	Proportion of labels entirely endemic to neighbour sets 1 and 2 combined	region grower	1

Absolute endemism lists

Description: Lists underlying the absolute endemism scores.

Subroutine: calc_endemism_absolute_lists

Index #	Index	Index description	Minimum number of neighbour sets
28	END_ABS1_LIST	List of labels entirely endemic to neighbour set 1	1
29	END_ABS2_LIST	List of labels entirely endemic to neighbour set 1	1
30	END_ABS_ALL_LIST	List of labels entirely endemic to neighbour sets 1 and 2 combined	1

Endemism central

Description: Calculate endemism for labels only in neighbour set 1, but with local ranges calculated using both neighbour sets

Subroutine: calc_endemism_central

Reference: Crisp et al. (2001) J Biogeog. https://doi.org/10.1046/j.1365-2699.2001.00524.x ; Laffan and Crisp (2003) J Biogeog. http://www3.interscience.wiley.com/journal/118882020/abstract

Index #	Index	Index description	Minimum number of neighbour sets	Formula	Reference
31	ENDC_CWE	Corrected weighted endemism	1	$= \frac{ENDC\_WE}{ENDC\_RICHNESS}$
32	ENDC_RICHNESS	Richness used in ENDC_CWE (same as index RICHNESS_SET1)	1
33	ENDC_SINGLE	Endemism unweighted by the number of neighbours. Counts each label only once, regardless of how many groups in the neighbourhood it is found in. Useful if your data have sampling biases and best applied with a small window.	1	$= \sum_{t \in T} \frac {1} {R_t}$ where is a label (taxon) in the set of labels (taxa) in neighbour set 1, and is the global range of label across the data set (the number of groups it is found in, unless the range is specified at import).	Slatyer et al. (2007) J. Biogeog https://doi.org/10.1111/j.1365-2699.2006.01647.x
34	ENDC_WE	Weighted endemism	1	$= \sum_{t \in T} \frac {r_t} {R_t}$ where is a label (taxon) in the set of labels (taxa) in neighbour set 1, is the local range (the number of elements containing label within neighbour sets 1 & 2, this is also its value in list ABC2_LABELS_ALL), and is the global range of label across the data set (the number of groups it is found in, unless the range is specified at import).

Endemism central hierarchical partition

Description: Partition the endemism central results based on the taxonomic hierarchy inferred from the label axes. (Level 0 is the highest).

Subroutine: calc_endemism_central_hier_part

Reference: Laffan et al. (2013) J Biogeog. https://doi.org/10.1111/jbi.12001

Index #	Index	Index description	Minimum number of neighbour sets
35	ENDC_HPART_0	List of the proportional contribution of labels to the endemism central calculations, hierarchical level 0	1
36	ENDC_HPART_1	List of the proportional contribution of labels to the endemism central calculations, hierarchical level 1	1
37	ENDC_HPART_C_0	List of the proportional count of labels to the endemism central calculations (equivalent to richness per hierarchical grouping), hierarchical level 0	1
38	ENDC_HPART_C_1	List of the proportional count of labels to the endemism central calculations (equivalent to richness per hierarchical grouping), hierarchical level 1	1
39	ENDC_HPART_E_0	List of the expected proportional contribution of labels to the endemism central calculations (richness per hierarchical grouping divided by overall richness), hierarchical level 0	1
40	ENDC_HPART_E_1	List of the expected proportional contribution of labels to the endemism central calculations (richness per hierarchical grouping divided by overall richness), hierarchical level 1	1
41	ENDC_HPART_OME_0	List of the observed minus expected proportional contribution of labels to the endemism central calculations , hierarchical level 0	1
42	ENDC_HPART_OME_1	List of the observed minus expected proportional contribution of labels to the endemism central calculations , hierarchical level 1	1

Endemism central lists

Description: Lists used in endemism central calculations

Subroutine: calc_endemism_central_lists

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
43	ENDC_RANGELIST	List of ranges for each label used in the endemism central calculations		1
44	ENDC_WTLIST	List of weights for each label used in the endemism central calculations		1

Endemism central normalised

Description: Normalise the WE and CWE scores by the neighbourhood size. (The number of groups used to determine the local ranges).

Subroutine: calc_endemism_central_normalised

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
45	ENDC_CWE_NORM	Corrected weighted endemism normalised by groups		1	$= \frac{ENDC\_CWE}{EL\_COUNT\_ALL}$
46	ENDC_WE_NORM	Weighted endemism normalised by groups		1	$= \frac{ENDC\_WE}{EL\_COUNT\_ALL}$

Endemism whole

Description: Calculate endemism using all labels found in both neighbour sets

Subroutine: calc_endemism_whole

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula	Reference
47	ENDW_CWE	Corrected weighted endemism	region grower	1	$= \frac{ENDW\_WE}{ENDW\_RICHNESS}$
48	ENDW_RICHNESS	Richness used in ENDW_CWE (same as index RICHNESS_ALL)	region grower	1
49	ENDW_SINGLE	Endemism unweighted by the number of neighbours. Counts each label only once, regardless of how many groups in the neighbourhood it is found in. Useful if your data have sampling biases and best applied with a small window.	region grower	1	$= \sum_{t \in T} \frac {1} {R_t}$ where is a label (taxon) in the set of labels (taxa) across neighbour sets 1 & 2, and is the global range of label across the data set (the number of groups it is found in, unless the range is specified at import).	Slatyer et al. (2007) J. Biogeog https://doi.org/10.1111/j.1365-2699.2006.01647.x
50	ENDW_WE	Weighted endemism	region grower	1	$= \sum_{t \in T} \frac {r_t} {R_t}$ where is a label (taxon) in the set of labels (taxa) across both neighbour sets, is the local range (the number of elements containing label within neighbour sets 1 & 2, this is also its value in list ABC2_LABELS_ALL), and is the global range of label across the data set (the number of groups it is found in, unless the range is specified at import).

Endemism whole hierarchical partition

Description: Partition the endemism whole results based on the taxonomic hierarchy inferred from the label axes. (Level 0 is the highest).

Subroutine: calc_endemism_whole_hier_part

Reference: Laffan et al. (2013) J Biogeog. https://doi.org/10.1111/jbi.12001

Index #	Index	Index description	Minimum number of neighbour sets
51	ENDW_HPART_0	List of the proportional contribution of labels to the endemism whole calculations, hierarchical level 0	1
52	ENDW_HPART_1	List of the proportional contribution of labels to the endemism whole calculations, hierarchical level 1	1
53	ENDW_HPART_C_0	List of the proportional count of labels to the endemism whole calculations (equivalent to richness per hierarchical grouping), hierarchical level 0	1
54	ENDW_HPART_C_1	List of the proportional count of labels to the endemism whole calculations (equivalent to richness per hierarchical grouping), hierarchical level 1	1
55	ENDW_HPART_E_0	List of the expected proportional contribution of labels to the endemism whole calculations (richness per hierarchical grouping divided by overall richness), hierarchical level 0	1
56	ENDW_HPART_E_1	List of the expected proportional contribution of labels to the endemism whole calculations (richness per hierarchical grouping divided by overall richness), hierarchical level 1	1
57	ENDW_HPART_OME_0	List of the observed minus expected proportional contribution of labels to the endemism whole calculations , hierarchical level 0	1
58	ENDW_HPART_OME_1	List of the observed minus expected proportional contribution of labels to the endemism whole calculations , hierarchical level 1	1

Endemism whole lists

Description: Lists used in the endemism whole calculations

Subroutine: calc_endemism_whole_lists

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
59	ENDW_RANGELIST	List of ranges for each label used in the endemism whole calculations		1
60	ENDW_WTLIST	List of weights for each label used in the endemism whole calculations		1

Endemism whole normalised

Description: Normalise the WE and CWE scores by the neighbourhood size. (The number of groups used to determine the local ranges).

Subroutine: calc_endemism_whole_normalised

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
61	ENDW_CWE_NORM	Corrected weighted endemism normalised by groups	region grower	1	$= \frac{ENDW\_CWE}{EL\_COUNT\_ALL}$
62	ENDW_WE_NORM	Weighted endemism normalised by groups	region grower	1	$= \frac{ENDW\_WE}{EL\_COUNT\_ALL}$

Hierarchical Labels

Ratios of hierarchical labels

Description: Analyse the diversity of labels using their hierarchical levels. The A, B and C scores are the same as in the Label Counts analysis (calc_label_counts) but calculated for each hierarchical level, e.g. for three axes one could have A0 as the Family level, A1 for the Family:Genus level, and A2 for the Family:Genus:Species level. The number of indices generated depends on how many axes are used in the labels. In this case there are 2. Axes are numbered from zero as the highest level in the hierarchy, so level 0 is the top level of the hierarchy.

