Step 2: Choosing a biome scheme

Goal

Step 1 gave us occurrence records and the 31 biome schemes. With 31 schemes to choose from, this step picks the one that best fits your data, so the choice is explicit and reproducible rather than defaulting to a familiar scheme.

Terms. A biome scheme is one of the 31 classification systems; a biome class is a category within it; a biome scheme number (1-31) identifies a scheme. The scheme_type argument groups schemes by methodology.

1. Rank the schemes for your data

biomes_rank() scores every scheme for your occurrences and proposes a single best-fitting scheme. Each scheme is rated on three complementary, data-driven criteria:

coverage: share of records that fall on a defined biome class.
effective number of classes: exp(H'), the effective number of biome classes the records occupy (rewards schemes that spread the data over several well-populated classes).
granularity: occupied biome classes divided by the total number of biome classes in the scheme.

The three criteria are min-max scaled to [0, 1] and averaged (equal weights) into a composite score. The best-scoring scheme is returned in attr(ranking, "best_scheme").

ranking <- biomes_rank(biomes_example, verbose = FALSE)
best    <- attr(ranking, "best_scheme")
best
#> [1] 16
head(ranking)
#>   scheme
#> 1      1
#> 2      2
#> 3      3
#> 4      4
#> 5      5
#> 6      6
#>                                                                                                                  scheme_name
#> 1                                                                       Global vegetation patterns of the past 140,000 years
#> 2                                                  Dataset of the global component of the Copernicus Land Monitoring Service
#> 3                                            Present and future Köppen-Geiger climate classification maps at 1-km resolution
#> 4 Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential
#> 5                                                       An ecoregion-based approach to protecting half the terrestrial realm
#> 6                                              A global classification of vegetation based on NDVI, rainfall and temperature
#>   year n_total n_hit n_na pct_na coverage_raw coverage_scaled
#> 1 2020   29104 24452 4652  15.98    0.8401594       0.2844065
#> 2 2019   29104 27587 1517   5.21    0.9478766       0.7708301
#> 3 2018   29104 28023 1081   3.71    0.9628573       0.8384794
#> 4 2018   29104 27538 1566   5.38    0.9461930       0.7632273
#> 5 2017   29104 27943 1161   3.99    0.9601086       0.8260667
#> 6 2017   29104 22619 6485  22.28    0.7771784       0.0000000
#>   effective_classes_raw effective_classes_scaled granularity_raw
#> 1             10.080182                0.6202006       0.9047619
#> 2              7.637310                0.3138203       0.8500000
#> 3             11.659664                0.8182963       0.8333333
#> 4              9.012508                0.4862950       0.9500000
#> 5              7.159880                0.2539420       1.0000000
#> 6              6.163172                0.1289367       1.0000000
#>   granularity_scaled composite_score rank is_best
#> 1          0.4285714       0.4443928   26   FALSE
#> 2          0.1000000       0.3948835   28   FALSE
#> 3          0.0000000       0.5522586   23   FALSE
#> 4          0.7000000       0.6498408   20   FALSE
#> 5          1.0000000       0.6933362   13   FALSE
#> 6          1.0000000       0.3763122   29   FALSE

The result is a data frame with one row per scheme; the key columns are scheme (the biome scheme number), scheme_name, composite_score and is_best.

Rank within a conceptually comparable group

Comparing schemes of different methodologies can mislead, so restrict the ranking to one group with scheme_type:

r_veg <- biomes_rank(biomes_example, scheme_type = "vegetation", verbose = FALSE)
attr(r_veg, "best_scheme")
#> [1] 9

table(biomes_information$scheme_type)   # how many schemes per group
#> 
#> anthropogenic       climate     ecoregion   integrative    land_cover 
#>             1             8             4             5             7 
#>    vegetation 
#>             6

scheme_type = "all" (the default) ranks all 31 schemes. Other groups are "climate", "vegetation", "land_cover", "ecoregion", "integrative" and "anthropogenic".

2. Inspect the ranking

The rank panel of biomes_visualise() shows the composite score per scheme with the best scheme highlighted:

biomes_visualise(biomes_example, panels = "rank")

Treat the ranking as a shortlist, not an authoritative answer: the most suitable scheme ultimately depends on your research question. Inspect the criterion-specific columns of the ranking and use biomes_info() to pick the scheme whose concept and resolution match your data.

The integer in attr(ranking, "best_scheme") is exactly the biome scheme number you pass as scheme to the classification and visualisation functions next.

You have a chosen biome scheme. Continue with Step 3: Occurrences-to-biome classification.

Goal

1. Rank the schemes for your data

Rank within a conceptually comparable group

2. Inspect the ranking

Next