To produce our global Forest Landscape Integrity Index (FLII), we combined four sets of spatially explicit datasets representing: (i) forest extent23; (ii) observed pressure from high impact, localized human activities for which spatial datasets exist, specifically: infrastructure, agriculture, and recent deforestation27; (iii) inferred pressure associated with edge effects27, and other diffuse processes, (e.g., activities such as hunting and selective logging)27 modeled using proximity to observed pressures; and iv) anthropogenic changes in forest connectivity due to forest loss27 (see Supplementary Table 1 for data sources). These datasets were combined to produce an index score for each forest pixel (300 m), with the highest scores reflecting the highest forest integrity (Fig. 1), and applied to forest extent for the start of 2019. We use globally consistent parameters for all elements (i.e., parameters do not vary geographically). All calculations were conducted in Google Earth Engine (GEE)60.
Forest extent
We derived a global forest extent map for 2019 by subtracting from the Global Tree Cover product for 200023 annual Tree Cover Loss 2001–2018, except for losses categorized by Curtis and colleagues24 as those likely to be temporary in nature (i.e., those due to fire, shifting cultivation and rotational forestry). We applied a canopy threshold of 20% based on related studies e.g.31,61, and resampled to 300 m resolution and used this resolution as the basis for the rest of the analysis (see Supplementary Note 1 for further methods).
Observed human pressures
We quantify observed human pressures (P) within a pixel as the weighted sum of impact of infrastructure (I; representing the combined effect of 41 types of infrastructure weighted by their estimated general relative impact on forests (Supplementary Table 3), agriculture (A) weighted by crop intensity (indicated by irrigation levels), and recent deforestation over the past 18 years (H; excluding deforestation from fire, see Discussion). Specifically, for pixel i:
$${mathrm{P}}_{mathrm{i}} = {mathrm{exp}}left( { – {upbeta}_1{mathrm{I}}_{mathrm{i}}} right) + {mathrm{exp}}left( { – {upbeta}_2{mathrm{A}}_{mathrm{i}}} right) + {mathrm{exp}}left( { – {upbeta}_3{mathrm{H}}_{mathrm{i}}} right)$$
(1)
whereby the values of β were selected so that the median of the non-zero values for each component was 0.75. This use of exponents is a way of scaling variables with non-commensurate units so that they can be combined numerically, while also ensuring that the measure of observed pressure is sensitive to change (increase or decrease) in the magnitude of any of the three components, even at large values of I, A, or H. This is an adaptation of the Human Footprint methodology62. See Supplementary Note 3 for further details.
Inferred human pressures
Inferred pressures are the diffuse effects of a set of processes for which directly observed datasets do not exist, that include microclimate and species interactions relating to the creation of forest edges63 and a variety of intermittent or transient anthropogenic pressures such as selective logging, fuelwood collection, hunting; spread of fires and invasive species, pollution, and livestock grazing64,65,66. We modeled the collective, cumulative impacts of these inferred effects through their spatial association with observed human pressure in nearby pixels, including a decline in effect intensity according to distance, and partitioning into stronger short-range and weaker long-range effects. The inferred pressure (P′) on pixel i from source pixel j is:
$$Pprime _{i,j} = P_jleft( {w_{i,j} + v_{i,j}} right)$$
(2)
where wi,j is the weighting given to the modification arising from short-range pressure, as a function of distance from the source pixel, and vi,j is the weighting given to the modification arising from long-range pressures.
Short-range effects include most of the processes listed above, which together potentially affect most biophysical features of a forest, and predominate over shorter distances. In our model, they decline exponentially, approach zero at 3 km, and are truncated to zero at 5 km (see Supplementary Note 4).
$$begin{array}{l}{mathrm{w}}_{i,j} = alpha ,{mathrm{exp}}( – lambda {mathrm{d}}_{i,j}),,,,,,[{mathrm{for}},{mathrm{d}}_{{mathrm{i,j}}} le {mathrm{5km}}] {mathrm{w}}_{i,j} = {mathrm{0}},,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,[{mathrm{for}},{mathrm{d}}_{i,j} > {mathrm{5km}}]end{array}$$
(3)
where α is a constant set to ensure that the sum of the weights across all pixels in the range is 1.85 (see below), λ is a decay constant set to a value of 1 (see67 and other references in Supplementary Note 4) and di,j is the Euclidean distance between the centers of pixels i and j expressed in units of km.