Subroutine: calc_hierarchical_label_ratios

Reference: Jones and Laffan (2008) Trans Philol Soc https://doi.org/10.1111/j.1467-968X.2008.00209.x

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
63	HIER_A0	A score for level 0		1
64	HIER_A1	A score for level 1		1
65	HIER_ARAT1_0	Ratio of A scores, (HIER_A1 / HIER_A0)		1
66	HIER_ASUM0	Sum of shared label sample counts, level 0		1
67	HIER_ASUM1	Sum of shared label sample counts, level 1		1
68	HIER_ASUMRAT1_0	1 - Ratio of shared label sample counts, (HIER_ASUM1 / HIER_ASUM0)	cluster metric	1
69	HIER_B0	B score for level 0		1
70	HIER_B1	B score for level 1		1
71	HIER_BRAT1_0	Ratio of B scores, (HIER_B1 / HIER_B0)		1
72	HIER_C0	C score for level 0		1
73	HIER_C1	C score for level 1		1
74	HIER_CRAT1_0	Ratio of C scores, (HIER_C1 / HIER_C0)		1

Lists and Counts

Element counts

Description: Counts of elements used in neighbour sets 1 and 2.

Subroutine: calc_elements_used

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
75	EL_COUNT_ALL	Count of elements in both neighbour sets	region grower	1
76	EL_COUNT_SET1	Count of elements in neighbour set 1		1
77	EL_COUNT_SET2	Count of elements in neighbour set 2		2

Element lists

Description: Lists of elements used in neighbour sets 1 and 2. These form the basis for all the spatial calculations.

Subroutine: calc_element_lists_used

Index #	Index	Index description	Minimum number of neighbour sets
78	EL_LIST_ALL	List of elements in both neighour sets	2
79	EL_LIST_SET1	List of elements in neighbour set 1	1
80	EL_LIST_SET2	List of elements in neighbour set 2	2

Label counts

Description: Counts of labels in neighbour sets 1 and 2. These form the basis for the Taxonomic Dissimilarity and Comparison indices.

Subroutine: calc_abc_counts

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
81	ABC_A	Count of labels common to both neighbour sets	region grower	1
82	ABC_ABC	Total label count across both neighbour sets (same as RICHNESS_ALL)	region grower	1
83	ABC_B	Count of labels unique to neighbour set 1	region grower	1
84	ABC_C	Count of labels unique to neighbour set 2	region grower	1

Label counts not in sample

Description: Count of basedata labels not in either neighbour set (shared absence) Used in some of the dissimilarity metrics.

Subroutine: calc_d

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
85	ABC_D	Count of labels not in either neighbour set (D score)	region grower	1

Local range lists

Description: Lists of labels with their local ranges as values. The local ranges are the number of elements in which each label is found in each neighour set.

Subroutine: calc_local_range_lists

Index #	Index	Index description	Minimum number of neighbour sets
86	ABC2_LABELS_ALL	List of labels in both neighbour sets	2
87	ABC2_LABELS_SET1	List of labels in neighbour set 1	1
88	ABC2_LABELS_SET2	List of labels in neighbour set 2	2

Local range summary statistics

Description: Summary stats of the local ranges within neighour sets.

Subroutine: calc_local_range_stats

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
89	ABC2_MEAN_ALL	Mean label range in both element sets	region grower	1
90	ABC2_MEAN_SET1	Mean label range in neighbour set 1		1
91	ABC2_MEAN_SET2	Mean label range in neighbour set 2		2
92	ABC2_SD_ALL	Standard deviation of label ranges in both element sets	region grower	2
93	ABC2_SD_SET1	Standard deviation of label ranges in neighbour set 1		1
94	ABC2_SD_SET2	Standard deviation of label ranges in neighbour set 2		2

Non-empty element counts

Description: Counts of non-empty elements in neighbour sets 1 and 2.

Subroutine: calc_nonempty_elements_used

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
95	EL_COUNT_NONEMPTY_ALL	Count of non-empty elements in both neighbour sets	region grower	1
96	EL_COUNT_NONEMPTY_SET1	Count of non-empty elements in neighbour set 1		1
97	EL_COUNT_NONEMPTY_SET2	Count of non-empty elements in neighbour set 2		2

Rank relative sample counts per label

Description: Find the per-group percentile rank of all labels across both neighbour sets, relative to the processing group. An absence is treated as a sample count of zero.

Subroutine: calc_label_count_quantile_position

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
98	LABEL_COUNT_RANK_PCT	List of percentile ranks for each label's sample count		1

Redundancy

Description: Ratio of labels to samples. Values close to 1 are well sampled while zero means there is no redundancy in the sampling

Subroutine: calc_redundancy

Reference: Garcillan et al. (2003) J Veget. Sci. https://doi.org/10.1111/j.1654-1103.2003.tb02174.x

Formula: $= 1 - \frac{richness}{sum\ of\ the\ sample\ counts}$

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
99	REDUNDANCY_ALL	for both neighbour sets	region grower	1	$= 1 - \frac{RICHNESS\_ALL}{ABC3\_SUM\_ALL}$
100	REDUNDANCY_SET1	for neighour set 1		1	$= 1 - \frac{RICHNESS\_SET1}{ABC3\_SUM\_SET1}$
101	REDUNDANCY_SET2	for neighour set 2		2	$= 1 - \frac{RICHNESS\_SET2}{ABC3\_SUM\_SET2}$

Richness

Description: Count the number of labels in the neighbour sets

Subroutine: calc_richness

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
102	RICHNESS_ALL	for both sets of neighbours	region grower	1
103	RICHNESS_SET1	for neighbour set 1		1
104	RICHNESS_SET2	for neighbour set 2		2

Sample count lists

Description: Lists of sample counts for each label within the neighbour sets. These form the basis of the sample indices.

Subroutine: calc_local_sample_count_lists

Index #	Index	Index description	Minimum number of neighbour sets
105	ABC3_LABELS_ALL	List of labels in both neighbour sets with their sample counts as the values.	2
106	ABC3_LABELS_SET1	List of labels in neighbour set 1. Values are the sample counts.	1
107	ABC3_LABELS_SET2	List of labels in neighbour set 2. Values are the sample counts.	2

Sample count quantiles

Description: Quantiles of the sample counts across the neighbour sets.

Subroutine: calc_local_sample_count_quantiles

Index #	Index	Index description	Minimum number of neighbour sets
108	ABC3_QUANTILES_ALL	List of quantiles for both neighbour sets	2
109	ABC3_QUANTILES_SET1	List of quantiles for neighbour set 1	1
110	ABC3_QUANTILES_SET2	List of quantiles for neighbour set 2	2

Sample count summary stats

Description: Summary stats of the sample counts across the neighbour sets.

Subroutine: calc_local_sample_count_stats

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
111	ABC3_MEAN_ALL	Mean of label sample counts across both element sets.	region grower	2
112	ABC3_MEAN_SET1	Mean of label sample counts in neighbour set1.		1
113	ABC3_MEAN_SET2	Mean of label sample counts in neighbour set 2.		2
114	ABC3_SD_ALL	Standard deviation of label sample counts in both element sets.	region grower	2
115	ABC3_SD_SET1	Standard deviation of sample counts in neighbour set 1.		1
116	ABC3_SD_SET2	Standard deviation of label sample counts in neighbour set 2.		2
117	ABC3_SUM_ALL	Sum of the label sample counts across both neighbour sets.	region grower	2
118	ABC3_SUM_SET1	Sum of the label sample counts across both neighbour sets.		1
119	ABC3_SUM_SET2	Sum of the label sample counts in neighbour set2.		2

Matrix

Compare dissimilarity matrix values

Description: Compare the set of labels in one neighbour set with those in another using their matrix values. Labels not in the matrix are ignored. This calculation assumes a matrix of dissimilarities and uses 0 as identical, so take care).

Subroutine: calc_compare_dissim_matrix_values

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
120	MXD_COUNT	Count of comparisons used.	region grower	1
121	MXD_LIST1	List of the labels used from neighbour set 1 (those in the matrix). The list values are the number of times each label was used in the calculations. This will always be 1 for labels in neighbour set 1.		1
122	MXD_LIST2	List of the labels used from neighbour set 2 (those in the matrix). The list values are the number of times each label was used in the calculations. This will equal the number of labels used from neighbour set 1.		1
123	MXD_MEAN	Mean dissimilarity of labels in set 1 to those in set 2.	cluster metric	1
124	MXD_VARIANCE	Variance of the dissimilarity values, set 1 vs set 2.	cluster metric	1

Matrix statistics

Description: Calculate summary statistics of matrix elements in the selected matrix for labels found across both neighbour sets. Labels not in the matrix are ignored.

