The natural capital $\mathcal{K}$ is the sum of the natural capital $\mathcal{K}_{i,j}$ for $n$ biomes and $m$ ecosystem services:

$$ \mathcal{K} = \sum_{i=1}^{n} \sum_{j=1}^{m} \mathcal{K}_{i,j} \qquad (5.10) $$

We can assume that the value of a biome's ecosystem service is proportional to its area:

$$ \mathcal{K}_{i,j} = A_i V_{i,j} \qquad (5.11) $$

where $A_i$ is the area of biome $i$ (in hectares) and $V_{i,j}$ is the monetary value of ecosystem service $j$ (in dollars per hectare per year). Therefore, $\mathcal{K}_{i,j}$ and $\mathcal{K}$ are expressed in dollars per year. From Equation (5.10), we deduce that:

$$ \mathcal{K} = \sum_{i=1}^{n} \mathcal{K}_{i}^{(\text{biome})} = \sum_{j=1}^{m} \mathcal{K}_{j}^{(\text{service})} \qquad (5.12) $$

where $\mathcal{K}_{i}^{(\text{biome})} = \sum_{j=1}^{m} A_i V_{i,j} = A_i \sum_{j=1}^{m} V_{i,j}$ and $\mathcal{K}_{j}^{(\text{service})} = \sum_{i=1}^{n} A_i V_{i,j}$. To calculate $\mathcal{K}_{i,j}$ or $V_{i,j}$, Costanza et al. (1997) estimated the willingness to pay of individuals for ecosystem services. For example, if a 10-hectare forest provides $500 per year in market benefits for raw materials and $250 per year in non-market benefits for aesthetic services, the total value of ecosystem services for that forest would be $750 per year or $75 ha^{-1}yr^{-1}$. In Table 5.27, we report the values of annual ecosystem services for the different biomes estimated by Costanza et al. (1997). Of the $33 tn, 63\%$ comes from marine biomes and 37% from terrestrial biomes. In the case of marine biomes, both open ocean and coastal areas make a significant contribution, the former because of its large area (33 200 ha), the latter because of its high unit value ($4 052 ha^{-1}yr^{-1}$). In the case of terrestrial biomes, forest and wetland are the two main contributors, the former because of its large area (4 855 ha), the latter because of its high unit value ($14 785 ha^{-1}yr^{-1}$). Table 5.28 shows the breakdown of the $33 tn$ in terms of the 17 ecosystem services examined by the authors. 51.3% comes from nutrient cycling, followed by cultural services at 9.1%, waste treatment at 6.8%, and disturbance regulation at 5.3%.

The previous study has been updated in 2014 using the same methodology, but with a more comprehensive approach to estimate the unit value $V_{i,j}$. In fact, the unit values in Costanza et al. (2014) are based on the TEEB valuation database developed by Van der Ploeg and de Groot (2010), which collected 1 310 value estimates from 320 publications. An analysis of this database can be found in De Groot et al. (2012), who selected 665 unit values. For example, using 2007 price levels, they estimate that the mean value of coral reefs is $352 249 ha^{-1}yr^{-1}$, while the mean value of open oceans is only $491 ha^{-1}yr^{-1}$. Other interesting figures are: $193 845 ha^{-1}yr^{-1}$ for coastal wetlands, $25 682 ha^{-1}yr^{-1}$ for coastal wetlands, $5 264 ha^{-1}yr^{-1}$ for tropical forests, $4 267 ha^{-1}yr^{-1}$ for lakes and rivers, etc. We report the calculations made by Costanza et al. (2014) for the year 2011 in Table 5.29. The authors estimated 4 total values of natural capital by considering both 1997 and 2011 figures. Using 2007 price levels$^{89}$, they obtained $45.9 tn/yr$ when considering 2011 biome areas and unit values, $41.6 tn/yr$ when considering 2011 biome areas and 1997 unit values, $145 tn/yr$ when considering 1997 biome areas and 2011 unit values, and $124.8 tn/yr$ when considering 2011 biome areas and unit values. These different figures can be used to assess changes in biome areas and the effect of updated unit values. They concluded that changes in global land use have resulted in a loss of ecosystem services ranging from $4.3 to $20.2 tn/yr.

<footnote>$^{89}$The 1997 study used 1995 prices to calculate unit values. To convert them to 2007 prices, we need to multiply them by a factor of 1.38.</footnote>
<table><caption>Table 5.28: Average global value of annual ecosystem services in 1997 (1995 price levels)</caption><thead><tr><th>#</th><th>Ecosystem service</th><th>Ecosystem functions</th><th>Value (in $ bn/yr)</th><th>Breakdown (in %)</th></tr></thead><tbody><tr><td>1</td><td>Gas regulation</td><td>Regulation of atmospheric chemical composition</td><td>1 341</td><td>4.0</td></tr><tr><td>2</td><td>Climate regulation</td><td>Regulation of global temperature, precipitation, and other biologically mediated climatic processes at global or local levels</td><td>684</td><td>2.1</td></tr><tr><td>3</td><td>Disturbance regulation</td><td>Capacitance, damping, and integrity of ecosystem response to environmental fluctuations</td><td>1 779</td><td>5.3</td></tr><tr><td>4</td><td>Water regulation</td><td>Regulation of hydrological flows</td><td>1 115</td><td>3.4</td></tr><tr><td>5</td><td>Water supply</td><td>Storage and retention of water</td><td>1 692</td><td>5.1</td></tr><tr><td>6</td><td>Erosion control and sediment retention</td><td>Retention of soil within an ecosystem</td><td>576</td><td>1.7</td></tr><tr><td>7</td><td>Soil formation</td><td>Soil formation processes</td><td>53</td><td>0.2</td></tr><tr><td>8</td><td>Nutrient cycling</td><td>Storage, internal cycling, processing, and acquisition of nutrients</td><td>17 075</td><td>51.3</td></tr><tr><td>9</td><td>Waste treatment</td><td>Recovery of mobile nutrients and removal or breakdown of excess or xenic nutrients and compounds</td><td>2 277</td><td>6.8</td></tr><tr><td>10</td><td>Pollination</td><td>Movement of floral gametes</td><td>117</td><td>0.4</td></tr><tr><td>11</td><td>Biological control</td><td>Trophic-dynamic regulations of populations</td><td>417</td><td>1.3</td></tr><tr><td>12</td><td>Refugia</td><td>Habitat for resident and transient populations</td><td>124</td><td>0.4</td></tr><tr><td>13</td><td>Food production</td><td>That portion of gross primary production extractable as food</td><td>1 386</td><td>4.2</td></tr><tr><td>14</td><td>Raw materials</td><td>That portion of gross primary production extractable as raw materials</td><td>721</td><td>2.2</td></tr><tr><td>15</td><td>Genetic resources</td><td>Sources of unique biological materials and products</td><td>79</td><td>0.2</td></tr><tr><td>16</td><td>Recreation</td><td>Providing opportunities for recreational activities</td><td>815</td><td>2.4</td></tr><tr><td>17</td><td>Cultural</td><td>Providing opportunities for non-commercial uses</td><td>3 015</td><td>9.1</td></tr></tbody><tfoot><tr><td colspan="2">Total</td><td></td><td>33 268</td><td>100.0</td></tr></tfoot></table>

Source: Costanza et al. (1997, Table 2, page 256) & Author's calculations.
<table><caption>Table 5.29: Changes in annual ecosystem services between 1997 and 2011</caption><thead><tr><th rowspan="2"></th><th colspan="2">Area<br>(in hectare)</th><th colspan="2">Unit values<br>(in $/ha/yr)</th><th colspan="4">Total value<br>(in $/yr)</th></tr><tr><th>1997</th><th>2011</th><th>2007</th><th>2007</th><th>1995</th><th>2007</th><th>2007</th><th>2007</th></tr></thead><tbody><tr><td>Price levels</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Area</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Unit values</td><td></td><td></td><td>1997</td><td>2011</td><td>1997</td><td>1997</td><td>1997</td><td>2011</td></tr><tr><td>Marine</td><td>36 302</td><td>36 302</td><td>796</td><td>1 368</td><td>20.9</td><td>28.9</td><td>29.5</td><td>60.5</td></tr><tr><td>Open Ocean</td><td>33 200</td><td>33 200</td><td>348</td><td>660</td><td>8.4</td><td>11.6</td><td>11.6</td><td>21.9</td></tr><tr><td>Coastal</td><td>3 102</td><td>3 102</td><td>5 592</td><td>8 944</td><td>12.6</td><td>17.3</td><td>18.0</td><td>38.6</td></tr><tr><td>Terrestrial</td><td>15 323</td><td>15 323</td><td>1 109</td><td>4 901</td><td>12.3</td><td>17.0</td><td>12.1</td><td>84.5</td></tr><tr><td>Forest</td><td>4 855</td><td>4 261</td><td>1 338</td><td>3 800</td><td>4.7</td><td>6.5</td><td>4.7</td><td>19.5</td></tr><tr><td>Grassland</td><td>3 898</td><td>4 418</td><td>321</td><td>4 166</td><td>0.9</td><td>1.2</td><td>1.4</td><td>16.2</td></tr><tr><td>Wetland</td><td>330</td><td>188</td><td>20 404</td><td>140 174</td><td>4.9</td><td>6.7</td><td>3.4</td><td>36.2</td></tr><tr><td>Lake & river</td><td>200</td><td>200</td><td>11 727</td><td>12 512</td><td>1.7</td><td>2.3</td><td>2.3</td><td>2.5</td></tr><tr><td>Cropland</td><td>1 400</td><td>1 672</td><td>126</td><td>5 567</td><td>0.1</td><td>0.2</td><td>0.2</td><td>7.8</td></tr><tr><td>Urban</td><td>332</td><td>352</td><td></td><td>6 661</td><td></td><td></td><td></td><td>2.2</td></tr><tr><td>Total</td><td>51 625</td><td>51 625</td><td></td><td></td><td>33.3</td><td>45.9</td><td>41.6</td><td>145.0</td></tr><tr><td>Difference</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-4.3</td><td>-20.2</td></tr></tbody></table>

Source: Costanza et al. (2014, Table 3, page 156) & Author's calculations.

<figure><img src="image_188.png" /><figcaption>Figure 5.42: World map with the different ecosystem types and the calculated values of their ecosystem services (in US dollars per hectare per year)</figcaption></figure>

Source: World Ocean Review (2015, Figure 1.12, page 27).
**Remark 56** Since 1997, the valuation of ecosystem services has been widely adopted by scientists, organizations and policy makers<fnref n="90" /> (Hein et al., 2020; Legesse et al., 2022). The following are some of the frameworks, projects and initiatives for the valuation of ecosystem services<fnref n="91" />: System of Environmental-Economic Accounting (SEEA), Common International Classification of Ecosystem Services (CICES), The Economics of Ecosystems and Biodiversity (TEEB), Natural Capital Accounting and Valuation of Ecosystem Services (NCAVES).

Box 5.10: The System of Environmental-Economic Accounting (SEEA)

The System of Environmental-Economic Accounting (SEEA), endorsed by the United Nations, is a framework that integrates economic and environmental data to provide a comprehensive view of the interactions between the economy and the environment, and the stocks and flows of environmental assets. It establishes common concepts, definitions, classifications, and accounting rules, to enable the production of internationally comparable statistics and accounts. The SEEA framework follows an accounting structure similar to the System of National Accounts (SNA), ensuring coherence with traditional economic accounting practices. The SEEA is modular, with each module addressing a specific topic. The SEEA central framework develops the rules and guidelines for measuring the relationship between the environment and the economy, focusing on three key areas: environmental flows (changes in natural assets), stocks of environmental assets (measurement of natural assets) and economic activity related to the environment (monetary economic flows related to natural assets). The four main modules are:

* Ecosystem accounting (SEEA EA) with 4 types of accounts: ecosystem extent accounts, ecosystem condition accounts, ecosystem services flow accounts (physical and monetary), and monetary ecosystem asset accounts;

* Agriculture, forestry and fisheries with two types of accounts: flow accounts and asset accounts (both physical and monetary);

* SEEA-Energy with two types of accounts: flow accounts and asset accounts (both physical and monetary);

* SEEA-Water with three types of accounts: physical flow accounts, physical asset accounts and economic accounts.

In addition, four other modules have been developed on air emissions, environmental activities, land and material flow accounts. Examples of SEEA accounts can be found at seea.un.org/content/projects.

In January 2020, the World Economic Forum published a series of reports aimed at “providing pathways for business to be part of the transition to a nature-positive economy”. In particular, World Economic Forum (2020a) estimated that $44 trillion of economic value creation — more than half of global GDP — is moderately or highly dependent on nature<fnref n="92" />. Among the 163 economic

<footnote n="90">⁹⁰World maps of ecosystem valuation, such as Figure 5.42, are very common in academic and policy publications.</footnote>

<footnote n="91">⁹¹The corresponding websites are seea.un.org, www.cices.eu, www.teebweb.org, and seea.un.org/home/natural-capital-accounting-project.</footnote>

<footnote n="92">⁹²The exact figures are $31 tn or 37% of the world GDP for moderately dependent industries and $13 tn or 15% of the world GDP for highly dependent industries.</footnote>
sectors considered in the study, construction ($4 tn), agriculture ($2.5 tn), and food and beverages ($1.4 tn) are the three largest industries most dependent on nature. The impact on secondary and tertiary sectors can also be significant through their supply chains. This publication was followed by several public studies in 2021 showing how GDP creation is strongly linked to biodiversity<sup>93</sup>. These reports consistently highlight that the link between GDP and biodiversity is currently poorly understood, while the economic and financial risks posed by biodiversity loss are enormous.

## Pollination service

Pollination is the process by which pollen is transferred from the male part of a flower (the anther) to the female part (the stigma) of the same or another flower. This is a crucial step in the reproductive cycle of many plants, as it leads to the production of seeds and fruits. There are two main types of pollination:

1. Self-pollination, in which pollen from one flower is transferred to the stigma of the same flower. For example, many crops and fruit trees are self-pollinating, including aubergines, barley, beans, cauliflower, lentils, lettuce, olives, onions, peppers, potatoes, soybeans, tomatoes, rice, and wheat;

2. Cross-pollination, when pollen is transferred from the anther of one flower to the stigma of a flower on another plant of the same species. Examples include apples, blackberries, black-currants, broccoli, carrots, cacao, daffodils, lavender, pears, plums, pumpkins, raspberries, strawberries, and tulips.

We also distinguish three methods of cross-pollination:

1. Abiotic pollination involves natural transport phenomena such as wind, water, and rain. Examples of wind-pollinated plants include conifers, grasses, and many trees.

2. Biotic pollination requires living pollinators to move pollen from one flower to another. Most pollinators are insects (ants, beetles, bees, butterflies, flies, wasps and moths), but some are vertebrates (bats, birds, e.g., honeyeaters and hummingbirds).

3. Hand pollination (also known as mechanical or human pollination) is a technique in which humans manually transfer pollen from the male to the female plant. Examples of plants that are commonly hand pollinated include corn, cucumbers, melons, vanilla, and zucchini.

These classifications are only theoretical. Most plants are pollinated in more than one way. For example, a self-pollinating species may also be pollinated by animals or by hand. Similarly, 100% of apples are not 100% animal pollinated. Olhnuud et al. (2022) showed that the animal-dependent pollination rate varies from species to species (e.g., Gala vs. Golden apple) and from region to region (e.g., Asia vs. Europe).

By analyzing data of 137 single crops and 115 commodity<sup>94</sup> crops provided by FAO, Klein et al. (2007) identified 124 major crops (57 single crops and 67 commodity crops) used for human food with significant annual production, representing 99% of total world food production. They

<footnote n="93"><sup>93</sup>We can cite Dasgupta (2021), OECD (2021) and Svartzman et al. (2021) among others.</footnote>

<footnote n="94"><sup>94</sup>A single crop refers to a specific plant species. A commodity, on the other hand, represents a group of different crop species. For instance, the fresh vegetables category as defined by the FAO includes 21 distinct crops, such as celery and rhubarb. Similarly, other commodity categories include fresh fruits (15 crops, including litchi and pawpaw), fresh tropical fruits (17 crops, including guava and passion fruit), roots and tubers (8 crops, such as yam bean and topinambur), and pulses (6 crops, including guar bean and velvet bean).</footnote>
Box 5.11: The story of the original vanilla bean

The Chinantla Forest is considered the birthplace of vanilla, and the vanilla plant, or vine, is native to Mexico. There, the vines grew and flourished without the help of humans. Wild vanilla is naturally pollinated by melipona bees and small hummingbirds found only in Mexico. Before 1850, all vanilla beans came from the forests of Mexico, and France was the number one importer of the ‘black flower’. The Aztecs, and the Mayans before them, believed that the scent of vanilla could help them communicate with the gods and had long mastered the fermentation techniques needed to cure the beans. They cultivated ‘tlilxochitl’ or black flowers so that the flavors could be combined with cocoa and coffee. In 1521, Cortés was the first European to bring the dark pods or beans back to Charles Quint. Vanilla beans first arrived in France in 1664. Later, Louis XIV fell in love with the taste of vanilla and wanted vanilla beans to be grown on the island of Réunion, then known as Bourbon Island. But until the mid-19th century, vanilla beans were still only made in Mexico. Although the technique of curing the beans was known, pollination of the flower was not. In 1836 and 1841, Charles Morren, a Belgian botanist, and Edmond Albius, a slave on Réunion Island discovered how to bypass bee pollination by manually pollinating vanilla flowers. Soon after, vanilla plants were exported by the French to plantations in Tahiti, Madagascar, Mauritius, Réunion Island, and the Comoros.