Long-range effects include over-exploitation of high socio-economic value animals and plants, changes to migration and ranging patterns, and scattered fire and pollution events. We modeled long-range effects at a uniform level at all distances below 6 km and they then decline linearly with distance, conservatively reaching zero at a radius of 12 km65,68 (and other references in Supplementary Note 4):
$$begin{array}{l}{mathrm{v}}_{i,j} = gamma ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,[for,d_{i,j} le 6km] {mathrm{v}}_{i,j} = gamma left( {12 – d_{i,j}} right)/6,,,,[{mathrm{for}},6{mathrm{km}}, < ,{mathrm{d}}_{i,j} le 12{mathrm{km}}] {mathrm{v}}_{i,j} = 0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,[for,{mathrm{d}}_{i,j} > 12{mathrm{km}}]end{array}$$
(4)
where γ is a constant set to ensure that the sum of the weights across all pixels in the range is 0.15 and di,j is the Euclidean distance between the centers of pixels i and j, expressed in kilometers.
The form of the weighting functions for short- and long-range effects and the sum of the weights (α + γ) were specified based on a hypothetical reference scenario where a straight forest edge is adjacent to a large area with uniform human pressure, and ensuring that in this case total inferred pressure immediately inside the forest edge is equal to the pressure immediately outside, before declining with distance. γ is set to 0.15 to ensure that the long-range effects conservatively contribute no more than 5% to the final index in the same scenario, based on expert opinion and supported e.g., Berzaghi et al.69 regarding the approximate level of impact on values that would be affected by severe defaunation and other long-range effects.
The aggregate effect from inferred pressures (Q) on pixel i from all n pixels within range (j = 1 to j = n) is then the sum of these individual, normalized, distance-weighted pressures, i.e.,
$$Q_i = mathop {sum}_{j=1}^{n} {P{prime}_{i,j}}$$
(5)
Loss of forest connectivity
Average connectivity of forest around a pixel was quantified using a method adapted from Beyer et al.70. The connectivity Ci around pixel i surrounded by n other pixels within the maximum radius (numbered j = 1, 2…n) is given by:
$${mathrm{C}}_i = mathop {sum}_{j=1}^{n} {left( {{mathrm{F}}_j{mathrm{G}}_{i,j}} right)}$$
(6)
where Fj is the forest extent is a binary variable indicating if forested (1) or not (0) and Gi,j is the weight assigned to the distance between pixels i and j. Gi,j uses a normalized Gaussian curve, with σ = 20 km and distribution truncated to zero at 4σ for computational convenience (see Supplementary Note 2). The large value of σ captures landscape connectivity patterns operating at a broader scale than processes captured by other data layers. Ci ranges from 0 to 1 (Ci∈[0,1]).
Current Configuration (CCi) of forest extent in pixel i was calculated using the final forest extent map and compared to the Potential Configuration (PC) of forest extent without extensive human modification, so that areas with naturally low connectivity, e.g., coasts and natural vegetation mosaics, are not penalized. PC was calculated from a modified version of the map of Laestadius et al38. and resampled to 300 m resolution (see Supplementary Note 2 for details). Using these two measures, we calculated Lost Forest Configuration (LFC) for every pixel as:
$${mathrm{LFC}}_i = 1 – left( {{mathrm{CC}}_i/{mathrm{PC}}_i} right)$$
(7)
Values of CCi/PCi > 1 are assigned a value of 1 to ensure that LFC is not sensitive to apparent increases in forest connectivity due to inaccuracy in estimated potential forest extent – low values represent least loss, high values greatest loss (LFCi∈[0,1]).