Subroutine: calc_matrix_stats

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
125	MX_KURT	Kurtosis	region grower	1
126	MX_LABELS	List of the matrix labels in the neighbour sets		1
127	MX_MAXVALUE	Maximum value	region grower	1
128	MX_MEAN	Mean	region grower	1
129	MX_MEDIAN	Median	region grower	1
130	MX_MINVALUE	Minimum value	region grower	1
131	MX_N	Number of samples (matrix elements, not labels)	region grower	1
132	MX_PCT05	5th percentile value	region grower	1
133	MX_PCT25	First quartile (25th percentile)	region grower	1
134	MX_PCT75	Third quartile (75th percentile)	region grower	1
135	MX_PCT95	95th percentile value	region grower	1
136	MX_RANGE	Range (max-min)	region grower	1
137	MX_SD	Standard deviation	region grower	1
138	MX_SKEW	Skewness	region grower	1
139	MX_VALUES	List of the matrix values		1

Rao's quadratic entropy, matrix weighted

Description: Calculate Rao's quadratic entropy for a matrix weights scheme. BaseData labels not in the matrix are ignored

Subroutine: calc_mx_rao_qe

Formula: $= \sum_{i \in L} \sum_{j \in L} d_{ij} p_i p_j$ where p_i and p_j are the sample counts for the i'th and j'th labels, $d_{ij}$ is the matrix value for the pair of labels and is the set of labels across both neighbour sets that occur in the matrix.

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
140	MX_RAO_QE	Matrix weighted quadratic entropy	region grower	1
141	MX_RAO_TLABELS	List of labels and values used in the MX_RAO_QE calculations		1
142	MX_RAO_TN	Count of comparisons used to calculate MX_RAO_QE	region grower	1

Numeric Labels

Numeric label data

Description: The underlying data used for the numeric labels stats, as an array. For the hash form, use the ABC3_LABELS_ALL index from the 'Sample count lists' calculation.

Subroutine: calc_numeric_label_data

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
143	NUM_DATA_ARRAY	Numeric label data in array form. Multiple occurrences are repeated based on their sample counts.		1

Numeric label dissimilarity

Description: Compare the set of numeric labels in one neighbour set with those in another.

Subroutine: calc_numeric_label_dissimilarity

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
144	NUMD_ABSMEAN	Mean absolute dissimilarity of labels in set 1 to those in set 2.	cluster metric	1	$= \frac{\sum_{l_{1i} \in L_1} \sum_{l_{2j} \in L_2} abs (l_{1i} - l_{2j})(w_{1i} \times w_{2j})}{n_1 \times n_2}$ where and are the labels in neighbour sets 1 and 2 respectively, and and are the sample counts in neighbour sets 1 and 2
145	NUMD_COUNT	Count of comparisons used.	region grower	1	where values are as for $NUMD\_ABSMEAN$
146	NUMD_VARIANCE	Variance of the dissimilarity values (mean squared deviation), set 1 vs set 2.	cluster metric	1	$= \frac{\sum_{l_{1i} \in L_1} \sum_{l_{2j} \in L_2} (l_{1i} - l_{2j})^2(w_{1i} \times w_{2j})}{n_1 \times n_2}$ where values are as for $NUMD\_ABSMEAN$

Numeric label harmonic and geometric means

Description: Calculate geometric and harmonic means for a set of numeric labels.

Subroutine: calc_numeric_label_other_means

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
147	NUM_GMEAN	Geometric mean	region grower	1
148	NUM_HMEAN	Harmonic mean	region grower	1

Numeric label quantiles

Description: Calculate quantiles from a set of numeric labels. Weights by samples so multiple occurrences are accounted for.

Subroutine: calc_numeric_label_quantiles

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
149	NUM_Q005	5th percentile	region grower	1
150	NUM_Q010	10th percentile	region grower	1
151	NUM_Q015	15th percentile	region grower	1
152	NUM_Q020	20th percentile	region grower	1
153	NUM_Q025	25th percentile	region grower	1
154	NUM_Q030	30th percentile	region grower	1
155	NUM_Q035	35th percentile	region grower	1
156	NUM_Q040	40th percentile	region grower	1
157	NUM_Q045	45th percentile	region grower	1
158	NUM_Q050	50th percentile	region grower	1
159	NUM_Q055	55th percentile	region grower	1
160	NUM_Q060	60th percentile	region grower	1
161	NUM_Q065	65th percentile	region grower	1
162	NUM_Q070	70th percentile	region grower	1
163	NUM_Q075	75th percentile	region grower	1
164	NUM_Q080	80th percentile	region grower	1
165	NUM_Q085	85th percentile	region grower	1
166	NUM_Q090	90th percentile	region grower	1
167	NUM_Q095	95th percentile	region grower	1

Numeric label statistics

Description: Calculate summary statistics from a set of numeric labels. Weights by samples so multiple occurrences are accounted for.

Subroutine: calc_numeric_label_stats

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
168	NUM_CV	Coefficient of variation (NUM_SD / NUM_MEAN)	region grower	1
169	NUM_KURT	Kurtosis	region grower	1
170	NUM_MAX	Maximum value (100th quantile)	region grower	1
171	NUM_MEAN	Mean	region grower	1
172	NUM_MIN	Minimum value (zero quantile)	region grower	1
173	NUM_N	Number of samples	region grower	1
174	NUM_RANGE	Range (max - min)	region grower	1
175	NUM_SD	Standard deviation	region grower	1
176	NUM_SKEW	Skewness	region grower	1

Numeric labels Gi* statistic

Description: Getis-Ord Gi* statistic for numeric labels across both neighbour sets

Subroutine: calc_num_labels_gistar

Reference: Getis and Ord (1992) Geographical Analysis. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
177	NUM_GISTAR	List of Gi* scores	region grower	1

PhyloCom Indices

NRI and NTI expected values

Description: Expected values used in the NRI and NTI calculations. Derived using a null model without resampling where each label has an equal probability of being selected (a null model of even distrbution). The expected mean and SD are the same for each unique number of labels across all neighbour sets. This means if you have three neighbour sets, each with three labels, then the expected values will be identical for each, even if the labels are completely different.

Subroutine: calc_nri_nti_expected_values

Reference: Webb et al. (2008) https://doi.org/10.1093/bioinformatics/btn358, Tsirogiannis et al. (2012) https://doi.org/10.1007/978-3-642-33122-0_3

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
178	PHYLO_NRI_NTI_SAMPLE_N	Number of random resamples used	region grower	1
179	PHYLO_NRI_SAMPLE_MEAN	Expected mean of pair-wise distances	region grower	1
180	PHYLO_NRI_SAMPLE_SD	Expected standard deviation of pair-wise distances	region grower	1
181	PHYLO_NTI_SAMPLE_MEAN	Expected mean of nearest taxon distances	region grower	1
182	PHYLO_NTI_SAMPLE_SD	Expected standard deviation of nearest taxon distances	region grower	1

NRI and NTI, abundance weighted

Description: NRI and NTI for the set of labels on the tree in the sample. This version is -1* the Phylocom implementation, so values >0 have longer branches than expected. Abundance weighted.

Subroutine: calc_nri_nti3

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
183	PHYLO_NRI3	Net Relatedness Index, abundance weighted	region grower	1
184	PHYLO_NTI3	Nearest Taxon Index, abundance weighted	region grower	1

NRI and NTI, local range weighted

Description: NRI and NTI for the set of labels on the tree in the sample. This version is -1* the Phylocom implementation, so values >0 have longer branches than expected. Local range weighted.

Subroutine: calc_nri_nti2

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
185	PHYLO_NRI2	Net Relatedness Index, local range weighted	region grower	1
186	PHYLO_NTI2	Nearest Taxon Index, local range weighted	region grower	1

NRI and NTI, unweighted

Description: NRI and NTI for the set of labels on the tree in the sample. This version is -1* the Phylocom implementation, so values >0 have longer branches than expected. Not weighted by sample counts, so each label counts once only.

Subroutine: calc_nri_nti1

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
187	PHYLO_NRI1	Net Relatedness Index, unweighted	region grower	1	$NRI = \frac{MPD_{obs} - mean(MPD_{rand})}{sd(MPD_{rand})}$
188	PHYLO_NTI1	Nearest Taxon Index, unweighted	region grower	1	$NTI = \frac{MNTD_{obs} - mean(MNTD_{rand})}{sd(MNTD_{rand})}$

Net VPD expected values

Description: Expected values for VPD, analogous to the NRI/NTI results

Subroutine: calc_vpd_expected_values

Reference: Warwick & Clarke (2001) https://dx.doi.org/10.3354/meps216265

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
189	PHYLO_NET_VPD_SAMPLE_MEAN	Expected mean of pair-wise variance (VPD)	region grower	1
190	PHYLO_NET_VPD_SAMPLE_N	Number of random resamples used to calculate expected pair-wise variance scores(will equal PHYLO_NRI_NTI_SAMPLE_N for non-ultrametric trees)	region grower	1
191	PHYLO_NET_VPD_SAMPLE_SD	Expected standard deviation of pair-wise variance (VPD)	region grower	1

Net variance of pair-wise phylogenetic distances, unweighted

Description: Z-score of VPD calculated using NRI/NTI resampling Not weighted by sample counts, so each label counts once only.