Text reproduced from www.epices-roellinger.com.

estimated that 70% of these major crops depend on animal pollination (87 crops), 23% do not rely on animal pollination (28 crops), while 7% have not been evaluated (9 crops). Despite this, in terms of global production in 2004, these three categories contributed 35%, 60%, and 5%, respectively. This is because the four largest crops (sugar cane, corn, wheat, and rice) do not require animal pollination, but represented more than 50% of global crop production in 2004. Klein et al. (2007) also created a classification of the pollination dependence of different crops. They considered a crop to be pollinator dependent if animal pollination is required to increase the quantity and/or quality of fruits or seeds directly consumed by humans. Alternatively, a crop is considered non-dependent if it is pollinated either abiotically (wind) or autogamously (self-fertilizing). They created five categories:

1. Essential
Animal pollinators are essential for most varieties, otherwise we observe a reduction in production of more than 90%, comparing experiments with and without animal pollinators;

2. High
Animal pollinators are strongly needed (40 to less than 90% reduction);

3. Modest
Animal pollinators are clearly beneficial (10 to less than 40% reduction);

4. Little
Some evidence suggests that animal pollinators are beneficial (greater than 0 to less than 10% reduction);

5. No increase
No increase in production with animal pollination.

Table 5.30 shows the classification found by Klein et al. (2007, Supplemental Material 2) (103 crops) and updated by Aizen et al. (2019, Appendix S1) (11 additional crops).
<table><caption>Table 5.30: How dependent are foods on pollinator insects?</caption><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>No dependency</td><td>Yields are not affected by pollinators</td><td></td></tr><tr><td></td><td>Cereals: barley, maize, millet, oats, rice, rye, sorghum, wheat</td><td></td></tr><tr><td></td><td>Roots and tubers: carrots, cassava, potatoes, sweet potatoes</td><td></td></tr><tr><td></td><td>Legumes including chickpeas, lentils, peas</td><td></td></tr><tr><td></td><td>Fruit and veg including bananas, grapes, lettuce, pepper, pineapples</td><td></td></tr><tr><td></td><td>Sugar crops: sugar beet, sugar cane</td><td></td></tr><tr><td></td><td>Also includes areca nuts, asparagus, broccoli, cabbages, castor oil seed, cauliflower, chicory roots, dates, garlic, hazelnuts, jojoba seeds, leeks, olives, onions, pistachios, quinoa, spinach, taro, triticale, walnuts, yams</td><td></td></tr><tr><td>Little dependency</td><td>Yield reduction of 0% to 10% without pollinators</td><td></td></tr><tr><td></td><td>Fruits and veg including lemons, limes, oranges, papayas, tomatoes</td><td></td></tr><tr><td></td><td>Oilcrops including linseed, palm oil, poppy seed, safflower seed</td><td></td></tr><tr><td></td><td>Legumes including beans (dry & green), cow peas, pigeon peas</td><td></td></tr><tr><td></td><td>Groundnuts</td><td></td></tr><tr><td></td><td>Also includes bambara beans, chillies, clementines, grapefruit, mandarins, persimmons, string beans, tangerines</td><td></td></tr><tr><td>Modest dependency</td><td>Yield reduction of 10% to 40% without pollinators</td><td></td></tr><tr><td></td><td>Oilcrops including mustard seed, rapeseed, sesame, sunflower seed</td><td></td></tr><tr><td></td><td>Soybeans</td><td></td></tr><tr><td></td><td>Fruits including currants, eggplant, figs, gooseberries, strawberries</td><td></td></tr><tr><td></td><td>Coconuts and okra</td><td></td></tr><tr><td></td><td>Coffee beans</td><td></td></tr><tr><td></td><td>Also includes broad beans, chestnut, karite nuts, seed cotton</td><td></td></tr><tr><td>High dependency</td><td>Yield reduction of 40% to 90% without pollinators</td><td></td></tr><tr><td></td><td>Fruits including apples, apricots, blueberries, cherries, cranberries, guavas, mangoes, nectarines, peaches, plums, pears, raspberries</td><td></td></tr><tr><td></td><td>Nuts including almonds, cashew nuts, kola nuts</td><td></td></tr><tr><td></td><td>Avocados</td><td></td></tr><tr><td></td><td>Also includes anise, badian, buckwheat, coriander, cucumber, fennel, nutmeg</td><td></td></tr><tr><td>Essential</td><td>Yield reduction greater than 90% without pollinators</td><td></td></tr><tr><td></td><td>Fruits including kiwi, melons, pumpkins, watermelons</td><td></td></tr><tr><td></td><td>Cocoa beans</td><td></td></tr><tr><td></td><td>Brazil nuts</td><td></td></tr><tr><td></td><td>Also includes quinces, vanilla</td><td></td></tr></tbody></table>

Source: Klein et al. (2007, Supplemental Material 2), Aizen et al. (2019, Appendix S1), https://ourworldindata.org/pollinator-dependence & icons taken from https://icons8.comicons.
Box 5.12: Who are animal pollinators?

Bees are the most important group of animal pollinators. There are an estimated 20 000 species of bees (family Apidae), but only nine species of honey bees are recognized and form the genus Apis (Apidae; Apinae; Apini; Apis). The Western honey bee (Apis mellifera) is the most common honey bee and the one most often domesticated for honey production. The other species are Apis andreniformis, Apis cerana (Asiatic honey bee), Apis dorsata (Giant honey bee), Apis florea (Little honey bee), Apis koschevnikovi, Apis laboriosa (Himalayan honey bee), Apis nigrocincta and Apis nuluensis. We generally distinguish between honey bees that are managed by humans and wild bees. There are many species in the latter category, but the most important are Bumble bees (Apidae; Apinae; Bombini), Carpenter bees (Apidae; Xylocopinae), Stingless bees (Apidae; Apinae; Meliponini), and solitary bees such as sand bees (family Andrenidae; Andreninae; Andrena) or nomad bees (Apidae; Nomadinae; Nomadini; Nomada). Bees are generally attracted to yellow (and blue) flowers.

Non-bee insects also play a significant role in global crop pollination. For instance, Rader *et al.* (2016) found that approximately 40% of visits to crop flowers are made by non-bee insects. In fact, butterflies and moths (order Lepidoptera) are the second most important group of animal pollinators. Other insects that contribute to pollination include ants (fam- ily Formicidae), beetles (order Coleoptera), and flies (order Diptera). In the case of ants, pollination typically occurs in low-growing flowers positioned close to the stem. Examples of ant-pollinated plants include small's stonecrop (Diamorpha smallii), alpine nailwort (Paronychia pulvinata), and cascade knotweed (Polygonum cascadense). Butterflies have excellent color vision, which explains why they are attracted to red, orange, yellow, blue and purple flowers. Moths, on the other hand, prefer pale or white flowers because they tend to come out at night.

The primary pollinators of cacao are tiny flies from the families of biting midges (Cer-
atopogonidae, including the genus *Forcipomyia*) and, to a lesser extent, gall midges (Ce-
cidomyiidae). These small flies, sometimes only a few millimeters long, are uniquely suited
to pollinate cacao because the reproductive parts of cacao flowers are very small (less than
2 mm) and complex, with structures that make them difficult for larger insects to access.
In addition to insect pollinators, vertebrate pollinators also participate in the reproductive
success of plants, especially tropical plants (Ratto *et al.*, 2018). Bird pollinators include
hummingbirds (*Trochilidae*), sunbirds (*Nectariniidae*), honeycreepers (*Meliphagidae*), and
some parrots. Birds are generally attracted to red, orange, and yellow flowers. Some known
bird-pollinated plants include hibiscus, eucalyptus, and some orchids. Bats, like moths,
prefer pale or white flowers. They are involved in the pollination of mango, banana,
durian, guava, and agave (used to make tequila). While rodents and reptiles are less
common vertebrate pollinators, they can still contribute to the reproduction of certain
plants.

It is very difficult to estimate the contribution of each species to pollination. However, it
is generally accepted that managed honey bees provide 50-75% of agricultural pollination,
while wild bees and other pollinators account for the remaining 25-50% (Garibaldi *et al.*,
2013; IPBES, 2016).

Source: www.fs.usda.gov/managing-land/wildflowers/pollinators/who-are-the-pollinators &
www.cacaopollination.com/cacao-pollinators.
Using the previous classification, Aizen et al. (2009) estimated that the direct reduction in total agricultural production in the absence of animal pollination would be 5% for developed regions and 8% for developing regions. These figures are based on the production deficit in volume, calculated as:

$$D_t^{(\text{volume})} = \frac{\sum_{i=1}^{n} P_{i,t} - \sum_{i=1}^{n} P_{i,t} (1 - \delta_i)}{\sum_{i=1}^{n} P_{i,t}} = \frac{\sum_{i=1}^{n} P_{i,t} \delta_i}{\sum_{i=1}^{n} P_{i,t}} = \sum_{i=1}^{n} w_{i,t} \delta_i \quad (5.13)$$

where $n$ is the number of crops, $P_{i,t}$ is the volume production in metric tonnes of crop $i$ in year $t$, $w_{i,t}$ is the weight of crop $i$ in the total agricultural production, $\delta_i$ is the dependency rate, which is equal to 0% for crops that are not dependent on animal pollinators and 100% for crops that are completely dependent on animal pollinators<fnref n="95" />. Aizen et al. (2009) also calculated the percentage increase in area needed to compensate for the production deficit:

$$A_t^{(\text{compensation})} = \frac{\sum_{i=1}^{n} \frac{A_{i,t}}{1 - \delta_i} - \sum_{i=1}^{n} A_{i,t}}{\sum_{i=1}^{n} A_{i,t}} = \frac{\sum_{i=1}^{n} A_{i,t} \frac{\delta_i}{1 - \delta_i}}{\sum_{i=1}^{n} A_{i,t}} = \sum_{i=1}^{n} w_{i,t} \frac{\delta_i}{1 - \delta_i} \quad (5.14)$$

where $A_{i,t}$ is the cultivated area (in hectares) of crop $i$ in year $t$ and $(1 - \delta_i)^{-1} A_{i,t}$ is the area needed to produce $P_{i,t}$ in the absence of pollination<fnref n="96" />. They found that area compensation is 15% in developed regions and 42% in developing regions. Another interesting finding is that production deficit and area compensation have generally increased over time. These different results are mainly explained by the fact that human diets have changed over time and that human diets today are more diversified than fifty years ago, with more vegetables and fruits being consumed. In addition, the low figures for production deficit are mainly due to the importance of cereals in the human diet, while the high figures for area compensation are mainly due to the greater area needed to cultivate crops that depend on animal pollination, e.g., apples, avocados, cocoa, mangoes, peaches. In terms of volume and metric tonnes, the production yield of the first category is higher than that of the second. However, it's worth considering whether a volume-based analysis is the most appropriate approach to assess the importance of pollinators. For example, 1 kg of wheat and 1 kg of tomatoes provide different nutritional values, suggesting that volume alone may not accurately reflect their contribution to human well-being. Eilers et al. (2011) conducted a similar analysis using the same data and methodology but focused on the nutritional value of crops instead of volume. Let $V_{j,t}^{(\text{nutritional})}$ be the total amount of nutrient $j$ in year $t$. We have:

$$V_{j,t}^{(\text{nutritional})} = \sum_{i=1}^{n} V_{i,j}^{(\text{nutritional})} P_{i,t} (1 - R_i) \quad (5.15)$$

where $V_{i,j}^{(\text{nutritional})}$ is the amount of nutrient $j$ in a metric tonne of crop $i$, $P_{i,t}$ is the volume production in tonnes of crop $i$ in year $t$, and $R_i$ is the proportion of crop $i$ that is not consumed by humans due to inedible parts, such as pits, stems, or shells. We can decompose $V_{j,t}^{(\text{nutritional})}$ into three components: $V_{j,t}^{(1)} = \sum_{i=1}^{n} 1 \{\delta_i = 0\} \cdot V_{i,j}^{(\text{nutritional})} P_{i,t} (1 - R_i)$ is the nutritional value of pollinator-independent crops; $V_{j,t}^{(2)} = \sum_{i=1}^{n} 1 \{\delta_i > 0\} \cdot (1 - \delta_i) V_{i,j}^{(\text{nutritional})} P_{i,t} (1 - R_i)$ is the nutritional value of pollinator-dependent crops due to abiotic and self-pollination; $V_{j,t}^{(3)} = \sum_{i=1}^{n} 1 \{\delta_i > 0\} \cdot \delta_i V_{i,j}^{(\text{nutritional})} P_{i,t} (1 - R_i)$ is the nutritional value of pollinator-dependent crops attributed to animal pollination alone. Results are shown in Table 5.31, where the key metric to

<footnote n="95">⁹⁵Aizen et al. (2009) used the following values for $\delta_i$: 0%, 5%, 25%, 65% and 95% for no, little, modest, high and essential dependency classes.</footnote>

<footnote n="96">⁹⁶We assume that production is proportional to area: $P_{i,t} \propto A_{i,t}$.</footnote>
consider is $V_{j,t}^{(3)}$ (last column). In terms of energy (calories) and protein, about 80% of the human diet comes from pollinator-independent crop production. The proportion attributed to crop production dependent on animal pollination is low, at less than 3%. Similar figures are observed for minerals, except for fluoride and calcium, where the proportions attributed to animal pollination are 19.83% and 9.11%, respectively. The most significant effects of animal pollination are observed for vitamins<fnref n="97" />, especially vitamin A (41.03%), carotene (about 40%), certain forms of vitamin E (about 20%) and vitamin C (19.64%). We conclude that animal pollination has a significant impact on human diets by contributing to nutritional diversification, particularly at the vitamin level.

<table><caption>Table 5.31: Proportion in % of nutrients derived from pollinator-independent and pollinator-dependent crops</caption><thead><tr><th>Nutrient</th><th></th><th>V<sub>j,t</sub><sup>(1)</sup></th><th>V<sub>j,t</sub><sup>(2)</sup></th><th>V<sub>j,t</sub><sup>(3)</sup></th></tr></thead><tbody><tr><td rowspan="3">Macro-nutrients</td><td>Energy</td><td>78.83</td><td>18.59</td><td>2.58</td></tr><tr><td>Protein</td><td>83.43</td><td>13.57</td><td>3.00</td></tr><tr><td>Fat</td><td>26.02</td><td>66.98</td><td>7.00</td></tr><tr><td rowspan="15">Vitamins</td><td>A</td><td>28.71</td><td>30.26</td><td>41.03</td></tr><tr><td>β-carotene</td><td>27.44</td><td>34.19</td><td>38.37</td></tr><tr><td>α-carotene</td><td>32.25</td><td>29.83</td><td>37.92</td></tr><tr><td>β-cryptoxanthin (carotene)</td><td>0.77</td><td>56.99</td><td>42.24</td></tr><tr><td>Lycopene (carotene)</td><td>0.00</td><td>56.67</td><td>43.33</td></tr><tr><td>Lutein & Zeaxanthin (carotene)</td><td>94.05</td><td>3.92</td><td>2.03</td></tr><tr><td>E (α-tocopherol)</td><td>63.73</td><td>28.94</td><td>7.33</td></tr><tr><td>E (β-tocopherol)</td><td>0.63</td><td>72.50</td><td>26.87</td></tr><tr><td>E (γ-tocopherol)</td><td>32.92</td><td>52.66</td><td>14.42</td></tr><tr><td>E (δ-tocopherol)</td><td>14.87</td><td>62.50</td><td>22.63</td></tr><tr><td>K</td><td>71.55</td><td>19.28</td><td>9.17</td></tr><tr><td>C</td><td>6.99</td><td>73.37</td><td>19.64</td></tr><tr><td>B1 (Thiamin)</td><td>95.29</td><td>4.00</td><td>0.71</td></tr><tr><td>B2 (Riboflavin)</td><td>97.66</td><td>1.92</td><td>0.42</td></tr><tr><td>B3 (Niacin)</td><td>89.46</td><td>8.93</td><td>1.61</td></tr><tr><td>B5 (Pantothenic acid)</td><td>87.57</td><td>9.34</td><td>3.09</td></tr><tr><td>B6</td><td>97.93</td><td>1.58</td><td>0.49</td></tr><tr><td>B9 (Folate)</td><td>55.49</td><td>37.19</td><td>7.32</td></tr><tr><td rowspan="12">Minerals</td><td>Calcium</td><td>42.40</td><td>48.49</td><td>9.11</td></tr><tr><td>Iron</td><td>70.66</td><td>23.14</td><td>6.20</td></tr><tr><td>Magnesium</td><td>88.50</td><td>9.06</td><td>2.44</td></tr><tr><td>Phosphorus</td><td>89.06</td><td>8.72</td><td>2.22</td></tr><tr><td>Potassium</td><td>72.74</td><td>20.93</td><td>6.33</td></tr><tr><td>Sodium</td><td>87.18</td><td>8.63</td><td>4.19</td></tr><tr><td>Zinc</td><td>91.80</td><td>6.54</td><td>1.66</td></tr><tr><td>Copper</td><td>80.92</td><td>15.21</td><td>3.87</td></tr><tr><td>Manganese</td><td>93.87</td><td>4.94</td><td>1.19</td></tr><tr><td>Selenium</td><td>97.46</td><td>1.97</td><td>0.57</td></tr><tr><td>Fluoride</td><td>45.57</td><td>34.60</td><td>19.83</td></tr></tbody></table>

Source: Eilers et al. (2011, Table 1, page 2011).

<footnote n="97">⁹⁷Vitamin A is essential for vision, vitamin C is a powerful antioxidant and vital for collagen production, vitamin E plays a role in immune function, and carotenes act as precursors to vitamin A.</footnote>
Another interesting figure is that 308 000 out of a total of 352 000 flowering plants are partially or totally pollinated by animals, which represents a proportion of 87.5% (Ollerton et al., 2011). These species, known as angiosperms (*Angiosperma*), include many fruit trees and flowers such as roses and tulips. While the primary benefits of these species are food and medicine, they also provide secondary benefits such as reducing pest populations and improving soil and water quality. Another important service is the enhancement of rural aesthetics and the provision of cultural and visual amenities (Wratten et al., 2012; Dicks et al., 2021). Without bees and butterflies, the world would not be the same, and many blue, orange, pink, purple, red, violet and yellow flowers would disappear, as these pollinators are essential for their reproduction. The loss of these vibrant flowers would have a significant impact on the beauty of natural landscapes.

<figure><img src="image_189.png" /><figcaption>Figure 5.43: Trend in the number of bee colonies (1961–2022)</figcaption></figure>

Source: www.fao.org/faostat/en/#data/QCL & Author's calculations.

Potts et al. (2010) alerted the scientific community and policy makers to the global decline of pollinators and its potential impact on human well-being:

> “Pollinators are a key component of global biodiversity, providing vital ecosystem services to crops and wild plants. There is clear evidence of recent declines in both wild and domesticated pollinators, and parallel declines in the plants that rely upon them.” (Potts et al., 2010, page 345).