Calculating the Forest Landscape Integrity Index
The three constituent metrics, LFC, P, and Q, all represent increasingly modified conditions the larger their values become. To calculate a forest integrity index in which larger values represent less degraded conditions we, therefore, subtract the sum of those components from a fixed large value (here, 3). Three was selected as our assessment indicates that values of LFC + P + Q of 3 or more correspond to the most severely degraded areas. The metric is also rescaled to a convenient scale (0-10) by multiplying by an arbitrary constant (10/3). The FLII for forest pixel i is thus calculated as:
$${mathrm{FLII}}_i = left[ {10/3} right] (3 – {mathrm{min}}(3,,[P_i + Q_i + {mathrm{LFC}}_i]))$$
(8)
where FLIIi ranges from 0 to 10, forest areas with no modification detectable using our methods scoring 10 and those with the most scoring 0.
Illustrative forest integrity classes
Whilst a key strength of the index is its continuous nature, the results can also be categorized for a range of purposes. In this paper three illustrative classes were defined, mapped, and summarized to give an overview of broad patterns of integrity in the world’s forests. The three categories were defined as follows.
High Forest Integrity (scores ≥ 9.6) Interiors and natural edges of more or less unmodified naturally regenerated (i.e., non-planted) forest ecosystems, comprised entirely or almost entirely of native species, occurring over large areas either as continuous blocks or natural mosaics with non-forest vegetation; typically little human use other than low-intensity recreation or spiritual uses and/or low-intensity extraction of plant and animal products and/or very sparse presence of infrastructure; key ecosystem functions such as carbon storage, biodiversity, and watershed protection and resilience expected to be very close to natural levels (excluding any effects from climate change) although some declines possible in the most sensitive elements (e.g., some high value hunted species).
Medium Forest Integrity (scores > 6.0 but <9.6) Interiors and natural edges of naturally regenerated forest ecosystems in blocks smaller than their natural extent but large enough to have some core areas free from strong anthropogenic edge effects (e.g., set-asides within forestry areas, fragmented protected areas), dominated by native species but substantially modified by humans through a diversity of processes that could include fragmentation, creation of edges and proximity to infrastructure, moderate or high levels of extraction of plant and animal products, significant timber removals, scattered stand-replacement events such as swidden and/or moderate changes to fire and hydrological regimes; key ecosystem functions such as carbon storage, biodiversity, watershed protection and resilience expected to be somewhat below natural levels (excluding any effects from climate change).
Low Forest Integrity (score ≤ 6.0): Diverse range of heavily modified and often internally fragmented ecosystems dominated by trees, including (i) naturally regenerated forests, either in the interior of blocks or at edges, that have experienced multiple strong human pressures, which may include frequent stand-replacing events, sufficient to greatly simplify the structure and species composition and possibly result in significant presence of non-native species, (ii) tree plantations and, (iii) agroforests; in all cases key ecosystem functions such as carbon storage, biodiversity, watershed protection and resilience expected to be well below natural levels (excluding any effects from climate change).
The numerical category boundaries were derived by inspecting FLII scores for a wide selection of benchmark locations whose forest integrity according to the category definitions was known to the authors, see text S6 and Table S4.
Protected areas analysis
Data on protected area location, boundary, and year of the inscription were obtained from the February 2018 World Database on Protected Areas71. Following similar global studies e.g.72, we extracted protected areas from the WDPA database by selecting those areas that have a status of “designated”, “inscribed”, or “established”, and were not designated as UNESCO Man and Biosphere Reserves. We included only protected areas with detailed geographic information in the database, excluding those represented as a point only. To assess the integrity of the protected forest, we extracted all 300 m forest pixels that were at least 50% covered by a formally protected area and measured the average FLII score.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Source: Ecology - nature.com