Subroutine: calc_net_vpd

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
192	PHYLO_NET_VPD	Net variance of pair-wise phylogenetic distances, unweighted	region grower	1

Phylogenetic and Nearest taxon distances, abundance weighted

Description: Distance stats from each label to the nearest label along the tree. Compares with all other labels across both neighbour sets. Weighted by sample counts (which currently must be integers)

Subroutine: calc_phylo_mpd_mntd3

Reference: Webb et al. (2008) https://doi.org/10.1093/bioinformatics/btn358 Warwick & Clarke (2001) https://dx.doi.org/10.3354/meps216265

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
193	PMPD3_MAX	Maximum of pairwise phylogenetic distances	region grower	1
194	PMPD3_MEAN	Mean of pairwise phylogenetic distances	region grower	1	$MPD = \frac {\sum_{t_i = 1}^{n_t-1} \sum_{t_j = 1}^{n_t} d_{t_i \leftrightarrow t_j}}{(n_t-1)^2}, i \neq j$ where $d_{t_i \leftrightarrow t_j} = \sum_{b \in B_{t_i \leftrightarrow t_j}} L_b$ is the sum of the branch lengths along the path connecting and such that is the length of each branch in the set of branches
195	PMPD3_MIN	Minimum of pairwise phylogenetic distances	region grower	1
196	PMPD3_N	Count of pairwise phylogenetic distances	region grower	1
197	PMPD3_RMSD	Root mean squared pairwise phylogenetic distances	region grower	1
198	PMPD3_VARIANCE	Variance of pairwise phylogenetic distances, similar to Clarke and Warwick (2001; http://dx.doi.org/10.3354/meps216265) but uses tip-to-tip distances instead of tip to most recent common ancestor.	region grower	1
199	PNTD3_MAX	Maximum of nearest taxon distances	region grower	1
200	PNTD3_MEAN	Mean of nearest taxon distances	region grower	1
201	PNTD3_MIN	Minimum of nearest taxon distances	region grower	1
202	PNTD3_N	Count of nearest taxon distances	region grower	1
203	PNTD3_RMSD	Root mean squared nearest taxon distances	region grower	1
204	PNTD3_VARIANCE	Variance of nearest taxon distances	region grower	1

Phylogenetic and Nearest taxon distances, local range weighted

Description: Distance stats from each label to the nearest label along the tree. Compares with all other labels across both neighbour sets. Weighted by sample counts

Subroutine: calc_phylo_mpd_mntd2

Reference: Webb et al. (2008) https://doi.org/10.1093/bioinformatics/btn358 Warwick & Clarke (2001) https://dx.doi.org/10.3354/meps216265

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
205	PMPD2_MAX	Maximum of pairwise phylogenetic distances	region grower	1
206	PMPD2_MEAN	Mean of pairwise phylogenetic distances	region grower	1	$MPD = \frac {\sum_{t_i = 1}^{n_t-1} \sum_{t_j = 1}^{n_t} d_{t_i \leftrightarrow t_j}}{(n_t-1)^2}, i \neq j$ where $d_{t_i \leftrightarrow t_j} = \sum_{b \in B_{t_i \leftrightarrow t_j}} L_b$ is the sum of the branch lengths along the path connecting and such that is the length of each branch in the set of branches
207	PMPD2_MIN	Minimum of pairwise phylogenetic distances	region grower	1
208	PMPD2_N	Count of pairwise phylogenetic distances	region grower	1
209	PMPD2_RMSD	Root mean squared pairwise phylogenetic distances	region grower	1
210	PMPD2_VARIANCE	Variance of pairwise phylogenetic distances, similar to Clarke and Warwick (2001; http://dx.doi.org/10.3354/meps216265) but uses tip-to-tip distances instead of tip to most recent common ancestor.	region grower	1
211	PNTD2_MAX	Maximum of nearest taxon distances	region grower	1
212	PNTD2_MEAN	Mean of nearest taxon distances	region grower	1
213	PNTD2_MIN	Minimum of nearest taxon distances	region grower	1
214	PNTD2_N	Count of nearest taxon distances	region grower	1
215	PNTD2_RMSD	Root mean squared nearest taxon distances	region grower	1
216	PNTD2_VARIANCE	Variance of nearest taxon distances	region grower	1

Phylogenetic and Nearest taxon distances, unweighted

Description: Distance stats from each label to the nearest label along the tree. Compares with all other labels across both neighbour sets.

Subroutine: calc_phylo_mpd_mntd1

Reference: Webb et al. (2008) https://doi.org/10.1093/bioinformatics/btn358 Warwick & Clarke (2001) https://dx.doi.org/10.3354/meps216265

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
217	PMPD1_MAX	Maximum of pairwise phylogenetic distances	region grower	1
218	PMPD1_MEAN	Mean of pairwise phylogenetic distances	region grower	1	$MPD = \frac {\sum_{t_i = 1}^{n_t-1} \sum_{t_j = 1}^{n_t} d_{t_i \leftrightarrow t_j}}{(n_t-1)^2}, i \neq j$ where $d_{t_i \leftrightarrow t_j} = \sum_{b \in B_{t_i \leftrightarrow t_j}} L_b$ is the sum of the branch lengths along the path connecting and such that is the length of each branch in the set of branches
219	PMPD1_MIN	Minimum of pairwise phylogenetic distances	region grower	1
220	PMPD1_N	Count of pairwise phylogenetic distances	region grower	1
221	PMPD1_RMSD	Root mean squared pairwise phylogenetic distances	region grower	1
222	PMPD1_VARIANCE	Variance of pairwise phylogenetic distances, similar to Clarke and Warwick (2001; http://dx.doi.org/10.3354/meps216265) but uses tip-to-tip distances instead of tip to most recent common ancestor.	region grower	1
223	PNTD1_MAX	Maximum of nearest taxon distances	region grower	1
224	PNTD1_MEAN	Mean of nearest taxon distances	region grower	1
225	PNTD1_MIN	Minimum of nearest taxon distances	region grower	1
226	PNTD1_N	Count of nearest taxon distances	region grower	1
227	PNTD1_RMSD	Root mean squared nearest taxon distances	region grower	1
228	PNTD1_VARIANCE	Variance of nearest taxon distances	region grower	1

Phylogenetic Endemism Indices

Corrected weighted phylogenetic endemism

Description: What proportion of the PD is range-restricted to this neighbour set?

Subroutine: calc_phylo_corrected_weighted_endemism

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula	Reference
229	PE_CWE	Corrected weighted endemism. This is the phylogenetic analogue of corrected weighted endemism.	region grower	1	$PE\_WE / PD$

Corrected weighted phylogenetic endemism, central variant

Description: What proportion of the PD in neighbour set 1 is range-restricted to neighbour sets 1 and 2?

Subroutine: calc_pe_central_cwe

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
230	PEC_CWE	Corrected weighted phylogenetic endemism, central variant	region grower	1
231	PEC_CWE_PD	PD used in the PEC_CWE index.	region grower	1

Corrected weighted phylogenetic rarity

Description: What proportion of the PD is abundance-restricted to this neighbour set?

Subroutine: calc_phylo_corrected_weighted_rarity

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula	Reference
232	PHYLO_RARITY_CWR	Corrected weighted phylogenetic rarity. This is the phylogenetic rarity analogue of corrected weighted endemism.	region grower	1

PD-Endemism

Description: Absolute endemism analogue of PE. It is the sum of the branch lengths restricted to the neighbour sets.

Subroutine: calc_pd_endemism

Reference: See Faith (2004) Cons Biol. https://doi.org/10.1111/j.1523-1739.2004.00330.x

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
233	PD_ENDEMISM	Phylogenetic Diversity Endemism	region grower	1
234	PD_ENDEMISM_P	Phylogenetic Diversity Endemism, as a proportion of the whole tree	region grower	1
235	PD_ENDEMISM_WTS	Phylogenetic Diversity Endemism weights per node found only in the neighbour set		1

PE clade contributions

Description: Contribution of each node and its descendents to the Phylogenetic endemism (PE) calculation.