It is challenging to accurately define pollinator decline, as data can sometimes initially be contradictory. For example, Potts et al. (2010) reported declines in honey bee populations in the United States (59% loss of colonies between 1947 and 2005) and Central Europe (25% loss of colonies between 1985 and 2005). Globally, however, the number of managed honey bee colonies has increased over the past six decades. From 1965 to 2015, production of pollinator-dependent crops has increased by 300% (IPBES, 2016, page XXI). This increase is mainly due to the growing presence
<table><caption>Table 5.32: Regional distribution of managed honey bee colonies (in millions)</caption><thead><tr><th rowspan="2">Region</th><th colspan="4">Stock (in million colonies)</th><th colspan="2">Growth (in %)</th></tr><tr><th>1961</th><th>1980</th><th>2000</th><th>2022</th><th>1961-2022</th><th>2000-2022</th></tr></thead><tbody><tr><td>Europe</td><td>21.10</td><td>21.42</td><td>15.55</td><td>25.12</td><td>19.1</td><td>61.6</td></tr><tr><td>Western Europe</td><td>3.76</td><td>3.35</td><td>2.45</td><td>3.55</td><td>-5.5</td><td>45.3</td></tr><tr><td>Northern Europe</td><td>0.44</td><td>0.40</td><td>0.27</td><td>0.64</td><td>45.6</td><td>138.3</td></tr><tr><td>Eastern Europe</td><td>14.02</td><td>13.71</td><td>7.36</td><td>10.66</td><td>-24.0</td><td>44.8</td></tr><tr><td>Southern Europe</td><td>2.87</td><td>3.95</td><td>5.47</td><td>10.27</td><td>257.3</td><td>87.6</td></tr><tr><td>Americas</td><td>10.02</td><td>10.03</td><td>10.62</td><td>11.71</td><td>16.9</td><td>10.2</td></tr><tr><td>Northern America</td><td>5.85</td><td>4.75</td><td>3.22</td><td>3.40</td><td>-41.9</td><td>5.5</td></tr><tr><td>Central America</td><td>2.26</td><td>2.80</td><td>2.19</td><td>2.68</td><td>18.5</td><td>22.3</td></tr><tr><td>Caribbean</td><td>0.23</td><td>0.32</td><td>0.28</td><td>0.40</td><td>72.5</td><td>45.8</td></tr><tr><td>South America</td><td>1.67</td><td>2.16</td><td>4.94</td><td>5.23</td><td>212.4</td><td>6.0</td></tr><tr><td>Africa</td><td>6.85</td><td>9.37</td><td>15.92</td><td>17.46</td><td>155.1</td><td>9.7</td></tr><tr><td>Asia</td><td>10.70</td><td>18.61</td><td>26.82</td><td>45.34</td><td>323.6</td><td>69.1</td></tr><tr><td>Oceania</td><td>0.51</td><td>0.76</td><td>0.80</td><td>1.36</td><td>168.7</td><td>70.9</td></tr><tr><td>World</td><td>49.17</td><td>60.20</td><td>69.71</td><td>101.00</td><td>105.4</td><td>44.9</td></tr></tbody></table>

Source: www.fao.org/faostat/en/#data/QCL & Author's calculations.

of fruits in human diets (such as tropical and seasonal fruits). However, many pollinator species are listed as vulnerable, endangered or critically endangered on the IUCN Red List. In fact, we need to distinguish between managed honey bees and other animal pollinators. According to FAO statistics<fnref n="98" />, the number of managed bee colonies has doubled between 1961 and 2022 (Figure 5.43). In 1961, there were 49.2 million colonies, while today there are more than 100 million (Table 5.32). Most of this growth has occurred in Asia (+324%), Africa (+155%), Southern Europe (+257%) and South America (+212%). Below is a regional breakdown of colony numbers:

<table><thead><tr><th>Year</th><th>Europe</th><th>America</th><th>Africa</th><th>Asia</th><th>Oceania</th></tr></thead><tbody><tr><td>1961</td><td>42.9%</td><td>20.4%</td><td>13.9%</td><td>21.8%</td><td>1.0%</td></tr><tr><td>2022</td><td>24.9%</td><td>11.6%</td><td>17.3%</td><td>44.9%</td><td>1.3%</td></tr></tbody></table>

Asia now accounts for 45% of the global market, followed by Europe with 25%. Three regions have experienced a decline in honey bee populations over the last sixty years: North America (−41.9%), Eastern Europe (−24.0%), and Western Europe (−5.5%). However, in the past 20 years, they have reversed this trend, showing positive growth. The current problem is not the decline of honey bee colonies, but the dramatic decline of insects worldwide. For example, Hallmann et al. (2017) measured a decline in total flying insect biomass of more than 75 percent over 27 years in 63 German protected areas. Sánchez-Bayo and Wyckhuys (2019) estimate that 40% of the world's insect species could become extinct in the next few decades. The case of butterflies, for example, is dramatic, as shown by Warren et al. (2021), who studied the status of butterflies in the United Kingdom, the Netherlands, and Belgium:

> "[...] In the United Kingdom, 8% of resident species have become extinct, and since 1976 overall numbers declined by around 50%. In the Netherlands, 20% of species have become extinct, and since 1990 overall numbers in the country declined by 50%. Distribution

<footnote n="98">⁹⁸The data can be obtained from the FAO website: www.fao.org/faostat/en/#data/QCL. Select the domain *Crops and livestock products*, then the item *Live Animals/Bees* and the element *Stocks*.</footnote>
trends showed that butterfly distributions began decreasing long ago, and between 1890 and 1940, distributions declined by 80%. In Flanders (Belgium), 20 butterflies have become extinct (29%), and between 1992 and 2007 overall numbers declined by around 30%. A European Grassland Butterfly Indicator from 16 European countries shows there has been a 39% decline of grassland butterflies since 1990. The 2010 Red List of European butterflies listed 38 of the 482 European species (8%) as threatened and 44 species (10%) as near threatened [...]." (Warren et al., 2021, page 1).

According to Butterfly Conservation<sup>99</sup>, 80% of butterfly species have declined in abundance or distribution since the 1970s. In the United States, the situation is even worse than in Europe, where some butterfly species have experienced declines of up to 2% per year in the recent period (Thogmartin et al., 2017; Wepprich et al., 2019). In fact, the decline of wild bees and other pollinators cannot be compensated by an increase in honeybees because they play different roles in crop pollination and do not visit plants in the same way or at the same time (Rader et al., 2016). Therefore, crop yield and quality depend on pollinator biodiversity.

The drivers and risks associated with pollinator decline have been extensively reviewed in the 2016 report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). A summary can be found in Potts et al. (2016). According to IPBES (2016, Chapter 2, pages 30–149), there are five main drivers of change in pollination networks:

1. Land use and its change ("It is well established that habitat loss and degradation [...] affect pollinator diversity, abundance and richness. These changes can negatively affect community stability, pollination networks and the survival and evolutionary potential of pollinator and plant species.")

2. Pesticides, GMOs, veterinary medicines and pollutants ("It is clear that pollinators may be exposed to a wide range of pesticides in both agricultural and urban environments. The risk posed by pesticides is driven by a combination of the toxicity (hazard) and the level of exposure; [...] Insecticides are toxic to insect pollinators and their exposure, and thus the risk posed, is increased if, for example, labels do not provide use information to minimise pollinator exposure or the label is not complied with by the pesticide applicator.")

3. Pollinator diseases and pollinator management ("Bee management is a global and complex driver of pollinator loss. Spreading of diseases by managed honey bees and bumble bees into wild bee species has been shown to present a threat to some wild species and populations.")

4. Invasive alien species ("Invasive predators can directly kill pollinators or disrupt pollinator communities and associated pollination systems, whilst invasive pollinators can outcompete or transmit diseases to native pollinator species or simply be accommodated in the existing pollinator assemblage.")

5. Climate change ("Many plant and pollinator species have moved their ranges, altered their abundance, and shifted their seasonal activities in response to observed climate change over recent decades.")

Using this classification, Dicks et al. (2021) conducted a formal expert elicitation process to assess the regional and global importance of these drivers. Results are presented in Table 5.33. Globally, land cover and management are the most important drivers of pollinator decline, followed by pesticide

<footnote n="99">⁹⁹See the latest report *The State of the UK's Butterflies 2022*, available at www.butterfly-conservation.org/state-of-uk-butterflies-2022.</footnote>
<table><caption>Table 5.33: Assessment of the importance of the top eight drivers of pollinator decline</caption><thead><tr><th>Drivers</th><th>Africa</th><th>Asia Pacific</th><th>Australasia</th><th>Europe</th><th>Latin America</th><th>North America</th><th>Global</th></tr></thead><tbody><tr><td>Land cover</td><td>○</td><td>●</td><td>○</td><td>●</td><td>●</td><td>○</td><td>●</td></tr><tr><td>Land management</td><td>●</td><td>●</td><td>○</td><td>●</td><td>●</td><td>●</td><td>●</td></tr><tr><td>Pesticide use</td><td>○</td><td>●</td><td>○</td><td>○</td><td>●</td><td>○</td><td>○</td></tr><tr><td>Climate change</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Pests and pathogens</td><td>●</td><td>○</td><td>○</td><td>●</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Pollinator management</td><td>●</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Invasive alien species</td><td>●</td><td>○</td><td>○</td><td>●</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Genetically modified crops</td><td>●</td><td>○</td><td>○</td><td>○</td><td>●</td><td>○</td><td>○</td></tr></tbody></table>

The circle size is proportional to the importance score between 1 (not important) and 4 (the most important). The circle color corresponds to the confidence score: ● = well established, ○ = established but incomplete, and ○ = inconclusive.

Source: Dicks et al. (2021, Figure 2, page 1457 & Supplementary Table 2).

use. Climate change, pests and pathogens, pollinator management and invasive alien species are also important, but to a lesser extent, and contribute equally. GMOs are the least important driver. However, there are some strong differences between regions. Pesticide use is very important in Asia Pacific and Latin America. In Europe and North America, land management is the most important issue. The impact of these changes can be divided into two main categories:

* Impact on food production (pollination deficits, yield instability, honey production, food security, wild fruit availability, managed pollinators)

* Impact on biocultural diversity (wild pollinator and plant diversity, aesthetic and cultural values)

Although we have some estimates of these risks, it is currently difficult to predict the future trajectory of pollination ecosystem services and the consequences for human quality of life. The conclusion of Dicks et al. (2021) summarized perfectly what science can say about this issue: “Despite extensive research on pollinator decline, our analysis reveals considerable scientific uncertainty about what this means for human society.” Regarding the impact on food production, the future is not entirely bleak, especially with the potential for hand pollination and advancements in robotic pollination. The impact on biocultural diversity is more complex and harder to address.
## Food and feed service

There are several ways to look at food and feed<sup>100</sup> in the context of biodiversity. Food biodiversity refers to the diversity of plants, animals and other organisms used for human consumption. It can be analyzed from two main perspectives. From a production perspective, food biodiversity describes the supply of food in terms of volume (e.g., tonnes produced). From a consumption perspective, it describes the demand for food in terms of nutrient intake, such as calories, proteins, vitamins and minerals. Additional approaches can be explored, such as comparing farmed foods to wild foods, examining the diversity of local versus global food systems, or exploring sustainable versus conventional agricultural practices.

The main source of food is agriculture, which involves food production managed by humans. Historically, wild foods were the sole component of human diets. Today, the proportion of food production attributed to wild foods is relatively small compared to agricultural sources, although it continues to play an important role in certain communities, particularly for indigenous peoples, in certain regions, and in some rural areas<sup>101</sup>. Therefore, in the following we will only consider non-wild foods for which data have been collected by the FAO since 1961 and are freely available for more than 245 countries.

**Food production** World production of primary crops reached 9.61 billion tonnes in 2022 (Table 5.34). Since 1961, this figure has been multiplied by a factor of 3.78, representing an annual growth rate of 2.2% over the past sixty years. Cereals and sugar crops account for 31.8% and 22.7% of global production, respectively, while fruits, roots and tubers, and vegetables each contribute approximately 10%. In Table 5.34 we have also reported the top 20 crops by production. We see that sugar cane has the largest share, accounting for 20% of global primary crop production. In second place is maize with 12.11%. This is followed by wheat (8.41%), rice (8.08%), and oil palm (4.42%). The top five crops account for more than 50% of total production, the top 10 for about 70%, and the top 20 for more than 80%. This indicates a high concentration of production in a small number of crops. In Figure 5.44, we present the Lorenz curve for the 162 crops in the FAO database. The graph confirms the previous findings and highlights the high concentration, as 10% of the crops account for 80% of the total production volume. Moreover, this lack of diversity is the same in 2022 as it was in 1961, but this does not mean that the structure of crop production has not changed. In fact, we observe that the market shares are very different between 1961 and 2022. For example, potatoes represented 10.7% of total production in 1961, but only 3.9% today. The shares of corn, oil palm fruits, soybeans, onions, cucumbers, yams, and rapeseed have increased significantly, while the shares of sugar beets, barley, and sweet potatoes have experienced notable declines. Figure 5.45 illustrates the evolution of harvested area over the past sixty years. Between 1961 and 2022, cropland expanded by 51.6%, though this growth was uneven. The period before 2000 saw an annual growth rate of 0.5%, while after 2000, this rate accelerated to 1% per year. As a result, the overall 278% increase in crop production during this period was largely due to productivity gains and shifts in production structure. In Table 5.34, we report crop yield, which is defined as the ratio between production (expressed in tonnes) and harvested area (expressed in hectares). Yield has increased for all crops, on average by a factor of 2.49. We notice that fruits have a better yield than staples. All these factors explain the dynamics of world crop production.

<footnote n="100"><sup>100</sup>Food is for human consumption, while feed is for animals.</footnote>

<footnote n="101"><sup>101</sup>In the early 1990s, it was estimated to be less than 10% worldwide. However, reliable data are lacking. In the European Union, according to Schulp et al. (2014), the amount of wild foods collected is very small compared to total food, and the economic and nutritional value of wild foods is a few thousandths of the total consumption. However, wild foods provide an important cultural ecosystem service, as a wide variety of game (38 species), mushrooms (27</footnote>
<table><caption>Table 5.34: World production of primary crops</caption><thead><tr><th rowspan="2">Crop</th><th colspan="4">Production (in billion tonnes)</th><th colspan="2">1961 breakdown</th><th colspan="2">2022 breakdown</th><th colspan="2">Yield (in t/ha)</th></tr><tr><th>1961</th><th>1980</th><th>2000</th><th>2020</th><th>2022</th><th>(in %)</th><th>(% cum.)</th><th>(in %)</th><th>(% cum.)</th><th>1961</th><th>2022</th></tr></thead><tbody><tr><td>Primary crops</td><td>2.54</td><td>4.02</td><td>6.14</td><td>9.38</td><td>9.61</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>2.61</td><td>6.49</td></tr><tr><td>Cereals</td><td>0.88</td><td>1.55</td><td>2.06</td><td>3.00</td><td>3.06</td><td>34.58</td><td>31.84</td><td></td><td></td><td>1.35</td><td>4.18</td></tr><tr><td>Fruit</td><td>0.22</td><td>0.40</td><td>0.68</td><td>1.07</td><td>1.10</td><td>8.87</td><td>11.44</td><td></td><td></td><td>7.62</td><td>14.09</td></tr><tr><td>Oil crops</td><td>0.07</td><td>0.15</td><td>0.32</td><td>0.66</td><td>0.68</td><td>2.90</td><td>7.06</td><td></td><td></td><td>0.33</td><td>1.01</td></tr><tr><td>Pulses, Roots and Tubers</td><td>0.50</td><td>0.56</td><td>0.75</td><td>0.96</td><td>1.00</td><td>19.56</td><td>10.44</td><td></td><td></td><td>4.45</td><td>6.02</td></tr><tr><td>Sugar crops</td><td>0.61</td><td>1.00</td><td>1.50</td><td>2.13</td><td>2.18</td><td>24.02</td><td>22.73</td><td></td><td></td><td>38.16</td><td>71.61</td></tr><tr><td>Vegetables</td><td>0.20</td><td>0.29</td><td>0.69</td><td>1.15</td><td>1.17</td><td>7.79</td><td>12.21</td><td></td><td></td><td>9.34</td><td>20.13</td></tr><tr><td>Other</td><td>0.06</td><td>0.06</td><td>0.15</td><td>0.41</td><td>0.41</td><td>2.28</td><td>4.29</td><td></td><td></td><td></td><td></td></tr><tr><td>Sugar cane</td><td>0.45</td><td>0.73</td><td>1.25</td><td>1.88</td><td>1.92</td><td>17.66</td><td>17.66</td><td>20.00</td><td>20.00</td><td>50.27</td><td>73.67</td></tr><tr><td>Maize (corn)</td><td>0.21</td><td>0.40</td><td>0.59</td><td>1.16</td><td>1.16</td><td>8.09</td><td>25.75</td><td>12.11</td><td>32.11</td><td>1.94</td><td>5.72</td></tr><tr><td>Wheat</td><td>0.22</td><td>0.44</td><td>0.59</td><td>0.76</td><td>0.81</td><td>8.77</td><td>34.52</td><td>8.41</td><td>40.52</td><td>1.09</td><td>3.69</td></tr><tr><td>Rice</td><td>0.22</td><td>0.40</td><td>0.60</td><td>0.77</td><td>0.78</td><td>8.50</td><td>43.02</td><td>8.08</td><td>48.60</td><td>1.87</td><td>4.70</td></tr><tr><td>Oil palm fruit</td><td>0.01</td><td>0.03</td><td>0.12</td><td>0.42</td><td>0.42</td><td>0.54</td><td>43.56</td><td>4.42</td><td>53.02</td><td>3.77</td><td>14.15</td></tr><tr><td>Potatoes</td><td>0.27</td><td>0.24</td><td>0.32</td><td>0.37</td><td>0.37</td><td>10.67</td><td>54.23</td><td>3.90</td><td>56.92</td><td>12.22</td><td>21.07</td></tr><tr><td>Soya beans</td><td>0.03</td><td>0.08</td><td>0.16</td><td>0.36</td><td>0.35</td><td>1.06</td><td>55.29</td><td>3.63</td><td>60.55</td><td>1.13</td><td>2.61</td></tr><tr><td>Cassava, fresh</td><td>0.07</td><td>0.12</td><td>0.18</td><td>0.31</td><td>0.33</td><td>2.81</td><td>58.10</td><td>3.44</td><td>63.99</td><td>7.40</td><td>10.31</td></tr><tr><td>Other vegetables, fresh n.e.c.</td><td>0.06</td><td>0.09</td><td>0.21</td><td>0.29</td><td>0.30</td><td>2.46</td><td>60.55</td><td>3.10</td><td>67.09</td><td>8.42</td><td>14.53</td></tr><tr><td>Sugar beet</td><td>0.16</td><td>0.27</td><td>0.25</td><td>0.25</td><td>0.26</td><td>6.33</td><td>66.88</td><td>2.72</td><td>69.81</td><td>23.17</td><td>60.77</td></tr><tr><td>Tomatoes</td><td>0.03</td><td>0.05</td><td>0.11</td><td>0.19</td><td>0.19</td><td>1.09</td><td>67.97</td><td>1.94</td><td>71.74</td><td>16.43</td><td>37.84</td></tr><tr><td>Barley</td><td>0.07</td><td>0.16</td><td>0.13</td><td>0.16</td><td>0.15</td><td>2.86</td><td>70.83</td><td>1.61</td><td>73.36</td><td>1.33</td><td>3.29</td></tr><tr><td>Bananas</td><td>0.02</td><td>0.04</td><td>0.07</td><td>0.13</td><td>0.14</td><td>0.88</td><td>71.71</td><td>1.41</td><td>74.76</td><td>10.65</td><td>22.75</td></tr><tr><td>Onions and shallots</td><td>0.01</td><td>0.02</td><td>0.05</td><td>0.11</td><td>0.11</td><td>0.55</td><td>72.26</td><td>1.15</td><td>75.91</td><td>11.68</td><td>18.54</td></tr><tr><td>Watermelons</td><td>0.02</td><td>0.03</td><td>0.08</td><td>0.10</td><td>0.10</td><td>0.70</td><td>72.96</td><td>1.04</td><td>76.95</td><td>9.13</td><td>34.27</td></tr><tr><td>Apples</td><td>0.02</td><td>0.03</td><td>0.06</td><td>0.09</td><td>0.10</td><td>0.67</td><td>73.64</td><td>1.00</td><td>77.95</td><td>9.91</td><td>19.86</td></tr><tr><td>Cucumbers and gherkins</td><td>0.01</td><td>0.01</td><td>0.04</td><td>0.09</td><td>0.09</td><td>0.38</td><td>74.01</td><td>0.99</td><td>78.94</td><td>9.43</td><td>43.56</td></tr><tr><td>Yams</td><td>0.01</td><td>0.01</td><td>0.04</td><td>0.08</td><td>0.09</td><td>0.33</td><td>74.34</td><td>0.92</td><td>79.85</td><td>7.23</td><td>8.49</td></tr><tr><td>Rape or colza seed</td><td>0.00</td><td>0.01</td><td>0.04</td><td>0.07</td><td>0.09</td><td>0.14</td><td>74.48</td><td>0.91</td><td>80.76</td><td>0.57</td><td>2.18</td></tr><tr><td>Sweet potatoes</td><td>0.10</td><td>0.14</td><td>0.14</td><td>0.09</td><td>0.09</td><td>3.87</td><td>78.36</td><td>0.90</td><td>81.66</td><td>7.35</td><td>11.92</td></tr></tbody></table>

Source: www.fao.org/faostat/en/#data/QCL & Author's calculations.
<figure><img src="image_190.png" /><figcaption>Figure 5.44: Lorenz curve of world crop production</figcaption></figure>

Source: www.fao.org/faostat/en/#data/QCL & Author's calculations.

<figure><img src="image_191.png" /><figcaption>Figure 5.45: Harvested area for crop production</figcaption></figure>

Source: www.fao.org/faostat/en/#data/QCL & Author's calculations.
We now look at livestock production (Table 5.35). Like crops, livestock has experienced remarkable growth: +1456% for poultry meat, +517% for eggs, +395% for pork meat, etc. However, the magnitude of the production volume is not the same. In fact, we get a total of less than 1.4 billion tonnes, smaller compared to the 9.6 billion tonnes of crop production.

<table><caption>Table 5.35: World production of primary livestock (in million tonnes)</caption><thead><tr><th>Livestock</th><th>1961</th><th>1970</th><th>1980</th><th>1990</th><th>2000</th><th>2010</th><th>2020</th><th>2021</th><th>2022</th><th>Growth</th></tr></thead><tbody><tr><td>Eggs</td><td>15</td><td>20</td><td>27</td><td>37</td><td>55</td><td>69</td><td>93</td><td>93</td><td>93</td><td>517%</td></tr><tr><td>Milk</td><td>344</td><td>392</td><td>466</td><td>542</td><td>579</td><td>725</td><td>921</td><td>941</td><td>930</td><td>170%</td></tr><tr><td>Meat</td><td>71</td><td>101</td><td>137</td><td>180</td><td>232</td><td>294</td><td>339</td><td>355</td><td>361</td><td>405%</td></tr><tr><td>Beef &amp; Buffalo</td><td>29</td><td>40</td><td>47</td><td>55</td><td>58</td><td>67</td><td>74</td><td>75</td><td>76</td><td>165%</td></tr><tr><td>Pork</td><td>25</td><td>36</td><td>53</td><td>70</td><td>89</td><td>108</td><td>108</td><td>121</td><td>123</td><td>395%</td></tr><tr><td>Poultry</td><td>9</td><td>15</td><td>26</td><td>41</td><td>69</td><td>99</td><td>135</td><td>136</td><td>139</td><td>1456%</td></tr><tr><td>Sheep &amp; Goat</td><td>6</td><td>7</td><td>7</td><td>10</td><td>11</td><td>14</td><td>16</td><td>16</td><td>17</td><td>176%</td></tr><tr><td>Other</td><td>3</td><td>3</td><td>4</td><td>4</td><td>4</td><td>6</td><td>6</td><td>6</td><td>6</td><td>106%</td></tr></tbody></table>

Source: www.fao.org/faostat/en/#data/QCL & Author's calculations.

The remarkable growth in primary crops and livestock can be attributed to three key factors: expansion of cultivated area (although we have seen that only one-third of this growth is due to this factor), the adoption of intensive agricultural practices, and the increased use of inputs such as fertilizers and pesticides. Fertilizer use has increased sixfold in the last sixty years, while pesticide use has doubled between 1990 and 2022 (Table 5.36).

<table><caption>Table 5.36: Agricultural use of inputs (fertilizers and pesticides)</caption><thead><tr><th rowspan="2">Input</th><th colspan="5">Agricultural use (in million tonnes)</th><th colspan="5">Use par area of cropland (in kg/ha)</th></tr><tr><th>1961</th><th>1980</th><th>2000</th><th>2020</th><th>2022</th><th>1961</th><th>1980</th><th>2000</th><th>2020</th><th>2022</th></tr></thead><tbody><tr><td>Fertilizer</td><td>31.0</td><td>116.6</td><td>135.2</td><td>201.7</td><td>185.4</td><td>20.8</td><td>76.8</td><td>85.9</td><td>123.5</td><td>113.1</td></tr><tr><td>Nitrogen</td><td>11.5</td><td>60.6</td><td>81.0</td><td>114.7</td><td>108.1</td><td>7.6</td><td>39.6</td><td>51.3</td><td>69.6</td><td>65.4</td></tr><tr><td>Phosphate</td><td>10.9</td><td>31.8</td><td>32.5</td><td>47.8</td><td>41.9</td><td>7.5</td><td>21.4</td><td>21.0</td><td>29.8</td><td>26.0</td></tr><tr><td>Potash</td><td>8.6</td><td>24.2</td><td>21.7</td><td>39.3</td><td>35.5</td><td>5.7</td><td>15.8</td><td>13.7</td><td>24.1</td><td>21.7</td></tr><tr><td>Pesticide</td><td></td><td></td><td>2.2</td><td>3.4</td><td>3.7</td><td></td><td></td><td>1.5</td><td>2.2</td><td>2.4</td></tr></tbody></table>

Source: www.fao.org/faostat/en/#data/RFN, www.fao.org/faostat/en/#data/RP & Author's calculations.

**Remark 57** The figures presented in this paragraph can be analyzed at the country or regional level. In this case, we observe large differences between regions of the world, for example between the Americas and Africa, or between developed and developing countries (FAO, 2023b).

**Food consumption** Table 5.37 shows the food supply<fnref n="102" /> per capital and per day for the different regions of the world. In general, food supply is assessed along three primary dimensions: energy intake, protein intake, and fat intake. In 2022, the global average daily per capita intake was 2985 kilocalories of energy<fnref n="103" />, 92 grams of protein, and 87 grams of fat. Over the past sixty years, these figures have increased by 36%, 51%, and 81%, respectively. However, these averages mask

<footnote>species), and vascular plants (81 species) are collected and consumed by more than 100 million EU citizens.</footnote>

<footnote n="102">¹⁰²Food supply measures the amount available for consumption at the end of the supply chain and does not include consumption waste.</footnote>

<footnote n="103">¹⁰³In the context of nutrition, 1 Calorie (with a capital C) is used as a shorthand for 1 kilocalorie (kcal).</footnote>
<table><caption>Table 5.37: Food supply per capital per day (energy, protein & fat)</caption><thead><tr><th rowspan="2">Region</th><th colspan="3">Energy<br>(in Cal/capita/day)</th><th colspan="3">Protein<br>(in g/capita/day)</th><th colspan="3">Fat<br>(in g/capita/day)</th></tr><tr><th>1961</th><th>1990</th><th>2022</th><th>1961</th><th>1990</th><th>2022</th><th>1961</th><th>1990</th><th>2022</th></tr></thead><tbody><tr><td>Africa</td><td>1993</td><td>2291</td><td>2567</td><td>53</td><td>59</td><td>66</td><td>40</td><td>47</td><td>56</td></tr><tr><td>Eastern Africa</td><td>1989</td><td>1925</td><td>2263</td><td>56</td><td>50</td><td>59</td><td>29</td><td>32</td><td>47</td></tr><tr><td>Middle Africa</td><td>2018</td><td>1847</td><td>2312</td><td>49</td><td>45</td><td>46</td><td>36</td><td>44</td><td>41</td></tr><tr><td>Northern Africa</td><td>1920</td><td>2851</td><td>3142</td><td>53</td><td>78</td><td>91</td><td>38</td><td>60</td><td>74</td></tr><tr><td>Southern Africa</td><td>2603</td><td>2755</td><td>2713</td><td>70</td><td>73</td><td>79</td><td>58</td><td>64</td><td>91</td></tr><tr><td>Western Africa</td><td>1909</td><td>2199</td><td>2644</td><td>46</td><td>52</td><td>65</td><td>46</td><td>49</td><td>57</td></tr><tr><td>Americas</td><td>2559</td><td>2953</td><td>3392</td><td>77</td><td>82</td><td>104</td><td>78</td><td>97</td><td>135</td></tr><tr><td>Northern America</td><td>2873</td><td>3447</td><td>3881</td><td>95</td><td>107</td><td>122</td><td>110</td><td>138</td><td>177</td></tr><tr><td>Central America</td><td>2180</td><td>2796</td><td>3173</td><td>59</td><td>73</td><td>94</td><td>48</td><td>69</td><td>101</td></tr><tr><td>Caribbean</td><td>1992</td><td>2390</td><td>2828</td><td>47</td><td>57</td><td>75</td><td>42</td><td>65</td><td>78</td></tr><tr><td>South America</td><td>2329</td><td>2599</td><td>3111</td><td>63</td><td>65</td><td>95</td><td>49</td><td>73</td><td>118</td></tr><tr><td>Asia</td><td>1805</td><td>2414</td><td>2944</td><td>47</td><td>61</td><td>93</td><td>25</td><td>49</td><td>78</td></tr><tr><td>Central Asia</td><td></td><td></td><td>3169</td><td></td><td></td><td>103</td><td></td><td></td><td>104</td></tr><tr><td>Eastern Asia</td><td>1594</td><td>2564</td><td>3361</td><td>44</td><td>69</td><td>124</td><td>18</td><td>56</td><td>95</td></tr><tr><td>Southern Asia</td><td>2014</td><td>2242</td><td>2584</td><td>52</td><td>54</td><td>72</td><td>30</td><td>40</td><td>62</td></tr><tr><td>South-eastern Asia</td><td>1836</td><td>2178</td><td>2880</td><td>40</td><td>48</td><td>80</td><td>27</td><td>41</td><td>69</td></tr><tr><td>Western Asia</td><td>2501</td><td>3273</td><td>3128</td><td>76</td><td>93</td><td>93</td><td>57</td><td>84</td><td>101</td></tr><tr><td>Europe</td><td>3041</td><td>3367</td><td>3471</td><td>90</td><td>104</td><td>112</td><td>89</td><td>125</td><td>140</td></tr><tr><td>Eastern Europe</td><td>3100</td><td>3360</td><td>3375</td><td>95</td><td>105</td><td>109</td><td>73</td><td>108</td><td>121</td></tr><tr><td>Northern Europe</td><td>3176</td><td>3214</td><td>3402</td><td>91</td><td>96</td><td>113</td><td>131</td><td>134</td><td>141</td></tr><tr><td>Southern Europe</td><td>2838</td><td>3469</td><td>3519</td><td>82</td><td>107</td><td>116</td><td>72</td><td>136</td><td>149</td></tr><tr><td>Western Europe</td><td>3016</td><td>3374</td><td>3615</td><td>89</td><td>103</td><td>113</td><td>113</td><td>149</td><td>162</td></tr><tr><td>Oceania</td><td>3021</td><td>3139</td><td>3101</td><td>100</td><td>105</td><td>101</td><td>108</td><td>126</td><td>128</td></tr><tr><td>Australasia</td><td>3060</td><td>3188</td><td>3417</td><td>103</td><td>109</td><td>115</td><td>111</td><td>129</td><td>152</td></tr><tr><td>Melanesia</td><td>2534</td><td>2547</td><td>2314</td><td>54</td><td>65</td><td>66</td><td>60</td><td>86</td><td>66</td></tr><tr><td>Micronesia</td><td>2424</td><td>2622</td><td>2937</td><td>51</td><td>64</td><td>86</td><td>116</td><td>94</td><td>126</td></tr><tr><td>Polynesia</td><td>2239</td><td>2683</td><td>2959</td><td>55</td><td>77</td><td>107</td><td>73</td><td>107</td><td>117</td></tr><tr><td>World</td><td>2196</td><td>2621</td><td>2985</td><td>61</td><td>70</td><td>92</td><td>48</td><td>67</td><td>87</td></tr></tbody></table>

Source: www.fao.org/faostat/en/#data/FBS, www.fao.org/faostat/en/#data/FBSH & Author's calculations.

significant differences between regions. For example, fat intake is currently only 41 g/capita/day in Middle Africa, while it reaches 177 g/capita/day in North America. These differences are even more pronounced when we look at countries. Figure 5.46 shows the dispersion of food supply for the 200 countries included in the FAO database. We can see that the dispersion has not decreased since 1961, in fact it has increased! Countries with the lowest average energy intake include Burundi, Lesotho, Somalia, Haiti, and Madagascar, while those with the highest energy intake are Israel, the United States, Ireland, Belgium, and Turkey. For protein intake, the lowest levels are found in the Democratic Republic of the Congo, Madagascar, Burundi, Somalia, and Mozambique, while Iceland, Israel, Ireland, Mongolia, and Montenegro have the highest. In terms of fat intake, Burundi, DR Congo, Madagascar, Rwanda, and Cambodia rank the lowest, while Belgium, Austria, the United States, Israel, and Germany rank the highest. This reflects economic disparities, but also some of the dietary habits of the countries with the highest levels. The vegetal/animal structure of the human diet also has an impact (Table 5.38). In fact, protein intake from animal sources has increased over the past sixty years, while the contribution of animal products to fat intake has decreased.
<figure><img src="image_192.png" /><figcaption>Figure 5.46: Country dispersion of food supply</figcaption></figure>

Source: www.fao.org/faostat/en/#data/FBS, www.fao.org/faostat/en/#data/FBSH & Author's calculations.

<table><caption>Table 5.38: Split of food supply between vegetal and animal products</caption><thead><tr><th rowspan="2">Food Origin</th><th colspan="3">1961</th><th colspan="3">2022</th></tr><tr><th>Energy</th><th>Protein</th><th>Fat</th><th>Energy</th><th>Protein</th><th>Fat</th></tr></thead><tbody><tr><td>Vegetal</td><td>1 858</td><td>41.80</td><td>22.80</td><td>2 460</td><td>53.45</td><td>51.33</td></tr><tr><td>Animal</td><td>338</td><td>19.66</td><td>24.72</td><td>525</td><td>38.08</td><td>35.98</td></tr><tr><td>Total</td><td>2 196</td><td>61.46</td><td>47.52</td><td>2 945</td><td>91.52</td><td>87.31</td></tr></tbody></table>

Source: www.fao.org/faostat/en/#data/FBS, www.fao.org/faostat/en/#data/FBSH & Author's calculations.

**Food security** Biodiversity is closely linked to food security, as it provides the foundation for resilient and sustainable food systems. This interconnectedness has several dimensions. Moreover, the concept of double materiality is highly relevant, as biodiversity impacts food production, while food and agricultural production also has a significant impact on biodiversity. In this context, it is challenging to classify and list all the dimensions. Therefore, we will focus on a few key dimensions that are essential for food security. The first dimension is the genetic diversity of crops and livestock. Below is an excerpt from the press release of the FAO 2019 biodiversity assessment publication (FAO, 2019):

> "Less biodiversity means that plants and animals are more vulnerable to pests and diseases. Compounded by our reliance on fewer and fewer species to feed ourselves, the increasing loss of biodiversity for food and agriculture puts food security and nutrition at risk." (José Graziano da Silva, FAO Director-General, 22 February 2019, Rome).
Many crop species are no longer cultivated because production is concentrated on the most profitable crops. Over time, this has led to an industrial selection of species, resulting in the homogenization and globalization of the human diet. In this context, we are more vulnerable when a species faces problems such as disease. Here are some examples:

* Irish potato famine (1845–1852)

The main cause was the infection of potato crops by the potato blight (caused by the fungus-like microorganism *Phytophthora infestans*). This blight led to one million deaths and caused the mass emigration of another million people. The severity of the disease was largely due to Ireland's dependence on a single crop, the potato.