Subroutine: calc_pe_clade_contributions

Index #	Index	Index description	Minimum number of neighbour sets
236	PE_CLADE_CONTR	List of node (clade) contributions to the PE calculation	1
237	PE_CLADE_CONTR_P	List of node (clade) contributions to the PE calculation, proportional to the entire tree	1
238	PE_CLADE_SCORE	List of PE scores for each node (clade), being the sum of all descendent PE weights	1

PE clade loss

Description: How much of the PE would be lost if a clade were to be removed? Calculates the clade PE below the last ancestral node in the neighbour set which would still be in the neighbour set.

Subroutine: calc_pe_clade_loss

Index #	Index	Index description	Minimum number of neighbour sets
239	PE_CLADE_LOSS_CONTR	List of the proportion of the PE score which would be lost if each clade were removed.	1
240	PE_CLADE_LOSS_CONTR_P	As per PE_CLADE_LOSS but proportional to the entire tree	1
241	PE_CLADE_LOSS_SCORE	List of how much PE would be lost if each clade were removed.	1

PE clade loss (ancestral component)

Description: How much of the PE clade loss is due to the ancestral branches? The score is zero when there is no ancestral loss.

Subroutine: calc_pe_clade_loss_ancestral

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
242	PE_CLADE_LOSS_ANC	List of how much ancestral PE would be lost if each clade were removed. The value is 0 when no ancestral PE is lost.		1
243	PE_CLADE_LOSS_ANC_P	List of the proportion of the clade's PE loss that is due to the ancestral branches.		1

Phylogenetic Endemism

Description: Phylogenetic endemism (PE). Uses labels across both neighbourhoods and trims the tree to exclude labels not in the BaseData object.

Subroutine: calc_pe

Reference: Rosauer et al (2009) Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2009.04311.x; Laity et al. (2015) https://doi.org/10.1016/j.scitotenv.2015.04.113; Laffan et al. (2016) https://doi.org/10.1111/2041-210X.12513

Formula: $PE = \sum_{\lambda \in \Lambda } L_{\lambda}\frac{r_\lambda}{R_\lambda}$ where $\Lambda$ is the set of branches found across neighbour sets 1 and 2, $L_\lambda$ is the length of branch $\lambda$ , $r_\lambda$ is the local range of branch $\lambda$ (the number of groups in neighbour sets 1 and 2 containing it), and $R_\lambda$ is the global range of branch $\lambda$ (the number of groups across the entire data set containing it).

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
244	PE_WE	Phylogenetic endemism	region grower	1
245	PE_WE_P	Phylogenetic weighted endemism as a proportion of the total tree length	region grower	1	$PE\_WE / L$ where L is the sum of all branch lengths in the trimmed tree

Phylogenetic Endemism central

Description: A variant of Phylogenetic endemism (PE) that uses labels from neighbour set 1 but local ranges from across both neighbour sets 1 and 2. Identical to PE if only one neighbour set is specified.

Subroutine: calc_pe_central

Reference: Rosauer et al (2009) Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2009.04311.x

Formula: $PEC = \sum_{\lambda \in \Lambda } L_{\lambda}\frac{r_\lambda}{R_\lambda}$ where $\Lambda$ is the set of branches found across neighbour set 1 only, $L_\lambda$ is the length of branch $\lambda$ , $r_\lambda$ is the local range of branch $\lambda$ (the number of groups in neighbour sets 1 and 2 containing it), and $R_\lambda$ is the global range of branch $\lambda$ (the number of groups across the entire data set containing it).

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
246	PEC_WE	Phylogenetic endemism, central variant	region grower	1
247	PEC_WE_P	Phylogenetic weighted endemism as a proportion of the total tree length, central variant	region grower	1

Phylogenetic Endemism central lists

Description: Lists underlying the phylogenetic endemism central indices. Uses labels from neighbour set one but local ranges from across both neighbour sets.

Subroutine: calc_pe_central_lists

Reference: Rosauer et al (2009) Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2009.04311.x

Index #	Index	Index description	Minimum number of neighbour sets
248	PEC_LOCAL_RANGELIST	Phylogenetic endemism local range lists, central variant	1
249	PEC_RANGELIST	Phylogenetic endemism global range lists, central variant	1
250	PEC_WTLIST	Phylogenetic endemism weights, central variant	1

Phylogenetic Endemism lists

Description: Lists used in the Phylogenetic endemism (PE) calculations.

Subroutine: calc_pe_lists

Reference: Rosauer et al (2009) Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2009.04311.x

Index #	Index	Index description	Minimum number of neighbour sets
251	PE_LOCAL_RANGELIST	Local node ranges used in PE calculations (number of groups in which a node is found)	1
252	PE_RANGELIST	Node ranges used in PE calculations	1
253	PE_WTLIST	Node weights used in PE calculations	1

Phylogenetic Endemism single

Description: PE scores, but not weighted by local ranges. This is the strict interpretation of the formula given in Rosauer et al. (2009), although the approach has always been implemented as the fraction of each branch's geographic range that is found in the sample window (see formula for PE_WE).

Subroutine: calc_pe_single

Reference: Rosauer et al (2009) Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2009.04311.x

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
254	PE_WE_SINGLE	Phylogenetic endemism unweighted by the number of neighbours. Counts each label only once, regardless of how many groups in the neighbourhood it is found in. Useful if your data have sampling biases. Better with small sample windows.	region grower	1
255	PE_WE_SINGLE_P	Phylogenetic endemism unweighted by the number of neighbours as a proportion of the total tree length. Counts each label only once, regardless of how many groups in the neighbourhood it is found. Useful if your data have sampling biases.	region grower	1

Phylogenetic Indices

Count labels on tree

Description: Count the number of labels that are on the tree

Subroutine: calc_count_labels_on_tree

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
256	PHYLO_LABELS_ON_TREE_COUNT	The number of labels that are found on the tree, across both neighbour sets	region grower	1

Evolutionary distinctiveness

Description: Evolutionary distinctiveness metrics (AED, ED, ES) Label values are constant for all neighbourhoods in which each label is found.

Subroutine: calc_phylo_aed

Reference: Cadotte & Davies (2010) https://doi.org/10.1111/j.1472-4642.2010.00650.x

Index #	Index	Index description	Minimum number of neighbour sets	Reference
257	PHYLO_AED_LIST	Abundance weighted ED per terminal label	1	Cadotte & Davies (2010) https://doi.org/10.1111/j.1472-4642.2010.00650.x
258	PHYLO_ED_LIST	"Fair proportion" partitioning of PD per terminal label	1	Isaac et al. (2007) https://doi.org/10.1371/journal.pone.0000296
259	PHYLO_ES_LIST	Equal splits partitioning of PD per terminal label	1	Redding & Mooers (2006) https://doi.org/10.1111%2Fj.1523-1739.2006.00555.x

Evolutionary distinctiveness per site

Description: Site level evolutionary distinctiveness

Subroutine: calc_phylo_aed_t

Reference: Cadotte & Davies (2010) https://doi.org/10.1111/j.1472-4642.2010.00650.x

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Reference
260	PHYLO_AED_T	Abundance weighted ED_t (sum of values in PHYLO_AED_LIST times their abundances). This is equivalent to a phylogenetic rarity score (see phylogenetic endemism)	region grower	1	Cadotte & Davies (2010) https://doi.org/10.1111/j.1472-4642.2010.00650.x

Evolutionary distinctiveness per terminal taxon per site

Description: Site level evolutionary distinctiveness per terminal taxon

Subroutine: calc_phylo_aed_t_wtlists

Reference: Cadotte & Davies (2010) https://doi.org/10.1111/j.1472-4642.2010.00650.x

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Reference
261	PHYLO_AED_T_WTLIST	Abundance weighted ED per terminal taxon (the AED score of each taxon multiplied by its abundance in the sample)		1	Cadotte & Davies (2010) https://doi.org/10.1111/j.1472-4642.2010.00650.x
262	PHYLO_AED_T_WTLIST_P	Proportional contribution of each terminal taxon to the AED_T score		1	Cadotte & Davies (2010) https://doi.org/10.1111/j.1472-4642.2010.00650.x

Labels not on tree

Description: Create a hash of the labels that are not on the tree

Subroutine: calc_labels_not_on_tree

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
263	PHYLO_LABELS_NOT_ON_TREE	A hash of labels that are not found on the tree, across both neighbour sets		1
264	PHYLO_LABELS_NOT_ON_TREE_N	Number of labels not on the tree	region grower	1
265	PHYLO_LABELS_NOT_ON_TREE_P	Proportion of labels not on the tree	region grower	1

Labels on tree

Description: Create a hash of the labels that are on the tree

Subroutine: calc_labels_on_tree

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
266	PHYLO_LABELS_ON_TREE	A hash of labels that are found on the tree, across both neighbour sets		1

Last shared ancestor properties

Description: Properties of the last shared ancestor of an assemblage. Uses labels in both neighbourhoods.