* Coffee leaf rust outbreak in Sri Lanka (1860–1890)

Coffee leaf rust (CLR) is a disease of the coffee plant caused by the fungus *Hemileia vastatrix*. In the 1860s, coffee production was an important resource for Sri Lanka, but the outbreak of coffee leaf rust destroyed the coffee industry in the region and led to a shift to tea cultivation.

* Panama disease in bananas (1950s)

Panama disease, caused by the fungus *Fusarium oxysporum*, infects banana plants and nearly eradicated the Gros Michel variety in the 1950s. This led to its replacement with the Cavendish variety, which now faces a similar threat from a new strain of the disease.

* Southern corn leaf blight in the United States (1970)

Southern corn leaf blight is a disease of corn caused by the plant pathogen *Bipolaris maydis* (Race O, Race C, and Race T). The 1970 epidemic in the USA was triggered by the widespread use of Texas male sterile cytoplasm corn, which made 90% of hybrid crops susceptible to the newly emerged Race T pathogen. This outbreak, which began in the southern U.S. and spread rapidly in the north, destroyed approximately 15% of corn production.

* Wheat stem rust Ug99 in Africa (1998–present)

Ug99 is a strain of wheat stem rust caused by the fungus *Puccinia graminis*. It was first identified in 1998 and has spread to several countries in Africa and the Middle East. It poses a significant threat to global wheat production due to the high susceptibility of many wheat varieties.

* Citrus greening disease in Florida (2005–present)

Citrus greening disease, caused by *Liberibacter bacteria*, affects citrus plants and exists in three forms: a heat-tolerant Asian type and heat-sensitive African and American types. First identified in 1929 and reported in southern China in 1943, the disease has severely impacted citrus production in the United States, particularly in Florida, where orange production has declined by more than 50% since 2005.

These diseases highlight the risks of focusing on one or a few crop species. In fact, reliance on a limited number of crop species increases the vulnerability of agriculture to disease. When a single pathogen targets a widely grown crop, such as Panama disease in bananas or Southern corn leaf blight in maize, the impact can be catastrophic, causing widespread losses. Biodiversity is then essential for building resilience to future outbreaks. Coffee is a typical example. According to Davis et al. (2019), coffee production is concentrated in a small number of coffee species. In particular, Arabica (*Coffea arabica*) and Robusta (*Coffea canephora*) account for 60% and 40% of traded coffee, respectively, leaving little room for other species. Arabica coffee has been cultivated for at least several hundred years, which is not the case with Robusta, which was first cultivated in the mid-1800s. Robusta coffee went from being a minor African crop to a major global commodity in only
about 150 years, and Davis et al. (2019) concluded that “*Robusta coffee* provides a good example of *how a (relatively) newly discovered wild species has transformed a globally important crop.*” One of the main reasons for its success is its resistance to coffee leaf rust. Of the 124 known coffee species worldwide, their conservation status according to the IUCN extinction risk classification is as follows: 13 are Critically Endangered (CR); 40 are Endangered (EN); 22 are Vulnerable (VU); 9 are Near Threatened (NT); 26 are Least Concern (LC); 14 are Not Evaluated (NE) or Data Deficient (DD). This means that 75% of coffee species are threatened with extinction (Davis et al., 2019, Figure 1, page 2). Therefore, coffee production is at significant risk if existing diseases<fnref n="104" /> become more severe or if new diseases emerge, as the number of alternative species that could potentially replace today’s Arabica or Robusta varieties has declined over the past century.

The previous example deals with intraspecific diversity (for example, the different types of coffee in the genus *Coffea*), but biodiversity loss is also related to interspecific diversity (for example, the different crops in the clade *Mesangiospermae*). While intraspecific diversity of the food supply has declined in recent times, interspecific diversity has increased with the diversification of diets and trade in commodities, especially in developed countries. Many people now eat exotic fruits, fish and vegetables that would not have been available less than a century ago. Agricultural specialization and the diversity of human diets explain the growth of agricultural trade. Table 5.39 shows that the export share of most crops and commodities has increased. For example, avocado exports represented less than 1% of production in 1961, compared to more than 35% of production in 2021. FAO statistics show that the largest increases (more than 30% between 1961 and 2021) are for cherries, coconut oil, cranberries, kiwi, lentils, mustard seeds, natural honey, olive oil, peas, quinoa, and soybeans. Looking at OECD statistics, the largest increases (+20% between 1990 and 2023) are for edible fish meals, skim milk powder, soybeans, and sugar. Agricultural trade is a resilient factor in food security, but it also reveals the vulnerability of some countries that have specialized in the most profitable crops.

<caption>Box 5.13: Founder crops</caption>

The concept of “founder crops” was introduced by Zohary and Hopf (1988). The authors proposed that eight plant species were first domesticated by early farming communities, laying the foundation of early Neolithic agriculture in Southwest Asia (the Fertile Crescent region of the Near East) around 10 500 years BP. These crops are thought to form the basis of modern agriculture in Europe, Southwest Asia, South Asia, and North Africa. The original founder crops included three cereals (emmer wheat, einkorn wheat, and barley), four pulses (lentil, pea, chickpea, and bitter vetch), and flax.

Subsequent research has suggested that many other species could also be considered as founder crops. This list has been extended for various regions and may include rice and foxtail millet in the Yangtze and Yellow River valleys (about 9 500 years BP), maize, squash, and common bean in southwest Mexico (5 300 years BP), potato, quinoa, and common bean in the Central Andes (5 100 years BP), as well as goosefoot, sunflower, and squash in the Eastern United States (5 100 years BP).

<footnote n="104">¹⁰⁴There are many coffee diseases that can be classified into four main categories: bacterial diseases, fungal diseases, root diseases, and viral diseases. Here are some examples: anthracnose (*Colletotrichum*), bacterial blight of coffee (*Pseudomonas syringae*), black rot (*Koleroga noxia*), brown eye spot (*Cercospora coffeicola*), cercospora leaf spot (*Cercospora coffeicola*), coffee leaf rust (*Hemileia vastatrix*), coffee berry disease (*Colletotrichum kahawae*), coffee ringspot virus (*Brevipalpus phoenicis*), coffee wilt disease (*Fusarium xylarioides*), root-knot nematode disease (*Meloidogyne*).</footnote>
<table><caption>Table 5.39: Share of world crop production exported (in %)</caption><thead><tr><th>Crop</th><th>1961</th><th>2021</th><th>Crop</th><th>1961</th><th>2021</th></tr></thead><tbody><tr><td>Apples</td><td>9.4</td><td>8.8</td><td>Olive oil</td><td>14.9</td><td>66.2</td></tr><tr><td>Apricots</td><td>5.0</td><td>8.6</td><td>Onions and shallots</td><td>2.0</td><td>9.1</td></tr><tr><td>Avocados</td><td>0.2</td><td>36.3</td><td>Oranges</td><td>16.3</td><td>10.0</td></tr><tr><td>Bananas</td><td>16.6</td><td>19.3</td><td>Peaches and nectarines</td><td>6.0</td><td>7.0</td></tr><tr><td>Barley</td><td>9.9</td><td>30.0</td><td>Peas</td><td>4.0</td><td>48.7</td></tr><tr><td>Blueberries</td><td>36.9</td><td>39.7</td><td>Persimmons</td><td>0.4</td><td>14.0</td></tr><tr><td>Cauliflowers</td><td>6.4</td><td>6.1</td><td>Pineapples</td><td>2.8</td><td>12.7</td></tr><tr><td>Cherries</td><td>2.9</td><td>35.2</td><td>Pomelos</td><td>10.3</td><td>11.0</td></tr><tr><td>Coconut oil</td><td>21.1</td><td>80.6</td><td>Potatoes</td><td>1.0</td><td>3.8</td></tr><tr><td>Cranberries</td><td>0.0</td><td>50.9</td><td>Quinoa</td><td>0.0</td><td>68.8</td></tr><tr><td>Cucumbers</td><td>1.5</td><td>3.5</td><td>Sesame seeds</td><td>11.0</td><td>32.2</td></tr><tr><td>Dates</td><td>14.0</td><td>19.1</td><td>Soybeans</td><td>15.5</td><td>43.2</td></tr><tr><td>Eggplants</td><td>0.2</td><td>1.1</td><td>Spinach</td><td>0.3</td><td>1.1</td></tr><tr><td>Kiwi fruit</td><td>0.0</td><td>36.4</td><td>Strawberries</td><td>5.4</td><td>11.1</td></tr><tr><td>Lentils</td><td>6.5</td><td>67.5</td><td>Tomatoes</td><td>3.9</td><td>4.4</td></tr><tr><td>Maize</td><td>6.8</td><td>16.2</td><td>Vanilla</td><td>73.8</td><td>91.6</td></tr><tr><td>Mustard seeds</td><td>19.3</td><td>53.8</td><td>Watermelons</td><td>0.9</td><td>4.7</td></tr><tr><td>Natural honey</td><td>11.0</td><td>42.3</td><td>Wheat</td><td>17.8</td><td>25.9</td></tr><tr><td>Commodity</td><td>1990</td><td>2023</td><td>Commodity</td><td>1990</td><td>2023</td></tr><tr><td>Beef meat</td><td>9.2</td><td>18.2</td><td>Pork meat</td><td>1.9</td><td>8.7</td></tr><tr><td>Butter</td><td>10.0</td><td>7.8</td><td>Poultry meat</td><td>8.5</td><td>11.2</td></tr><tr><td>Cheese</td><td>4.0</td><td>13.6</td><td>Pulses</td><td>10.5</td><td>19.8</td></tr><tr><td>Cotton</td><td>24.2</td><td>36.5</td><td>Rice</td><td>3.4</td><td>10.1</td></tr><tr><td>Edible fish meals</td><td>44.7</td><td>67.6</td><td>Roots and tubers</td><td>7.3</td><td>7.8</td></tr><tr><td>Eggs</td><td>2.6</td><td>1.6</td><td>Sheep meat</td><td>12.2</td><td>9.0</td></tr><tr><td>Fish</td><td>15.4</td><td>23.0</td><td>Skim milk powder</td><td>26.4</td><td>57.9</td></tr><tr><td>Fresh dairy products</td><td>0.0</td><td>0.1</td><td>Soybeans</td><td>26.6</td><td>45.1</td></tr><tr><td>Maize</td><td>12.8</td><td>15.3</td><td>Sugar</td><td>9.8</td><td>36.8</td></tr><tr><td>Oilseed meals</td><td>24.2</td><td>24.5</td><td>Vegetable oils</td><td>24.3</td><td>37.2</td></tr><tr><td>Other coarse grains</td><td>7.7</td><td>14.0</td><td>Wheat</td><td>19.4</td><td>24.1</td></tr><tr><td>Other oilseeds</td><td>15.2</td><td>13.6</td><td>Whole milk powder</td><td>45.9</td><td>49.9</td></tr></tbody></table>

Source: www.fao.org/faostat/en/#data/QCL, www.fao.org/faostat/en/#data/TCL, https://data-explorer.oecd.org & Author's calculations.

A typical example of crop expansion is maize (Pollan, 2007; Hartigan, 2017). In 2022, maize (Zea mays) was the world's most widely produced grain crop (1.16 billion tonnes, covering the second-largest cultivated area (203.5 million hectares) and achieving the highest grain yield (5.72 tonnes per hectare). Today, maize appears in a wide range of foods, including candy corn, corn dogs, corn flakes, corn soup, cornstarch, corn tortillas, cornbread, polenta, popcorn, tamales, and more. This proliferation is especially noticeable in the Americas. For instance, Maize is the focus of the documentary film King Corn, which was released in October 2007. The film explores the trend of increased corn production and its impact on American society. However, maize has not always been the dominant crop that “has conquered the world.” Native to Central America, maize was introduced to Europe in the early 16th century following Christopher Columbus's voyages to the Americas. Initially brought to Spain by explorers returning from the New World, maize gradually
spread across southern Europe, the Mediterranean, and eventually to Africa and Asia. Its global spread was largely due to its adaptability to different climates and soil conditions. Figure 5.47 illustrates the expansion of maize in world agriculture. The first panel shows the area harvested. In 1961, this was 105.6 million hectares — less than the area cultivated for rice (115.4 million hectares) and wheat (204.2 million hectares). By 2022, the area cultivated for maize is nearly equal to that of wheat and surpasses that of rice. Maize production has accelerated since 2000, driven by a significant expansion of harvested area and the introduction of genetically modified (GMO) maize varieties. One of the key issues with maize production is its substitution effect on the supply of other crops. In particular, the primary use of maize is not for direct human consumption but as animal feed. Using FAO statistics, we report below the breakdown of maize use<fnref n="105" />:

<table><thead><tr><th>Year</th><th>Animal feed</th><th>Human food</th><th>Losses</th><th>Seed</th><th>Processing</th><th>Other uses</th></tr></thead><tbody><tr><td>2010</td><td>55.3%</td><td>13.6%</td><td>3.5%</td><td>0.7%</td><td>5.7%</td><td>21.2%</td></tr><tr><td>2022</td><td>60.4%</td><td>11.4%</td><td>5.2%</td><td>0.7%</td><td>5.8%</td><td>16.5%</td></tr></tbody></table>

60% of maize is used as animal feed, mainly for poultry and cattle. Direct human consumption accounts for only 11% of total production, which is less than the *other uses* category. In fact, a significant portion of maize (between 10% and 20%) is processed to produce ethanol, a biofuel used primarily in gasoline blends.

<figure><img src="image_193.png" /><figcaption>Figure 5.47: Area harvested, production and yield of cereal crops</figcaption></figure>

Source: www.fao.org/faostat/en/#data/QCL & Author's calculations.

The previous issue relates to the Earth's capacity to provide all necessary ecosystem functions without compromising others. Here, the *Do No Significant Harm (DNSH)* concept plays a crucial

<footnote n="105">¹⁰⁵We first calculate the net supply quantity with the following equations: Supply quantity = Production + Imports - Exports - Stock variation. Then, we use the following decomposition: Supply quantity = Feed + Seed + Losses + Processing + Other uses (non-food) + Tourist consumption + Residuals + Food.</footnote>
role. But it's not just a matter of choosing one ecosystem service over another. Instead, we see a global deterioration of ecosystem services when examining the ecological footprint and the Earth's biocapacity. The ecological footprint, introduced by Wackernagel and Rees (1996), measures the rate at which we consume resources and produce waste compared to the rate at which nature can regenerate those resources and absorb waste. This metric balances the demand for ecosystem services against their supply. On the demand side, the ecological footprint assesses the use of productive land areas, and quantifies the ecological assets required by a population or product to produce the resources it consumes and to absorb its wastes. On the supply side, biocapacity measures the productivity of ecological assets (such as cropland, grazing land, forests, fisheries, and built-up areas). Both metrics are expressed in global hectares, either for a region or per capita, allowing for meaningful comparisons across regions.

<figure><img src="image_194.png" /><figcaption>Figure 5.48: Global ecological footprint and biocapacity (in global hectares per capita)</figcaption></figure>

Source: https://data.footprintnetwork.org & Author's calculations.

Figure 5.48 shows the global ecological footprint when considering the various ecological assets. If the ecological footprint exceeds biocapacity, the region has a biocapacity deficit (red area); otherwise, it has a biocapacity reserve (green area). Before 1970, global biocapacity was greater than the ecological footprint, but since then, a biocapacity deficit has emerged. This shift has occurred because, while the per capita ecological footprint has remained relatively constant, the per capita biocapacity has declined, as the Earth's resources are finite. There are significant regional differences. South America's biocapacity balance has declined over time but remains positive, while Europe has experienced a biocapacity deficit for many years. If we focus specifically on the ecosystem service of cropland (Figure 5.49), we see different regional trends. In Asia, Europe, and South America, both demand and supply per capita have increased. However, the biocapacity balance remains positive in South America, is negative but significantly reduced in Europe, and is negative and increasing in Asia. In North America, the biocapacity reserve has remained roughly constant since 2000.
<figure><img src="image_195.png" /><figcaption>Figure 5.49: Cropland ecological footprint and biocapacity (in global hectares per capita)</figcaption></figure>

Source: https://data.footprintnetwork.org & Author's calculations.

In addition to food diversity, a second important dimension of food security is the Earth's capacity to feed the planet, or 8.2 billion people. The FAO uses several measures to assess food insecurity. One of the main metrics is the prevalence of (severe/moderate) food insecurity. Let $X$ and $R$ be the random variables for energy intake and energy requirement, respectively, with a joint probability distribution $\mathbf{F}(x, r)$. Sukhatme (1961, Equation 1, page 466) defines the prevalence of undernourishment as the probability that intake is less than requirement:

$$ \mathrm{PoU} = \mathrm{Pr} \{X < R\} = \iint \mathbb{1} \{x < r\} \cdot d\mathbf{F}(x, r) = \iint_{x<r} f(x, r) \, dx \, dr \quad (5.16) $$

where $f(x, r)$ is the bivariate density function of $(X, R)$. The non-parametric estimator of PoU is the empirical frequency:

$$ \mathrm{PoU} = \frac{1}{n} \sum_{i=1}^{n} \mathbb{1} \{x_i < r_i\} $$

where $n$ denotes the population size and $(x_i, r_i)$ are the observed intake and requirement values for individual $i$. Another approach assumes a parametric density function $f(x, r; \theta)$, estimates the vector of parameters $\theta$, and calculates $\mathrm{PoU} = \iint_{x<r} f(x, r; \theta) \, dx \, dr$. Both statistical methods require a sample $(x_i, r_i)$ of energy intake and requirement values. However, in practice, determining an individual's exact energy requirement is challenging. Therefore, the FAO approximates the prevalence of undernourishment as follows:

$$ \mathrm{PoU} = \mathrm{Pr} \{X < r_L\} = \int_{x<r_L} f_x(x) \, dx = \mathbf{F}_x(r_L) \quad (5.17) $$
where $F_x(x)$ is the cumulative distribution function of energy intake, often called dietary energy consumption (DEC), and $r_L$ is a cut-off point representing the minimum requirement, also known as the minimum dietary energy requirement (MDER)$^{106}$. The univariate approach is valid when applied to a homogenous group such that $\sigma(R) \approx 0$. Therefore PoU is calculated at the group level (e.g., sex × age) and then aggregated across the different groups in a population. It is commonly accepted that $X \sim LN(\mu_x, \sigma_x^2)$. The calibration of the parameters $\mu_x$ and $\sigma_x$ is done by matching the first two statistical moments of $X$. Let $\mu(X)$ and $CV(X) = \sigma(X)/\mu(X)$ be the empirical mean$^{107}$ and the coefficient of variation of $X$, respectively. Since we have $\mu(X) = \exp(\mu_x + \frac{1}{2}\sigma_x^2)$ and $CV^2(X) = \exp(\sigma_x^2) - 1$, we deduce that:

$$ \left\{ \begin{array}{l} \mu_x = \ln \mu(X) - \frac{1}{2} \ln (CV^2(X) + 1) \\ \sigma_x = \sqrt{\ln (CV^2(X) + 1)} \end{array} \right. $$

According to FAO (2024a), the minimum energy requirement $m_i$ by sex and age group is calculated by estimating the basal metabolic rate (BMR) and multiplying it by the ideal weight for a healthy person in that sex/age group. These ideal weights are derived from the body mass index (BMI) reference tables published by the World Health Organization (WHO). The minimum energy requirement is then adjusted using a physical activity level (PAL) coefficient. Finally, the cut-off point is obtained as the weighted average of the minimum energy requirements: $r_L = \sum_{i=1}^{n} f_i m_i$ where $f_i$ is the frequency of individuals in sex/age group $i$ within the population.

**Remark 58** While the estimation of $\mu(X)$ is simply the empirical mean, the estimation of $CV(X)$ is more complex. According to Naiken (2002), the coefficient of variation is assumed to have two components:

$$ CV^2(X) = CV^2(X | Y) + CV^2(X | R) $$

where $CV(X | Y)$ is the coefficient of variation of $X$ due to income dispersion and $CV(X | R)$ is the coefficient of variation of $X$ due to energy requirement. In general, $CV(X | R)$ is assumed to be constant and equal to 20%.

**Example 23** We assume$^{108}$ that the average dietary energy consumption (ADEC) $\mu(X)$ is 2589 kcal/capita/day, the coefficient of variation $CV(X)$ is 0.27, the minimum dietary energy requirement (MDER) $r_L$ is 1803 kcal/capita/day and the average dietary energy requirement (ADER) is 2333 kcal/capita/day.

We first calibrate the log-normal distribution of the dietary energy consumption. The estimated values of the parameters are:

$$ \sigma_x = \sqrt{\ln(0.27^2 + 1)} = 0.2653 $$

$$ \mu(X) = \frac{1}{365} \left( \frac{1}{N} \sum_{i=1}^{m} Q_i C_i \right) $$

where $Q_i$ is the amount (in kg) of food product $i$ consumed annually by the population, $C_i$ is the energy density (in Calories/kg) of food product $i$, and $N$ is the population size. The term $\sum_{i=1}^{m} Q_i C_i$ represents the total calories consumed by the population in one year, while $\mu(X)$ is the average number of calories per capita per day. We recall that food available for human consumption is equal to the total food supply (production + imports – exports – changes in stocks) minus animal feed minus seeds minus other non-human uses minus waste.

<footnote>$^{106}$This approximation is suggested in Sukhatme (1961, Equation 4, page 473). See also Exercise 5.5.1 on page 564 for a comparison between the bivariate and univariate distribution approaches.</footnote>

<footnote>$^{107}$The mean dietary energy consumption is given by:</footnote>

<footnote>$^{108}$These figures are those obtained from the FAO in the case of India in 2022.</footnote>
and:

$$ \mu_x = \ln(2589) - \frac{0.2653^2}{2} = 7.8238 $$

We can then deduce that the prevalence of undernourishment is 10.91%:

$$ \text{PoU} = \text{Pr}\{X < 1803\} = \Phi\left(\frac{\ln(1803) - 7.8238}{0.2653}\right) = 0.1091 $$

Figure 5.50 shows the probability distribution of the dietary energy consumption $X$, the MDER and the prevalence of undernourishment. We also report the values of ADER and ADEC, which measure the average dietary energy requirement and consumption. In this case, we observe that ADER < ADEC, which is the normal situation.

<figure><img src="image_196.png" /><figcaption>Figure 5.50: Dietary energy consumption and prevalence of undernourishment (India, 2022)</figcaption></figure>

Source: www.fao.org/faostat/en/#data/FS & Author's calculations.

Figure 5.51 shows the evolution of several statistics from 2000 to 2022. The first panel compares ADEC, ADER and MDER. As expected, the average and minimum dietary energy requirements are relatively stable. On average, ADER is equal to 2360 kcal/capita/year, while MDER is equal to 1820 kcal/capita/year. On the contrary, the average dietary energy consumption is increasing over time at the global level. It is now close to 3000 kcal/capita/year. However, these global figures hide some large differences between countries, as some countries have experienced large declines in recent years. In fact, we observe that the prevalence of undernourishment has decreased from 2000 to 2019, but it is increasing since 2020. As a result, the number of undernourished people is currently around 730 million. Figure 5.52 highlights this wide variability across countries. Average dietary energy consumption ranges from 1834 Calories per capita per day in Somalia to 3912 Calories in the USA. Somalia also has the highest prevalence of undernourishment, at 51% in 2022. The suite of
<figure><img src="image_197.png" /><figcaption>Figure 5.51: Prevalence of undernourishment and number of undernourished people (World)</figcaption></figure>

Source: www.fao.org/faostat/en/#data/FS & Author's calculations.

<figure><img src="image_198.png" /><figcaption>Figure 5.52: Histogram of food insecurity indicators (country level, 2022)</figcaption></figure>

Source: www.fao.org/faostat/en/#data/FS & Author's calculations.
food security indicators on the FAOSTAT website includes many other statistics on food insecurity. For example, the bottom panels of Figure 5.52 show the prevalence of severe food insecurity in the total population and the prevalence of obesity in adults (18 years and older). Other indicators are presented in Table 5.40 for the different regions of the world.

<table><caption>Table 5.40: Food security indicators by region (2022)</caption><thead><tr><th>Indicator</th><th>Africa</th><th>Asia</th><th>Europe</th><th>North America</th><th>Oceania</th><th>South America</th><th>World</th></tr></thead><tbody><tr><td>MDER (kcal/capita/day)</td><td>1736</td><td>1831.0</td><td>1931</td><td>1962</td><td>1871</td><td>1856</td><td>1832</td></tr><tr><td>ADER (kcal/capita/day)</td><td>2237</td><td>2369.0</td><td>2505</td><td>2554</td><td>2424</td><td>2403</td><td>2370</td></tr><tr><td>ADEC (kcal/capita/day)</td><td>2578</td><td>2917.0</td><td>3467</td><td>3882</td><td>3104</td><td>3104</td><td>2971</td></tr><tr><td>Prevalence of undernourishment</td><td>19.9</td><td>8.2</td><td></td><td></td><td>7.1</td><td>6.6</td><td>9.1</td></tr><tr><td>People undernourished (million)</td><td>284.1</td><td>386.5</td><td></td><td></td><td>3.2</td><td>43.9</td><td>723.8</td></tr><tr><td>Severe food insecurity</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total population</td><td>21.7</td><td>9.7</td><td>1.8</td><td>0.9</td><td>9.3</td><td>11.0</td><td>10.8</td></tr><tr><td>Rural adult population</td><td>23.5</td><td>10.4</td><td>1.7</td><td>0.8</td><td>2.8</td><td>13.5</td><td>12.2</td></tr><tr><td>Town adult population</td><td>22.2</td><td>10.9</td><td>1.9</td><td>0.7</td><td>4.0</td><td>12.9</td><td>11.5</td></tr><tr><td>Urban adult population</td><td>19.8</td><td>8.3</td><td>1.8</td><td>1.2</td><td>3.0</td><td>9.6</td><td>9.3</td></tr><tr><td>Male adult population</td><td>20.8</td><td>8.6</td><td>1.7</td><td>0.7</td><td>8.5</td><td>9.6</td><td>9.1</td></tr><tr><td>Female adult population</td><td>21.3</td><td>9.9</td><td>1.9</td><td>1.2</td><td>8.3</td><td>12.0</td><td>10.2</td></tr><tr><td>Total population (million)</td><td>309.0</td><td>459.2</td><td>13.3</td><td>3.5</td><td>4.2</td><td>72.5</td><td>861.7</td></tr><tr><td>Male adults (million)</td><td>87.9</td><td>157.2</td><td>6.4</td><td>1.0</td><td>1.5</td><td>23.7</td><td>277.7</td></tr><tr><td>Female adults (million)</td><td>92.0</td><td>177.6</td><td>7.8</td><td>1.9</td><td>1.4</td><td>31.2</td><td>311.9</td></tr><tr><td>Water services</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Safely managed drinking water</td><td>33.0</td><td>76.0</td><td>93.0</td><td>97.0</td><td></td><td>75.0</td><td>73.0</td></tr><tr><td>Basic drinking water</td><td>66.0</td><td>95.0</td><td>98.0</td><td>100.0</td><td></td><td>98.0</td><td>91.0</td></tr><tr><td>Sanitation services</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Safely managed sanitation</td><td>26.0</td><td>59.0</td><td>79.0</td><td>96.0</td><td>73.0</td><td>49.0</td><td>57.0</td></tr><tr><td>Basic sanitation</td><td>36.0</td><td>86.0</td><td>97.0</td><td>100.0</td><td>80.0</td><td>90.0</td><td>81.0</td></tr><tr><td>Children under 5 years</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Affected by wasting</td><td>5.8</td><td>9.3</td><td></td><td>0.2</td><td></td><td>1.4</td><td>6.8</td></tr><tr><td>Who are stunted</td><td>30.0</td><td>22.3</td><td>4.0</td><td>3.6</td><td>22.0</td><td>11.5</td><td>22.3</td></tr><tr><td>Who are overweight</td><td>4.9</td><td>5.1</td><td>7.3</td><td>8.2</td><td>16.8</td><td>8.6</td><td>5.6</td></tr><tr><td>Affected by wasting (million)</td><td>12.2</td><td>31.6</td><td></td><td></td><td></td><td>0.7</td><td>45.0</td></tr><tr><td>Who are stunted (million)</td><td>63.1</td><td>76.6</td><td>1.4</td><td>0.7</td><td>0.8</td><td>5.7</td><td>148.1</td></tr><tr><td>Who are overweight (million)</td><td>10.2</td><td>17.7</td><td>2.6</td><td>1.7</td><td>0.6</td><td>4.2</td><td>37.0</td></tr><tr><td>Obesity</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Adult population</td><td>16.2</td><td>10.4</td><td>21.4</td><td>40.3</td><td>29.5</td><td>29.9</td><td>15.8</td></tr><tr><td>Adult population (million)</td><td>123.9</td><td>353.9</td><td>129.0</td><td>119.2</td><td>9.6</td><td>141.4</td><td>880.7</td></tr></tbody></table>

All statistics are expressed in %, except those whose units are indicated.

Source: www.fao.org/faostat/en/#data/FS & Author's calculations.

**Remark 59** As discussed in Section 2.1.1 on page 54, food security encompasses many dimensions and issues. Here, we focus on two critical aspects: genetic diversity and the capacity to feed the global population. In a fragmented world, a third important dimension is each country's dependence on external sources for its food supply.
### 5.4.3 Biodiversity threats and risks

Primack (2014) categorizes different threats to biodiversity into three main clusters: (1) extinction, (2) habitat destruction, fragmentation, degradation, and global climate change, and (3) overexploitation, invasive species, and disease. Sodhi and Ehrlich (2010) consider a similar list of biodiversity risks but groups them differently: (1) extinction, (2) habitat loss and fragmentation, (3) overexploitation, (4) invasive species, (5) climate change, and (6) fire. In reality, these factors often interact with each other, amplifying their combined effects. Moreover, it is important to distinguish between biodiversity threats and biodiversity risks:

* Biodiversity threats are the specific drivers or causes of biodiversity loss. They are external factors that contribute directly or indirectly to biodiversity loss by impacting species, habitats, and ecosystems. For example, invasive species can severely disrupt an ecosystem and therefore constitute a threat to biodiversity.

* Biodiversity risks refer to the potential consequences of biodiversity loss due to these threats. Specifically, biodiversity risks encompass the likelihood and severity of negative impacts on human life and ecosystem functions. For example, the emergence of new diseases as a result of ecosystem disturbances is a biodiversity risk.

The distinction between biodiversity threats and biodiversity risks is not always clear. For example, habitat loss is a threat because it directly reduces biodiversity and disrupts ecosystems. But it is also a risk because activities such as agriculture and land-use change contribute to further habitat loss. Similarly, pollution can be both a threat and a risk to biodiversity. It is a direct factor in biodiversity loss, but it can also result from that loss. In this context, the boundary between threats and risks to biodiversity can be somewhat artificial. Furthermore, these different threats are often interrelated, and the same is true of risks. In addition, feedback loops can complicate the distinction between the two concepts. For instance, habitat fragmentation may reduce biodiversity, but the resulting loss of biodiversity may in turn exacerbate further habitat fragmentation. According to Wilson (1989), the central cause of biodiversity risk is the destruction of natural ecosystems by human beings. He identified five major threats: habitat destruction, overexploitation, pollution, global climate change and invasion by introduced species. He argued that these five causes are driven by underlying social conditions, in particular increased per capita consumption, rapid population growth and unsound economic policies. Since this seminal paper on biodiversity loss, these key elements have been extensively studied and documented by scientists, showing that the Earth's ecosystems are dominated by humans (Vitousek et al., 1997), and that humans are ultimately the cause of the current biodiversity crisis. Land-use change is undoubtedly the greatest threat (Sala et al., 2000), while the decline in species richness is the most visible risk.

The possibility of a sixth mass extinction (Late Quaternary Mass Extinction or LQME), has been raised before<fnref n="109" />. According to Algeo and Shen (2024), the LQME differs from previous extinctions because it is driven by technological advances, particularly those that have allowed humans to exert widespread influence over the Earth's climate and ecosystems. The extinction process occurred in phases. In the first phase (50 Kyr BP–1750 AD), overhunting and the loss of top predators led to the extinction of many large animal species, especially after human expansion into new regions such as Australasia and the Americas. The first phase is characterized by limited global impacts (less than 1% of species loss) but significant ecosystem impacts. The current phase (1750 AD to present) is driven by habitat loss as humans convert land for agriculture and urbanization, with invasive species and climate change also contributing to species loss. The current phase is characterized by current

<footnote n="109">¹⁰⁹See the section on page 384 dedicated to the LQME.</footnote>
<figure><img src="image_199.png" /><figcaption>Figure 5.53: Stages of the Late Quaternary mass extinction</figcaption></figure>

Stage 1 (from ~ 50 to 0.25 ka), characterized by direct exploitation of species, comprised megafaunal extinctions in (A) Australasia, (B) the Americas and (C) the Indo-Pacific region. Stage 2 (from ~ 0.25 ka to the near future) is dominated by extinctions due to habitat loss. Stages 3 and 4 (future, timeline speculative) will be marked by climate change and ecosystem collapse, respectively, as the dominant proximate causes of extinction, while invasive species will play a supporting role during Stages 2 to 4.

Source: Algeo and Shen (2024, Figure 5, page 11).

losses of 1–2% of total biodiversity, but the extinction rate is about 1000 times the background extinction rate. Algeo and Shen (2024) suggest that the near future phase will be dominated by climate change, which will become the primary driver of biodiversity loss, while they estimate that the final phase could lead to widespread ecosystem collapse. In this case, the final phase could be characterized by a rapid and extensive loss of biodiversity, comparable to Earth’s largest historical extinction events. Figure 5.53 illustrates this worst-case scenario of the sixth mass extinction.

**Remark 60** *World Economic Forum (2020b) published the New Nature Economy Report to address the growing threats posed by biodiversity loss and the current biodiversity crisis to economies and businesses worldwide. The report emphasizes that biodiversity decline is not only an environmental issue but also a significant economic risk. The report outlines 15 major threats to biodiversity and identifies 15 critical transitions across three socio-economic systems. These threats are linked to issues such as habitat loss, overexploitation, and pollution, which pose not only risks to human welfare but also substantial risks to our economic systems. The proposed transitions aim to reduce nature-related risks while unlocking economic opportunities, potentially generating $10 trillion in economic value and creating 395 million jobs by 2030.*
In 2000, the World Economic Forum issued an alarming report on the impact of business on biodiversity and biodiversity on business (double materiality). This report highlights three key socio-economic systems: (1) food, land and ocean use, (2) infrastructure and the built environment, and (3) energy and extractives. These systems contribute 12%, 40% and 23% of global GDP, respectively, and employ 40%, 18% and 16% of the world's workforce. They also exert the greatest pressure on biodiversity, with 80% of threatened and near-threatened species at risk from activities in these sectors, and 72% affected by the food, land and ocean use sector alone. The WEF estimates that over half of the global GDP (around $44 trillion in economic value) relies heavily or moderately on natural resources. It also identifies 15 major pressing business-related threats to nature:

* Food, land and ocean use: (1) annual and perennial non-timber crops, (2) logging and wood harvesting, (3) livestock farming and ranching, (4) invasive non-native/alien species/diseases, (5) fire and fire suppression, (6) agricultural and forestry effluents, (7a) water management/use*, (8) fishing and aquatic resources;

* Infrastructure and the built environment: (9) housing and urban areas, (10) tourism and recreational areas, (11) domestic and urban wastewater, (12) roads and railroads, (13) commercial and industrial areas, (14) industrial and military effluents;

* Energy and extractives: (15) mining and quarrying, (7b) dams*.

In a similar way, the WEF identities 15 key socio-economic transitions needed to tackle the nature crisis:

* Food, land and ocean use: (1) ecosystem restoration and avoided expansion, (2) productive and regenerative agriculture, (3) healthy and productive ocean, (4) sustainable management of forests, (5) planet-compatible consumption, (6) transparent and sustainable supply chains;

* Infrastructure and the built environment: (7) densification of the urban environment, (8) nature-positive built environment design, (9) planet-compatible urban utilities, (10) nature as infrastructure, (11) nature-positive connecting infrastructure;

* Energy and extractives: (12) circular and resource efficient models, (13) nature-positive metals and minerals extraction, (14) sustainable materials supply chains, (15) nature-positive energy transition.