Subroutine: calc_last_shared_ancestor_props

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
267	LAST_SHARED_ANCESTOR_DEPTH	Depth of last shared ancestor from the root. The root has a depth of zero.	region grower	1
268	LAST_SHARED_ANCESTOR_DIST_TO_ROOT	Distance along the tree from the last shared ancestor to the root. Includes the shared ancestor's length.	region grower	1
269	LAST_SHARED_ANCESTOR_DIST_TO_TIP	Distance along the tree from the last shared ancestor to the furthest tip in the sample. This is calculated from the point at which the lineages merge, which is the branch end further from the root	region grower	1
270	LAST_SHARED_ANCESTOR_LENGTH	Branch length of last shared ancestor	region grower	1
271	LAST_SHARED_ANCESTOR_POS_REL	Relative position of the last shared ancestor. Value is the fraction of the distance from the root to the furthest terminal.This uses the point at which the lineages merge, and is the branch end further from the root	region grower	1

PD clade contributions

Description: Contribution of each node and its descendents to the Phylogenetic diversity (PD) calculation.

Subroutine: calc_pd_clade_contributions

Index #	Index	Index description	Minimum number of neighbour sets
272	PD_CLADE_CONTR	List of node (clade) contributions to the PD calculation	1
273	PD_CLADE_CONTR_P	List of node (clade) contributions to the PD calculation, proportional to the entire tree	1
274	PD_CLADE_SCORE	List of PD scores for each node (clade), being the sum of all descendent branch lengths	1

PD clade loss

Description: How much of the PD would be lost if a clade were to be removed? Calculates the clade PD below the last ancestral node in the neighbour set which would still be in the neighbour set.

Subroutine: calc_pd_clade_loss

Index #	Index	Index description	Minimum number of neighbour sets
275	PD_CLADE_LOSS_CONTR	List of the proportion of the PD score which would be lost if each clade were removed.	1
276	PD_CLADE_LOSS_CONTR_P	As per PD_CLADE_LOSS but proportional to the entire tree	1
277	PD_CLADE_LOSS_SCORE	List of how much PD would be lost if each clade were removed.	1

PD clade loss (ancestral component)

Description: How much of the PD clade loss is due to the ancestral branches? The score is zero when there is no ancestral loss.

Subroutine: calc_pd_clade_loss_ancestral

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
278	PD_CLADE_LOSS_ANC	List of how much ancestral PE would be lost if each clade were removed. The value is 0 when no ancestral PD is lost.		1
279	PD_CLADE_LOSS_ANC_P	List of the proportion of the clade's PD loss that is due to the ancestral branches.		1

Phylogenetic Abundance

Description: Phylogenetic abundance based on branch lengths back to the root of the tree. Uses labels in both neighbourhoods.

Subroutine: calc_phylo_abundance

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula	Reference
280	PHYLO_ABUNDANCE	Phylogenetic abundance	region grower	1	$= \sum_{c \in C} A \times L_c$ where is the set of branches in the minimum spanning path joining the labels in both neighbour sets to the root of the tree, is a branch (a single segment between two nodes) in the spanning path , and is the length of branch , and is the abundance of that branch (the sum of its descendant label abundances).
281	PHYLO_ABUNDANCE_BRANCH_HASH	Phylogenetic abundance per branch		1

Phylogenetic Diversity

Description: Phylogenetic diversity (PD) based on branch lengths back to the root of the tree. Uses labels in both neighbourhoods.

Subroutine: calc_pd

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula	Reference
282	PD	Phylogenetic diversity	region grower	1	$= \sum_{c \in C} L_c$ where is the set of branches in the minimum spanning path joining the labels in both neighbour sets to the root of the tree, is a branch (a single segment between two nodes) in the spanning path , and is the length of branch .	Faith (1992) Biol. Cons. https://doi.org/10.1016/0006-3207(92)91201-3
283	PD_P	Phylogenetic diversity as a proportion of total tree length	region grower	1	$= \frac { PD }{ \sum_{c \in C} L_c }$ where terms are the same as for PD, but , and are calculated for all nodes in the tree.
284	PD_P_per_taxon	Phylogenetic diversity per taxon as a proportion of total tree length	region grower	1	$= \frac { PD\_P }{ RICHNESS\_ALL }$
285	PD_per_taxon	Phylogenetic diversity per taxon	region grower	1	$= \frac { PD }{ RICHNESS\_ALL }$

Phylogenetic Diversity (local)

Description: Phylogenetic diversity (PD) based on branch lengths back to the last shared ancestor. Uses labels in both neighbourhoods.

Subroutine: calc_pd_local

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
286	PD_LOCAL	Phylogenetic diversity calculated to last shared ancestor	region grower	1	$= \sum_{c \in C} L_c$ where is the set of branches in the minimum spanning path joining the labels in both neighbour sets to the last shared ancestor, is a branch (a single segment between two nodes) in the spanning path , and is the length of branch .
287	PD_LOCAL_P	Phylogenetic diversity as a proportion of total tree length	region grower	1	$= \frac { PD }{ \sum_{c \in C} L_c }$ where terms are the same as for PD, but , and are calculated for all nodes in the tree.

Phylogenetic Diversity node list

Description: Phylogenetic diversity (PD) nodes used.

Subroutine: calc_pd_node_list

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
288	PD_INCLUDED_NODE_LIST	List of tree nodes included in the PD calculations		1

Phylogenetic Diversity terminal node count

Description: Number of terminal nodes in neighbour sets 1 and 2.

Subroutine: calc_pd_terminal_node_count

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
289	PD_INCLUDED_TERMINAL_NODE_COUNT	Count of tree terminal nodes included in the PD calculations	region grower	1

Phylogenetic Diversity terminal node list

Description: Phylogenetic diversity (PD) terminal nodes used.

Subroutine: calc_pd_terminal_node_list

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
290	PD_INCLUDED_TERMINAL_NODE_LIST	List of tree terminal nodes included in the PD calculations		1

Phylogenetic Indices (relative)

Labels not on trimmed tree

Description: Create a hash of the labels that are not on the trimmed tree

Subroutine: calc_labels_not_on_trimmed_tree

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
291	PHYLO_LABELS_NOT_ON_TRIMMED_TREE	A hash of labels that are not found on the tree after it has been trimmed to the basedata, across both neighbour sets		1
292	PHYLO_LABELS_NOT_ON_TRIMMED_TREE_N	Number of labels not on the trimmed tree	region grower	1
293	PHYLO_LABELS_NOT_ON_TRIMMED_TREE_P	Proportion of labels not on the trimmed tree	region grower	1

Labels on trimmed tree

Description: Create a hash of the labels that are on the trimmed tree

Subroutine: calc_labels_on_trimmed_tree

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
294	PHYLO_LABELS_ON_TRIMMED_TREE	A hash of labels that are found on the tree after it has been trimmed to match the basedata, across both neighbour sets		1

Relative Phylogenetic Diversity, type 1

Description: Relative Phylogenetic Diversity type 1 (RPD1). The ratio of the tree's PD to a null model of PD evenly distributed across terminals and where ancestral nodes are collapsed to zero length.You probably want to use RPD2 instead as it uses the tree's topology.

Subroutine: calc_phylo_rpd1

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
295	PHYLO_RPD1	RPD1	region grower	1
296	PHYLO_RPD_DIFF1	How much more or less PD is there than expected, in original tree units.	region grower	1	$= tree\_length \times (PD\_P - PHYLO\_RPD\_NULL1)$
297	PHYLO_RPD_NULL1	Null model score used as the denominator in the RPD1 calculations	region grower	1

Relative Phylogenetic Diversity, type 2

Description: Relative Phylogenetic Diversity (RPD), type 2. The ratio of the tree's PD to a null model of PD evenly distributed across all nodes (all branches are of equal length).

Subroutine: calc_phylo_rpd2

Reference: Mishler et al. (2014) https://doi.org/10.1038/ncomms5473

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
298	PHYLO_RPD2	RPD2	region grower	1
299	PHYLO_RPD_DIFF2	How much more or less PD is there than expected, in original tree units.	region grower	1	$= tree\_length \times (PD\_P - PHYLO\_RPD\_NULL2)$
300	PHYLO_RPD_NULL2	Null model score used as the denominator in the RPD2 calculations	region grower	1

Relative Phylogenetic Endemism, central

Description: Relative Phylogenetic Endemism (RPE). The ratio of the tree's PE to a null model where PE is calculated using a tree where all branches are of equal length. Same as RPE2 except it only uses the branches in the first neighbour set when more than one is set.