<footnote>*Dams and water management/use are treated as separate threats because they involve different economic activities.</footnote>
Box 5.14: New Nature Economy Report II (continued)

The capital investment required to implement these fifteen transitions across the three systems is approximately $2.7 trillion per year, which the following breakdown: 16% for the six transitions related to food, land and ocean use, 64% for the five transitions related to infrastructure and the built environment, and 31% for the four transitions related to energy and extractives. However, implementing these transitions could generate $10 trillion in business opportunities and create approximately 400 million jobs by 2030. Among these, the food, land, and ocean use system is expected to yield the greatest socio-economic benefits.

<table><caption>Table 5.C: Key figures of the New Nature Economy Report II</caption><thead><tr><th>System</th><th>Description</th><th>Share of threatened species impacted</th><th>Threats</th><th>Total business opportunities by 2030</th><th>Total jobs by 2030</th><th>Annualized investment costs (2020-2030)</th></tr></thead><tbody><tr><td></td><td>Food, land and ocean use</td><td>72%</td><td>8</td><td>$3,565 bn</td><td>191 mn</td><td>$440 bn</td></tr><tr><td></td><td>Infrastructure and the built environment</td><td>29%</td><td>6</td><td>$3,015 bn</td><td>117 mn</td><td>$1,430 bn</td></tr><tr><td></td><td>Energy and extractives</td><td>18%</td><td>2</td><td>$3,530 bn</td><td>87 mn</td><td>$840 bn</td></tr><tr><td></td><td>Total† of the three socio-economic systems</td><td>79%</td><td>15</td><td>$10,110 bn</td><td>395 mn</td><td>2,710 bn</td></tr></tbody></table>

<figure><img src="image_200.png" /></figure>

<figure><img src="image_201.png" /></figure>

<figure><img src="image_202.png" /></figure>

<figure><img src="image_203.png" /></figure>

<footnote>†Because some species are affected by more than one of the three systems, the percentage of species impacted by all systems is less than the sum of the percentages impacted by each individual system. The total number of threats is also 15 because water management/use and dams are considered as a single threat.</footnote>
<figure><img src="image_204.png" /><figcaption>Figure 5.54: Habitat loss vs. fragmentation vs. degradation</figcaption></figure>

## Habitat loss, fragmentation and degradation

Habitat loss, fragmentation, and degradation is the largest cause of biodiversity loss and the primary threat leading to species extinction. Written in the singular form, this sentence suggests that these three processes are interconnected or form a single, overarching factor. However, it's important to distinguish between them:

* Habitat loss occurs when a natural habitat is completely removed, destroyed, or converted to another use, resulting in the disappearance of species that previously lived there. For example, deforestation — transforming a forest into cropland or an urban area — is a typical case of habitat loss. This process is the most severe form of habitat disruption, because it eliminates the existing living space and is often irreversible.

* Habitat fragmentation occurs when a large, continuous habitat is divided into smaller, isolated patches by human-made structures. Building highways through a forest, for instance, is a typical example of habitat fragmentation. Fragmentation creates habitat islands that may be too small to support the original populations, increasing the risk of species decline or extinction.

* Habitat degradation is the process by which the quality of a habitat is damaged or reduced, making it less suitable for the species that live there. Factors such as pollution, water contamination, water stress, climate change or invasive species often contribute to degradation. In such cases, species may become endangered as their populations decline or suffer from poor health.

In summary, loss is the total elimination of a habitat, fragmentation is the breaking up of a habitat into smaller habitats, and degradation is the decline in habitat quality (Figure 5.54). The dis-
tinction between these three concepts and the definition of habitat fragmentation were popularized by *Fahrig* (2003). In her research, Lenore Fahrig challenges common perceptions of habitat fragmentation. She argues that while habitat loss typically has detrimental effects on biodiversity, the effects of fragmentation itself<fnref n="110" /> are more nuanced. In particular, fragmentation can sometimes have neutral or even potentially beneficial consequences for some species by creating more diverse landscape configurations that increase habitat heterogeneity and enhance dispersal opportunities (Fahrig, 2017):

> "I found 118 studies reporting 381 significant responses to habitat fragmentation independent of habitat amount. Of these responses, 76% were positive. Most significant fragmentation effects were positive, irrespective of how the authors controlled for habitat amount, the measure of fragmentation, the taxonomic group, the type of response variable, or the degree of specialization or conservation status of the species or species group. [...] Thus, although 24% of significant responses to habitat fragmentation were negative, I found no conditions in which most responses were negative. Authors suggest a wide range of possible explanations for significant positive responses to habitat fragmentation: increased functional connectivity, habitat diversity, positive edge effects, stability of predator-prey/host-parasitoid systems, reduced competition, spreading of risk, and landscape complementation." (Fahrig, 2017, page 1).

This conclusion contrasts sharply with the findings of Haddad et al. (2015), who reported very negative impacts:

> "We conducted an analysis of global forest cover to reveal that 70% of remaining forest is within 1 km of the forest's edge, subject to the degrading effects of fragmentation. A synthesis of fragmentation experiments spanning multiple biomes and scales, five continents, and 35 years demonstrates that habitat fragmentation reduces biodiversity by 13 to 75% and impairs key ecosystem functions by decreasing biomass and altering nutrient cycles." (Haddad et al., 2015, page 1).

These conflicting results have fueled a lively debate in the journal *Biological Conservation*, reflected in two contrasting publications: *Is Habitat Fragmentation Good for Biodiversity?* (Fletcher et al., 2018) and *Is Habitat Fragmentation Bad for Biodiversity?* (Fahrig et al., 2019). To this day, the debate remains open, especially when we consider human pressures and matrix conditions<fnref n="111" /> (Ramírez-Delgado et al., 2022).

**Theory of island biogeography** This theory (known as TIB) was proposed by MacArthur and Wilson (1967) to explain the factors that influence biodiversity on islands. They emphasised the importance of studying islands because they are less complex and more common than larger ecosystems, meaning that research can be easily replicated. Their theory describes how the size of an island and its distance from the mainland affect the balance between immigration and extinction rates of species. This balance ultimately determines an equilibrium, or the number and types of species an island can support. An illustration is provided in Figure 5.55. Two key elements characterize the theory of island biogeography. First, the distance from the mainland measures the isolation of the island. Indeed, islands closer to the mainland have more species because they are easier for species

<footnote n="110">¹¹⁰We generally speak of fragmentation ‘per se’, i.e., fragmentation independent of habitat loss.</footnote>

<footnote n="111">¹¹¹In the context of biodiversity, the matrix condition refers to the quality and characteristics of the landscape that surrounds habitat patches or fragments. It's essentially the non-habitat environment that separates discrete areas of suitable habitat.</footnote>
to reach, receive colonizers more frequently, and so on. Second, the size of the island determines species richness. Larger islands support greater species diversity because they offer more diverse habitats, contain more resources, and so forth. Specifically, MacArthur and Wilson (1967) proposed that the immigration rate<fnref n="112" /> λ (t) decreases with the number of species present and the isolation of the island (first panel in Figure 5.55), while the extinction rate μ (t) increases with the number of species present but decreases with the size of the island (second panel in Figure 5.55). Equilibrium occurs when immigration and extinction rates are equal, implying that the net diversification rate is zero: δ (t) = λ (t) − μ (t) = 0 (third panel in Figure 5.55).

<figure><img src="image_205.png" /><figcaption>Figure 5.55: Theory of island biogeography</figcaption></figure>

**Species-area relationship** The previous equilibrium model is often associated with the species-area relationship (SAR). Since extinction dynamics are influenced by the size of an island, an increase in island size shifts the equilibrium point to the right on the immigration-extinction diagram, indicating higher species richness. However, the rate of increase in species richness is generally slower than the rate of increase in area<fnref n="113" /> (fourth panel in Figure 5.55). This reflects “an ecological version of the law of diminishing returns” (Lomolino, 2001): as area increases, each additional unit of area contributes proportionally less to species diversity. Certainly, the most famous species-area relationship is the power model originally formulated by Arrhenius (1921):

$$S = cA^z \qquad (5.18)$$

where S is species richness, c is a constant, A is the area of the island and z is the slope of the log-log A-S curve. Typical values of z are between 0.15 and 0.35. If the areas of the original and

<footnote n="112">¹¹²In the theory of island biogeography, the immigration rate corresponds to the origination rate in the birth-death model.</footnote>

<footnote n="113">¹¹³See Exercise 5.5.2 on page 566 for a mathematical derivation of the species-area relationship from the theory of island biogeography.</footnote>
current habitats are $A_0$ and $A$, respectively, we obtain the following survival function:

$$ \frac{S}{S_0} = \left( \frac{A}{A_0} \right)^z $$

This equation is used by Pimm et al. (1995) to calibrate species loss due to habitat loss:

$$ \mathcal{L}oss_{species} = 1 - (1 - \mathcal{L}oss_{habitat})^z \quad (5.19) $$

Assuming $z = 0.25$, Pimm et al. (1995, page 349) found that 50% of habitat loss should reduce the number of species by 15.91%, while 90% of habitat loss leads to 43.77% of species loss (Table 5.41).

<table><caption>Table 5.41: Species loss $Loss_{species}$</caption><thead><tr><th rowspan="2">z</th><th colspan="6">Loss<sub>habitat</sub></th></tr><tr><th>0.00%</th><th>25.00%</th><th>50.00%</th><th>75.00%</th><th>90.00%</th><th>100.00%</th></tr></thead><tbody><tr><td>0.05</td><td>0.00%</td><td>1.43%</td><td>3.41%</td><td>6.70%</td><td>10.87%</td><td>100.00%</td></tr><tr><td>0.10</td><td>0.00%</td><td>2.84%</td><td>6.70%</td><td>12.94%</td><td>20.57%</td><td>100.00%</td></tr><tr><td>0.25</td><td>0.00%</td><td>6.94%</td><td>15.91%</td><td>29.29%</td><td>43.77%</td><td>100.00%</td></tr><tr><td>0.35</td><td>0.00%</td><td>9.58%</td><td>21.54%</td><td>38.44%</td><td>55.33%</td><td>100.00%</td></tr></tbody></table>

While ecologists are nearly unanimous in accepting the principles of the species-area relationship<fnref n="114" />, they do not always agree on the specific form it should take. An alternative to Equation (5.18) is the exponential model proposed by Gleason (1922):

$$ S = c + z \ln(A) \quad (5.20) $$

These two models (power and exponential) were extensively studied in the seminal review by Connor and McCoy (1979). The authors provided a comprehensive and critical analysis of the species-area curve, arguing that many estimated SAR curves could be explained by statistical sampling effects rather than underlying biological mechanisms. Therefore, they emphasized the importance of distinguishing between ecological drivers (e.g., habitat diversity, island biogeography) and statistical drivers (e.g., sampling bias, incomplete sampling, inappropriate statistical methods) of SAR. By examining 100 datasets, they showed that the value of $z$ varies across scales and contexts (e.g., small versus large areas) and concluded that the species-area relationship is not a fixed law but rather a complex, context-dependent pattern. This variation depends on specific ecological settings, taxonomic groups, and geographic regions. In particular, they noted that the parameters $c$ and $z$ estimated for one island or archipelago cannot be reliably applied to another island or archipelago. Furthermore, reducing the species-area relationship to a function of a single variable with two parameters fails to account for several second-order factors, such as elevation, latitude, and the density of small- and large-bodied species. Nevertheless, the search for the best species-area relationship model has continued to fascinate researchers since the publication of Connor and McCoy's paper in 1979. While it is impossible to cite all of the subsequent studies, some have significantly shaped this field. For instance, the default value of $z = 0.25$ for the power model is largely due to the influential

<footnote n="114">¹¹⁴The relationship between species and area is considered to be a fundamental ecological principle:</footnote>

<footnote>> "It is common, perhaps axiomatic, to refer to the species-area relationship as one of ecology's few laws [...]. According to a very broad consensus of ecologists, the pattern has two principal features: 1) species richness of a given taxon increases with the area sampled, and 2) the rate of increase slows for the larger islands or patches of habitat." (Lomolino, 2001, page 1).</footnote>
book by Rosenzweig (1995). Similarly, Tjørve (2003) made an important contribution by organizing
a comparison of 14 statistical models to identify the best-fitting SAR model. More recently, Dengler
(2009) and Triantis et al. (2012) reviewed 23 and 20 functions, respectively, and concluded that no
single function universally outperforms all others. However, they emphasized that the power model
often provides the best fit and remains a robust choice when it does not. Triantis et al. (2012)
also identified three other simple models that can rival the power model for certain datasets: the
linear model (S = c + zA), the exponential model (S = c + z *ln(A)*), and the Kobayashi model
(S = c *ln(1 + zA)*).

<figure><img src="image_206.png" /><figcaption>Figure 5.56: Analyzing the species-area relationship in Mediterranean islands</figcaption></figure>

Source: Fattorini et al. (2017, Figure 1), created with www.paintmaps.com.

Let us now examine an empirical application to understand how the species-area relationship
is estimated. For this purpose, we consider the study<sup>115</sup> by Fattorini et al. (2017). The authors
estimated the SAR curves for two groups of Mediterranean islands: the Tyrrhenian Islands, located
in Italy, and the Aegean Islands, located between Greece and Turkey. In Figure 5.56, we present
the Tyrrhenian islands with areas larger than 10 km<sup>2</sup> and the Aegean islands with areas exceeding
100 km<sup>2</sup>. The Tyrrhenian islands are mainly located off the western coast of Italy, near regions such
as Tuscany, Lazio, Campania, Calabria, and Sicily. The dataset includes 47 islands, with the largest
being Elba (223 km<sup>2</sup>) and the smallest being Scoglietto di Portoferraio (24.9 m<sup>2</sup>). The Aegean Sea
is known for its multitude of islands, the total number of which is estimated to be around 6,000.
However, the dataset contains only 127 islands, with the largest being Rodos (1,401 km<sup>2</sup>) and the
smallest being Petallidi (6,000 m<sup>2</sup>). The results for various taxonomic groups (centipedes, isopods,
land snails, reptiles and tenebrionid beetles) are given in Table 5.42 and Figure 5.57. Fattorini et al.
(2017) found that *z* varies between 0.141 and 0.308, while *c* varies between 2.716 and 12.274 species
per km<sup>2</sup>. According to the authors, these results “demonstrate the importance of comparing SARs
either of different groups within the same area, or of the same group in different areas.”

<footnote n="115"><sup>115</sup>The advantage of this study is that the dataset is relatively small, and the results are easily reproducible since the data are freely available on the paper’s webpage: onlinelibrary.wiley.com/doi/abs/10.1111/jbi.12874.</footnote>
<table><caption>Table 5.42: Estimated values of *c*, *z*, and *S* for different area values *A*</caption><thead><tr><th rowspan="2">Islands</th><th rowspan="2">Species</th><th colspan="7">Area A (in km²)</th></tr><tr><th>c</th><th>z</th><th>0.01</th><th>0.10</th><th>1</th><th>10</th><th>100</th><th>1000</th></tr></thead><tbody><tr><td rowspan="5">Tyrrhenian</td><td>Centipedes</td><td>6.281</td><td>0.308</td><td>1.52</td><td>3.09</td><td>6.28</td><td>12.77</td><td>25.94</td><td>52.73</td></tr><tr><td>Isopods</td><td>9.226</td><td>0.262</td><td>2.76</td><td>5.05</td><td>9.23</td><td>16.87</td><td>30.83</td><td>56.36</td></tr><tr><td>Land snails</td><td>12.274</td><td>0.225</td><td>4.35</td><td>7.31</td><td>12.27</td><td>20.61</td><td>34.59</td><td>58.08</td></tr><tr><td>Reptiles</td><td>3.357</td><td>0.141</td><td>1.75</td><td>2.43</td><td>3.36</td><td>4.64</td><td>6.43</td><td>8.89</td></tr><tr><td>Tenebrionids</td><td>8.610</td><td>0.270</td><td>2.48</td><td>4.62</td><td>8.61</td><td>16.03</td><td>29.85</td><td>55.59</td></tr><tr><td rowspan="5">Aegean</td><td>Centipedes</td><td>3.864</td><td>0.243</td><td>1.26</td><td>2.21</td><td>3.86</td><td>6.76</td><td>11.83</td><td>20.70</td></tr><tr><td>Isopods</td><td>9.354</td><td>0.203</td><td>3.67</td><td>5.86</td><td>9.35</td><td>14.93</td><td>23.82</td><td>38.02</td></tr><tr><td>Land snails</td><td>9.572</td><td>0.184</td><td>4.10</td><td>6.27</td><td>9.57</td><td>14.62</td><td>22.34</td><td>34.12</td></tr><tr><td>Reptiles</td><td>2.716</td><td>0.278</td><td>0.75</td><td>1.43</td><td>2.72</td><td>5.15</td><td>9.77</td><td>18.53</td></tr><tr><td>Tenebrionids</td><td>4.055</td><td>0.268</td><td>1.18</td><td>2.19</td><td>4.05</td><td>7.52</td><td>13.93</td><td>25.82</td></tr></tbody></table>

Source: Fattorini et al. (2017, Table 1).

<figure><img src="image_207.png" /><figcaption>Figure 5.57: SAR curves in Tyrrhenian and Aegean islands</figcaption></figure>

Source: Fattorini et al. (2017) & Author's calculations.
**Example 24** Table 5.43 shows the data used by Fattorini et al. (2017, Appendix S1) to estimate the SAR curve of land snail species in the Tyrrhenian Islands. The area A is expressed in km².