Subroutine: calc_phylo_rpe_central

Reference: Mishler et al. (2014) https://doi.org/10.1038/ncomms5473

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
301	PHYLO_RPEC	Relative Phylogenetic Endemism score, central	region grower	1
302	PHYLO_RPE_DIFFC	How much more or less PE is there than expected, in original tree units.	region grower	1	$= tree\_length \times (PE\_WEC\_P - PHYLO\_RPE\_NULLC)$
303	PHYLO_RPE_NULLC	Null score used as the denominator in the PHYLO_RPEC calculations	region grower	1

Relative Phylogenetic Endemism, type 1

Description: Relative Phylogenetic Endemism, type 1 (RPE1). The ratio of the tree's PE to a null model of PD evenly distributed across terminals, but with the same range per terminal and where ancestral nodes are of zero length (as per RPD1).You probably want to use RPE2 instead as it uses the tree's topology.

Subroutine: calc_phylo_rpe1

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
304	PHYLO_RPE1	Relative Phylogenetic Endemism score	region grower	1
305	PHYLO_RPE_DIFF1	How much more or less PE is there than expected, in original tree units.	region grower	1	$= tree\_length \times (PE\_WE\_P - PHYLO\_RPE\_NULL1)$
306	PHYLO_RPE_NULL1	Null score used as the denominator in the RPE calculations	region grower	1

Relative Phylogenetic Endemism, type 2

Description: Relative Phylogenetic Endemism (RPE). The ratio of the tree's PE to a null model where PE is calculated using a tree where all non-zero branches are of equal length.

Subroutine: calc_phylo_rpe2

Reference: Mishler et al. (2014) https://doi.org/10.1038/ncomms5473

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
307	PHYLO_RPE2	Relative Phylogenetic Endemism score, type 2	region grower	1
308	PHYLO_RPE_DIFF2	How much more or less PE is there than expected, in original tree units.	region grower	1	$= tree\_length \times (PE\_WE\_P - PHYLO\_RPE\_NULL2)$
309	PHYLO_RPE_NULL2	Null score used as the denominator in the RPE2 calculations	region grower	1

Phylogenetic Turnover

Phylo Jaccard

Description: Jaccard phylogenetic dissimilarity between two sets of taxa, represented by spanning sets of branches

Subroutine: calc_phylo_jaccard

Reference: Lozupone and Knight (2005) https://doi.org/10.1128/AEM.71.12.8228-8235.2005

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
310	PHYLO_JACCARD	Phylo Jaccard score	cluster metric	1	where A is the length of shared branches, and B and C are the length of branches found only in neighbour sets 1 and 2

Phylo Range weighted Turnover

Description: Phylo Range weighted Turnover

Subroutine: calc_phylo_rw_turnover

Reference: Laffan et al. (2016) https://doi.org/10.1111/2041-210X.12513

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
311	PHYLO_RW_TURNOVER	Range weighted turnover	cluster metric	1
312	PHYLO_RW_TURNOVER_A	Range weighted turnover, shared component	region grower	1
313	PHYLO_RW_TURNOVER_B	Range weighted turnover, component found only in nbr set 1	region grower	1
314	PHYLO_RW_TURNOVER_C	Range weighted turnover, component found only in nbr set 2	region grower	1

Phylo S2

Description: S2 phylogenetic dissimilarity between two sets of taxa, represented by spanning sets of branches

Subroutine: calc_phylo_s2

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
315	PHYLO_S2	Phylo S2 score	cluster metric	1	where A is the sum of shared branch lengths, and B and C are the sum of branch lengths foundonly in neighbour sets 1 and 2

Phylo Sorenson

Description: Sorenson phylogenetic dissimilarity between two sets of taxa, represented by spanning sets of branches

Subroutine: calc_phylo_sorenson

Reference: Bryant et al. (2008) https://doi.org/10.1073/pnas.0801920105

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
316	PHYLO_SORENSON	Phylo Sorenson score	cluster metric	1	where A is the length of shared branches, and B and C are the length of branches found only in neighbour sets 1 and 2

Phylogenetic ABC

Description: Calculate the shared and not shared branch lengths between two sets of labels

Subroutine: calc_phylo_abc

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
317	PHYLO_A	Length of branches shared by labels in nbr sets 1 and 2	region grower	1
318	PHYLO_ABC	Length of all branches associated with labels in nbr sets 1 and 2	region grower	1
319	PHYLO_B	Length of branches unique to labels in nbr set 1		1
320	PHYLO_C	Length of branches unique to labels in nbr set 2		1

Rarity

Rarity central

Description: Calculate rarity for species only in neighbour set 1, but with local sample counts calculated from both neighbour sets. Uses the same algorithm as the endemism indices but weights by sample counts instead of by groups occupied.

Subroutine: calc_rarity_central

Index #	Index	Index description	Minimum number of neighbour sets	Formula
321	RAREC_CWE	Corrected weighted rarity	1	$= \frac{RAREC\_WE}{RAREC\_RICHNESS}$
322	RAREC_RICHNESS	Richness used in RAREC_CWE (same as index RICHNESS_SET1).	1
323	RAREC_WE	Weighted rarity	1	$= \sum_{t \in T} \frac {s_t} {S_t}$ where is a label (taxon) in the set of labels (taxa) across neighbour set 1, is sum of the sample counts for across the elements in neighbour sets 1 & 2 (its value in list ABC3_LABELS_ALL), and is the total number of samples across the data set for label (unless the total sample count is specified at import).

Rarity central lists

Description: Lists used in rarity central calculations

Subroutine: calc_rarity_central_lists

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
324	RAREC_RANGELIST	List of ranges for each label used in the rarity central calculations		1
325	RAREC_WTLIST	List of weights for each label used in therarity central calculations		1

Rarity whole

Description: Calculate rarity using all species in both neighbour sets. Uses the same algorithm as the endemism indices but weights by sample counts instead of by groups occupied.

Subroutine: calc_rarity_whole

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
326	RAREW_CWE	Corrected weighted rarity	region grower	1	$= \frac{RAREW\_WE}{RAREW\_RICHNESS}$
327	RAREW_RICHNESS	Richness used in RAREW_CWE (same as index RICHNESS_ALL).	region grower	1
328	RAREW_WE	Weighted rarity	region grower	1	$= \sum_{t \in T} \frac {s_t} {S_t}$ where is a label (taxon) in the set of labels (taxa) across both neighbour sets, is sum of the sample counts for across the elements in neighbour sets 1 & 2 (its value in list ABC3_LABELS_ALL), and is the total number of samples across the data set for label (unless the total sample count is specified at import).

Rarity whole lists

Description: Lists used in rarity whole calculations

Subroutine: calc_rarity_whole_lists

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
329	RAREW_RANGELIST	List of ranges for each label used in the rarity whole calculations		1
330	RAREW_WTLIST	List of weights for each label used in therarity whole calculations		1

Richness estimators

ACE

Description: Abundance Coverage-based Estimator os species richness

Subroutine: calc_ace

Reference: needed

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
331	ACE_CI_LOWER	ACE lower confidence interval estimate	region grower	1
332	ACE_CI_UPPER	ACE upper confidence interval estimate	region grower	1
333	ACE_ESTIMATE	ACE score	region grower	1
334	ACE_ESTIMATE_USED_CHAO	Set to 1 when ACE cannot be calculated and so Chao1 estimate is used	region grower	1
335	ACE_INFREQUENT_COUNT	Count of infrequent species	region grower	1
336	ACE_SE	ACE standard error	region grower	1
337	ACE_UNDETECTED	Estimated number of undetected species	region grower	1
338	ACE_VARIANCE	ACE variance	region grower	1

Chao1

Description: Chao1 species richness estimator (abundance based)

Subroutine: calc_chao1

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Reference
339	CHAO1_CI_LOWER	Lower confidence interval for the Chao1 estimate	region grower	1
340	CHAO1_CI_UPPER	Upper confidence interval for the Chao1 estimate	region grower	1
341	CHAO1_ESTIMATE	Chao1 index	region grower	1	NEEDED
342	CHAO1_F1_COUNT	Number of singletons in the sample	region grower	1
343	CHAO1_F2_COUNT	Number of doubletons in the sample	region grower	1
344	CHAO1_META	Metadata indicating which formulae were used in the calculations. Numbers refer to EstimateS equations at http://viceroy.eeb.uconn.edu/EstimateS/EstimateSPages/EstSUsersGuide/EstimateSUsersGuide.htm		1
345	CHAO1_SE	Standard error of the Chao1 estimator [= sqrt(variance)]	region grower	1
346	CHAO1_UNDETECTED	Estimated number of undetected species	region grower	1
347	CHAO1_VARIANCE	Variance of the Chao1 estimator	region grower	1

Chao2

Description: Chao2 species richness estimator (incidence based)

Subroutine: calc_chao2

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Reference
348	CHAO2_CI_LOWER	Lower confidence interval for the Chao2 estimate	region grower	1
349	CHAO2_CI_UPPER	Upper confidence interval for the Chao2 estimate	region grower	1
350	CHAO2_ESTIMATE	Chao2 index	region grower	1	NEEDED
351	CHAO2_META	Metadata indicating which formulae were used in the calculations. Numbers refer to EstimateS equations at http://viceroy.eeb.uconn.edu/EstimateS/EstimateSPages/EstSUsersGuide/EstimateSUsersGuide.htm		1
352	CHAO2_Q1_COUNT	Number of uniques in the sample	region grower	1
353	CHAO2_Q2_COUNT	Number of duplicates in the sample	region grower	1
354	CHAO2_SE	Standard error of the Chao2 estimator [= sqrt (variance)]	region grower	1
355	CHAO2_UNDETECTED	Estimated number of undetected species	region grower	1
356	CHAO2_VARIANCE	Variance of the Chao2 estimator	region grower	1

ICE

Description: Incidence Coverage-based Estimator of species richness

Subroutine: calc_ice

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
357	ICE_CI_LOWER	ICE lower confidence interval estimate	region grower	1
358	ICE_CI_UPPER	ICE upper confidence interval estimate	region grower	1
359	ICE_ESTIMATE	ICE score	region grower	1
360	ICE_ESTIMATE_USED_CHAO	Set to 1 when ICE cannot be calculated and so Chao2 estimate is used	region grower	1
361	ICE_INFREQUENT_COUNT	Count of infrequent species	region grower	1
362	ICE_SE	ICE standard error	region grower	1
363	ICE_UNDETECTED	Estimated number of undetected species	region grower	1
364	ICE_VARIANCE	ICE variance	region grower	1

Taxonomic Dissimilarity and Comparison

Beta diversity

Description: Beta diversity between neighbour sets 1 and 2.

Subroutine: calc_beta_diversity

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
365	BETA_2	The other beta	cluster metric	1	$= \frac{A + B + C}{max((A+B), (A+C))} - 1$ where is the count of labels found in both neighbour sets, is the count unique to neighbour set 1, and is the count unique to neighbour set 2. Use the Label counts calculation to derive these directly.

Bray-Curtis non-metric

Description: Bray-Curtis dissimilarity between two sets of labels. Reduces to the Sorenson metric for binary data (where sample counts are 1 or 0).

Subroutine: calc_bray_curtis

Formula: $= 1 - \frac{2W}{A + B}$ where is the sum of the sample counts in neighbour set 1, is the sum of sample counts in neighbour set 2, and $W=\sum^n_{i=1} min(sample\count\label{i{set1}},sample\count\label{i{set2}})$ (meaning it sums the minimum of the sample counts for each of the labels across the two neighbour sets),

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
366	BC_A	The A factor used in calculations (see formula)		1
367	BC_B	The B factor used in calculations (see formula)		1
368	BC_W	The W factor used in calculations (see formula)	region grower	1
369	BRAY_CURTIS	Bray Curtis dissimilarity	cluster metric	1

Bray-Curtis non-metric, group count normalised

Description: Bray-Curtis dissimilarity between two neighbourhoods, where the counts in each neighbourhood are divided by the number of groups in each neighbourhood to correct for unbalanced sizes.

Subroutine: calc_bray_curtis_norm_by_gp_counts

Formula: $= 1 - \frac{2W}{A + B}$ where is the sum of the sample counts in neighbour set 1 normalised (divided) by the number of groups, is the sum of the sample counts in neighbour set 2 normalised by the number of groups, and $W = \sum^n_{i=1} min(sample\count\label{i{set1}},sample\count\label{i{set2}})$ (meaning it sums the minimum of the normalised sample counts for each of the labels across the two neighbour sets),

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
370	BCN_A	The A factor used in calculations (see formula)		1
371	BCN_B	The B factor used in calculations (see formula)		1
372	BCN_W	The W factor used in calculations (see formula)	region grower	1
373	BRAY_CURTIS_NORM	Bray Curtis dissimilarity normalised by groups	cluster metric	1

Jaccard

Description: Jaccard dissimilarity between the labels in neighbour sets 1 and 2.

Subroutine: calc_jaccard

Formula: $= 1 - \frac{A}{A + B + C}$ where is the count of labels found in both neighbour sets, is the count unique to neighbour set 1, and is the count unique to neighbour set 2. Use the Label counts calculation to derive these directly.

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
374	JACCARD	Jaccard value, 0 is identical, 1 is completely dissimilar	cluster metric	1

Kulczynski 2

Description: Kulczynski 2 dissimilarity between two sets of labels.

Subroutine: calc_kulczynski2

Formula: $= 1 - 0.5 * (\frac{A}{A + B} + \frac{A}{A + C})$ where is the count of labels found in both neighbour sets, is the count unique to neighbour set 1, and is the count unique to neighbour set 2. Use the Label counts calculation to derive these directly.

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
375	KULCZYNSKI2	Kulczynski 2 index	cluster metric	1

Nestedness-resultant

Description: Nestedness-resultant index between the labels in neighbour sets 1 and 2.

Subroutine: calc_nestedness_resultant

Reference: Baselga (2010) Glob Ecol Biogeog. https://doi.org/10.1111/j.1466-8238.2009.00490.x

Formula: $=\frac{ \left | B - C \right | }{ 2A + B + C } \times \frac { A }{ A + min (B, C) }= SORENSON - S2$ where is the count of labels found in both neighbour sets, is the count unique to neighbour set 1, and is the count unique to neighbour set 2. Use the Label counts calculation to derive these directly.

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
376	NEST_RESULTANT	Nestedness-resultant index	cluster metric	1

Range weighted Sorenson

Description: Range weighted Sorenson

Subroutine: calc_rw_turnover

Reference: Laffan et al. (2016) https://doi.org/10.1111/2041-210X.12513

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
377	RW_TURNOVER	Range weighted turnover	cluster metric	1
378	RW_TURNOVER_A	Range weighted turnover, shared component	region grower	1
379	RW_TURNOVER_B	Range weighted turnover, component found only in nbr set 1	region grower	1
380	RW_TURNOVER_C	Range weighted turnover, component found only in nbr set 2	region grower	1

Rao's quadratic entropy, taxonomically weighted

Description: Calculate Rao's quadratic entropy for a taxonomic weights scheme. Should collapse to be the Simpson index for presence/absence data.

Subroutine: calc_tx_rao_qe

Formula: $= \sum_{i \in L} \sum_{j \in L} d_{ij} p_i p_j$ where p_i and p_j are the sample counts for the i'th and j'th labels, $d_{ij}$ is a value of zero if i = j , and a value of 1 otherwise. is the set of labels across both neighbour sets.

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
381	TX_RAO_QE	Taxonomically weighted quadratic entropy	region grower	1
382	TX_RAO_TLABELS	List of labels and values used in the TX_RAO_QE calculations		1
383	TX_RAO_TN	Count of comparisons used to calculate TX_RAO_QE	region grower	1

S2

Description: S2 dissimilarity between two sets of labels

Subroutine: calc_s2

Reference: Lennon et al. (2001) J Animal Ecol. https://doi.org/10.1046/j.0021-8790.2001.00563.x

Formula: $= 1 - \frac{A}{A + min(B, C)}$ where is the count of labels found in both neighbour sets, is the count unique to neighbour set 1, and is the count unique to neighbour set 2. Use the Label counts calculation to derive these directly.

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
384	S2	S2 dissimilarity index	cluster metric	1

Simpson and Shannon

Description: Simpson and Shannon diversity metrics using samples from all neighbourhoods.

Subroutine: calc_simpson_shannon

Formula: For each index formula, p_i is the number of samples of the i'th label as a proportion of the total number of samples in the neighbourhoods.

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets	Formula
385	SHANNON_E	Shannon's evenness (H / HMAX)	region grower	1	$Evenness = \frac{H}{HMAX}$
386	SHANNON_H	Shannon's H	region grower	1	$H = - \sum^n_{i=1} (p_i \cdot ln (p_i))$
387	SHANNON_HMAX	maximum possible value of Shannon's H	region grower	1
388	SIMPSON_D	Simpson's D. A score of zero is more similar.	region grower	1	$D = 1 - \sum^n_{i=1} p_i^2$

Sorenson

Description: Sorenson dissimilarity between two sets of labels. It is the complement of the (unimplemented) Czechanowski index, and numerically the same as Whittaker's beta.

Subroutine: calc_sorenson

Formula: $= 1 - \frac{2A}{2A + B + C}$ where is the count of labels found in both neighbour sets, is the count unique to neighbour set 1, and is the count unique to neighbour set 2. Use the Label counts calculation to derive these directly.

Index #	Index	Index description	Grouping metric?	Minimum number of neighbour sets
389	SORENSON	Sorenson index	cluster metric	1

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