<table><caption>Table 5.43: Number of species of land snails in the Tyrrhenian islands</caption><thead><tr><th>Island</th><th>A</th><th>S</th><th>Island</th><th>A</th><th>S</th><th>Island</th><th>A</th><th>S</th></tr></thead><tbody><tr><td>Alicudi</td><td>5.000</td><td>11</td><td>Filicudi</td><td>9.000</td><td>20</td><td>Montecristo</td><td>10.430</td><td>18</td></tr><tr><td>Argentarola</td><td>0.012</td><td>4</td><td>Formica di Grosseto</td><td>0.145</td><td>5</td><td>Panarea</td><td>3.380</td><td>22</td></tr><tr><td>Basiluzzo</td><td>0.270</td><td>11</td><td>Giannutri</td><td>2.390</td><td>19</td><td>Pianosa</td><td>10.410</td><td>28</td></tr><tr><td>Capraia</td><td>19.000</td><td>18</td><td>Giglio</td><td>21.000</td><td>35</td><td>Salina</td><td>26.000</td><td>25</td></tr><tr><td>Cerboli</td><td>0.051</td><td>8</td><td>Gorgona</td><td>2.270</td><td>24</td><td>Stromboli</td><td>12.000</td><td>18</td></tr><tr><td>Elba</td><td>223.000</td><td>50</td><td>Lipari</td><td>37.000</td><td>28</td><td>Vulcano</td><td>21.000</td><td>13</td></tr></tbody></table>

Source: Fattorini et al. (2017, Appendix S1).

Since the equation $S = cA^z$ is equivalent to $\ln(S) = \ln(c) + z \ln(A)$, the species-area relationship is generally estimated using the log-log regression model:

$$Y_i = \beta^\top X_i + \varepsilon_i$$

where $i = 1, \dots, n$, $n$ is the number of observations, $S_i$ and $A_i$ are the number of species and the area of the $i$th observation, $Y_i = \ln(S_i)$, $X_i = (1 \ \ln(A_i))$, $\beta = \begin{pmatrix} \ln(c) \\ z \end{pmatrix}$ and $\varepsilon_i \sim N(0, \sigma_\varepsilon^2)$ is the noise process. We have:

$$ \begin{aligned} \hat{\beta} &= (X^{\top}X)^{-1} X^{\top}Y \\ &= \begin{pmatrix} n & \sum_{i=1}^{n} \ln(A_i) & \sum_{i=1}^{n} \ln^2(A_i) \\ \sum_{i=1}^{n} \ln(A_i) & \sum_{i=1}^{n} \ln^2(A_i) & \end{pmatrix}^{-1} \begin{pmatrix} \sum_{i=1}^{n} \ln(S_i) \\ \sum_{i=1}^{n} \ln(A_i) \ln(S_i) \end{pmatrix} \\ &= \begin{pmatrix} 18 & 24.5506 & 141.5006 \\ 24.5506 & 24.5506 & 141.5006 \end{pmatrix}^{-1} \begin{pmatrix} 50.6670 \\ 93.3758 \end{pmatrix} \\ &= \begin{pmatrix} 2.5084 \\ 0.2247 \end{pmatrix} \end{aligned} $$

We deduce that $\hat{c} = \exp(\hat{\beta}_1) = 12.285$ and $\hat{z} = \hat{\beta}_2 = 0.225$. In Table 5.44, we compare the figures presented in the research paper (columns c and z) and our estimates (columns $\hat{\beta}_1$, $\exp(\hat{\beta}_1)$ and $\hat{\beta}_2$). We get the same values for z and very close values for c, the difference being due to rounding.

<table><caption>Table 5.44: Comparison of SAR values: research paper results vs. our estimates</caption><thead><tr><th rowspan="2">Species</th><th colspan="6">Tyrrhenian islands</th><th colspan="6">Aegean islands</th></tr><tr><th>n</th><th>c</th><th>z</th><th>β̂<sub>1</sub></th><th>eβ̂<sub>1</sub></th><th>β̂<sub>2</sub></th><th>n</th><th>c</th><th>z</th><th>β̂<sub>1</sub></th><th>eβ̂<sub>1</sub></th><th>β̂<sub>2</sub></th></tr></thead><tbody><tr><td>Centipedes</td><td>32</td><td>6.281</td><td>0.308</td><td>1.837</td><td>6.275</td><td>0.308</td><td>43</td><td>3.864</td><td>0.243</td><td>1.352</td><td>3.865</td><td>0.243</td></tr><tr><td>Isopods</td><td>28</td><td>9.226</td><td>0.262</td><td>2.221</td><td>9.216</td><td>0.262</td><td>43</td><td>9.354</td><td>0.203</td><td>2.236</td><td>9.360</td><td>0.203</td></tr><tr><td>Land snails</td><td>18</td><td>12.274</td><td>0.225</td><td>2.508</td><td>12.285</td><td>0.225</td><td>65</td><td>9.572</td><td>0.184</td><td>2.258</td><td>9.567</td><td>0.184</td></tr><tr><td>Reptiles</td><td>28</td><td>3.357</td><td>0.141</td><td>1.212</td><td>3.360</td><td>0.141</td><td>56</td><td>2.716</td><td>0.278</td><td>1.001</td><td>2.720</td><td>0.277</td></tr><tr><td>Tenebrionids</td><td>46</td><td>8.610</td><td>0.270</td><td>2.153</td><td>8.607</td><td>0.270</td><td>32</td><td>4.055</td><td>0.268</td><td>1.417</td><td>4.125</td><td>0.265</td></tr></tbody></table>

Source: Fattorini et al. (2017, Table 1) & Author's calculations.
We have seen how the species-area relationship can be used to measure the effect of habitat loss, i.e., when area A is reduced. Hanski et al. (2013) suggested reformulating Equation (5.18) to account for habitat fragmentation. Let $\varphi$ be the degree of fragmentation. The authors introduce the function $P(\varphi)$, which measures the fraction of the species that are expected to persist when the degree of fragmentation of A is given by $\varphi$ and assume that $P(\varphi) = e^{-b\varphi}$ where b is a parameter. We have $P(0) = 1$ and $P(\infty) = 0$. We deduce that the species-area relationship becomes:

$$S = cA^zP(\varphi) = cA^z e^{-b\varphi} \qquad (5.21)$$

If the initial area $A_0$ is reduced to A, we have:

$$\frac{S}{S_0} = \left(\frac{A}{A_0}\right)^z e^{-b(\varphi-\varphi_0)}$$

where $\varphi_0$ is the degree of fragmentation of $A_0$. We deduce that:

$$\mathcal{L}oss_{\text{species}} = 1 - (1 - \mathcal{L}oss_{\text{habitat}})^z e^{-b \Delta_{\text{frag}}} \qquad (5.22)$$

where $\Delta_{frag} = \varphi - \varphi_0$ is the variation in the fragmentation. When $\varphi = \varphi_0$, we retrieve Equation (5.20).

**Example 25** We assume that $c = 10$, $z = 0.25$ and $b = 0.10$. We consider two initial landscapes, each with an initial area $A_0$ equal to 100 km$^2$. The first landscape is unfragmented ($\varphi_0 = 0$), while the second landscape is fragmented ($\varphi_0 = 0.50$). Both landscapes experience a habitat loss of 50%. In the first landscape, this habitat reduction is accompanied by habitat fragmentation ($\varphi = 0.50$), while in the second landscape we observe an improvement in habitat quality ($\varphi = 0.25$).

We obtain the following results:

<table><thead><tr><th>Landscape</th><th>#1</th><th>#2</th></tr></thead><tbody><tr><td>$S_0$</td><td>31.6228</td><td>30.0805</td></tr><tr><td>$S$</td><td>25.2946</td><td>25.9349</td></tr><tr><td>$Loss_{\text{species}}$</td><td>20.01%</td><td>13.78%</td></tr></tbody></table>

Normally, the impact of habitat loss alone on the species loss is 0.50$^{0.25}$, or 15.91%. However, the species loss is higher in the first landscape because habitat fragmentation has increased. In contrast, the species loss is lower in the second landscape because the fragmentation has increased.

**Species distribution, sampling and endemics-area curve** The species-area relationship describes the impact of area size on the number of species. It is a simple two-parameter model and may be limited in its ability to capture the effects of habitat loss on species richness and biodiversity. In particular, it overlooks important aspects of biodiversity such as species evenness and abundance$^{116}$. It also ignores the spatial distribution of species and sampling effects. In this section, we explain these two effects, show how sampling affects the species-area relationship, and define species loss more precisely by introducing the endemics-area relationship.

<footnote>$^{116}$Abundance refers to the total number of individuals per species or in an ecosystem, while evenness is the degree to which individuals are evenly distributed across species.</footnote>
**Species abundance models** We consider a region or community<fnref n="117" /> with *S* species. For each species *i*, the number of individuals is denoted by *n*ᵢ. The species abundance can be described by a traditional frequency distribution table<fnref n="118" />:

<table><thead><tr><th>Species</th><th>1</th><th>2</th><th>...</th><th><i>i</i></th><th>...</th><th><i>S</i></th></tr></thead><tbody><tr><th>Frequency</th><td><i>n</i><sub>1</sub></td><td><i>n</i><sub>2</sub></td><td></td><td><i>n</i><sub><i>i</i></sub></td><td></td><td><i>n</i><sub><i>S</i></sub></td></tr></tbody></table>

Another approach is to calculate the species abundance distribution (SAD), which summarizes the number of species by their abundance (McGill et al., 2007):

<table><thead><tr><th>Number of individuals</th><th>1</th><th>2</th><th>...</th><th><i>j</i></th><th>...</th><th><i>n</i><sup>+</sup></th></tr></thead><tbody><tr><th>Number of species</th><td><i>s</i>(1)</td><td><i>s</i>(2)</td><td></td><td><i>s</i>(<i>j</i>)</td><td></td><td><i>s</i>(<i>n</i><sup>+</sup>)</td></tr></tbody></table>

where *s*(*j*) is the number of species with *j* individuals, and *n*<sup>+</sup> = max *n*ᵢ is the maximum number of individuals found in any single species in the community. Mathematically, we have:

$$s(j) = \sum_{i=1}^{S} 1 \{n_i = j\}$$

The two representations — the frequency table and the species abundance distribution — then convey the same information about the structure of the community. However, it is not possible to retrieve the exact value of *n*ᵢ from the SAD. This is because individual species are not identified in the SAD, unlike in the frequency table, where species are explicitly listed.

**Example 26** We consider a community consisting of 25 species and 407 individuals, distributed across a region of 2 km². The abundances of the 25 species are as follows: 2, 10, 13, 2, 1, 5, 25, 17, 1, 4, 28, 117, 23, 10, 13, 1, 4, 3, 10, 5, 7, 70, 10, 25, 1.

There are four species with only one individual, so *s* (1) = 4. Similarly, *s* (2) = 2 because there are two species with two individuals each. For *s* (3), we have *s* (3) = 1, as only one species has three individuals, and so on. Finally, we get the resulting species abundance distribution:

<table><thead><tr><th><i>j</i></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>7</th><th>10</th><th>13</th><th>17</th><th>23</th><th>25</th><th>28</th><th>70</th><th>117</th></tr></thead><tbody><tr><th><i>s</i>(<i>j</i>)</th><td>4</td><td>2</td><td>1</td><td>2</td><td>2</td><td>1</td><td>4</td><td>2</td><td>1</td><td>1</td><td>2</td><td>1</td><td>1</td><td>1</td></tr></tbody></table>

We confirm that the total abundance is exactly 407 individuals<fnref n="119" />. In practice, the label *j* is omitted when *s*(*j*) = 0 (McGill et al., 2007). However, when the number of species is large, the species abundance distribution can become highly atomic or fragmented, with many values of *s*(*j*) = 1. To address this, it is important to group the number of individuals into classes. We have *s*(*C*<sub>c</sub>) = Σ<sub>j∈(*C*<sub>c</sub>)</sub> *s*(*j*) where *C*<sub>c</sub> represents the *c*<sup>th</sup> grouping class. Here is an example where we group species whose number of individuals is greater than 5:

<table><thead><tr><th><i>C</i><sub>c</sub></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6–10</th><th>11–25</th><th>26–50</th><th>51–100</th><th>100–117</th></tr></thead><tbody><tr><th><i>s</i>(<i>C</i><sub>c</sub>)</th><td>4</td><td>2</td><td>1</td><td>2</td><td>2</td><td>5</td><td>6</td><td>1</td><td>1</td><td>1</td></tr></tbody></table>

$$\sum_{j=1}^{\infty} s(j) \cdot j = \sum_{i=1}^{S} n_i = n$$

<footnote n="117">¹¹⁷A community is the total set of species in an ecosystem.</footnote>

<footnote n="118">¹¹⁸It is called the rank-abundance distribution when $n_i$ are sorted (in ascending or descending order).</footnote>

<footnote n="119">¹¹⁹We have the following property:</footnote>
There are five species with an abundance of 6 to 10 individuals, six species with an abundance of 11 to 25 individuals, and so on. According to McGill et al. (2007, page 996), another way to illustrate the relative abundance and diversity of species within a community is to use a rank-abundance diagram (RAD), which is a graphical representation used in ecology to provide insights into species richness and species evenness. On the x-axis, species are ranked according to their abundance, from most abundant (rank 1) to least abundant (rank S), while on the y-axis, we plot relative abundance, that is the proportion of individuals belonging to each species. Mathematically, we calculate the order statistics of the set {$n_1, \dots, n_S$}:

$$ \underset{\mathcal{R}_S}{n_{1:S}} \le \underset{\mathcal{R}_{S-1}}{n_{2:S}} \le \dots \le \underset{\mathcal{R}_k}{n_{S-k+1:S}} \le \dots \le \underset{\mathcal{R}_1}{n_{S:S}} $$

We then assign the rank $\mathcal{R}_k$ to the order statistic $n_{S-k+1:S}$. The relative abundance $f_k$ associated with $\mathcal{R}_k$ is defined as $f_k = \frac{n_{S-k+1:S}}{n} = \frac{n_i}{n}$ where index $i$ is the solution to the equation $n_i = n_{S-k+1:S}$. By construction we have $f_1 = \frac{\max n_i}{n}$ and $f_S = \frac{\min n_i}{n}$. Looking at the previous example, we get the following rank-abundance distribution:

<table><thead><tr><th>k</th><th>1</th><th>2</th><th>3</th><th>4, 5</th><th>6</th><th>7</th><th>8, 9</th><th>...</th><th>19</th><th>20, 21</th><th>22-25</th></tr></thead><tbody><tr><th>i</th><td>12</td><td>22</td><td>11</td><td>7, 24</td><td>13</td><td>8</td><td>3, 15</td><td></td><td>18</td><td>1,4</td><td>5, 9, 16, 25</td></tr><tr><th>n<sub>i</sub></th><td>117</td><td>70</td><td>28</td><td>25</td><td>23</td><td>17</td><td>13</td><td>...</td><td>3</td><td>2</td><td>1</td></tr><tr><th>f<sub>k</sub> (in %)</th><td>28.75</td><td>17.20</td><td>6.88</td><td>6.14</td><td>5.65</td><td>4.18</td><td>3.19</td><td></td><td>0.74</td><td>0.49</td><td>0.25</td></tr></tbody></table>

Species 12 ranks first and represents 28.75% of the total abundance of the community. It is followed by species 22 and 11, whose relative abundance is 17.20% and 6.88%, respectively.

In 1949–1951, Robert H. Whittaker studied plant community composition in the Siskiyou Mountains to understand variation across environmental gradients. He developed the concept of diversity partitioning. His work, published in 1960, led to extensive data analyses and contributed significantly to the study of community structure (Whittaker, 1960). Since 2022, the data are freely available at https://doi.org/10.6084/m9.fishshare.19661094.v3 (Whittaker et al., 2022). We use the tree data CSV file, which contains 2078 observations with the following information:

* The first column represents the sample number, corresponding to one of the 359 sampling locations;

* The second column lists the species name;

* The third column gives the number of individuals of each species in the sample;

* The fourth column indicates the number of large individuals with a diameter at breast height<fnref n="120" /> (DBH) greater than 30 inches (76.2 cm).

For each species, we pooled the data from the 359 sampling sites. We obtain the abundance of 59 tree species. The five most represented trees are *Lithocarpus densiflorus*, *Quercus chrysolepis*, *Abies concolor*, *Pseudotsuga menziesii* and *Chrysolepis chrysophylla*. Their abundance is 8439, 4207, 4172, 3725 and 1886, respectively. The species abundance distribution is shown in Figures 5.58 and 5.59. The species abundance curve is constructed with classes of length 50. Most of these classes have one or two species, except for the first three classes: 22 species for the first class (between 1 and 50 individuals), 7 species for the second class (between 51 and 100 individuals), and 3 species for the third class (between 101 and 150 individuals).

<footnote n="120">¹²⁰In the United States, DBH refers to the diameter of the tree measured 4.5 feet above the ground.</footnote>
<figure><img src="image_208.png" /><figcaption>Figure 5.59: Rank-abundance curve (Whittaker plot)</figcaption></figure>

<figure><img src="image_209.png" /></figure>
<figure><img src="image_210.png" /><figcaption>Figure 5.60: Barro Colorado Island (Panama)</figcaption></figure>

There are many ways to plot a species abundance distribution. The species abundance curve (also called a frequency histogram) and the rank-abundance curve (Whittaker, 1965) are just a few examples. Other methods include the species rarefaction curve, the empirical cumulative distribution function (ECDF), the *k*-dominance plot, and the Robbins curve, among others (Matthews and Whittaker, 2015). Moreover, the axes of these graphs can be linear, but they are often transformed using $log_2$ or $log_{10}$ functions to better display the variability in the shape of the species abundance distribution. One particularly famous plot was proposed by Preston (1948), who popularized the use of a frequency histogram with $log_2$-based classes along the *x*-axis. In this approach, the number of species is grouped into intervals of $2^k$ (e.g., 1, 2, 4, 8, 16, 32, 64, etc.), called octaves. Using Example 26, we obtain the following results<fnref n="121" />:

<table><thead><tr><th>Octave k</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th></tr></thead><tbody><tr><th>C<sub>k</sub></th><td>1</td><td>2-3</td><td>4-7</td><td>8-15</td><td>16-31</td><td>32-63</td><td>64-127</td></tr><tr><th>s(k)</th><td>4</td><td>3</td><td>5</td><td>6</td><td>5</td><td>0</td><td>2</td></tr></tbody></table>

We now consider another community ecology dataset, the Barro Colorado Island (BCI) data, which was established by the Smithsonian Tropical Research Institute (STRI) and studied to understand the dynamics of tropical forest vegetation. The data contain 50 contiguous 1-ha plots and record tree species identity, spatial coordinates, size measurements, and environmental variables. Periodic censuses have been conducted every five years since 1980 until 2015. Such analysis and data collection has been done in many other regions of the world, especially in South America. In Figure 5.61 we show the species abundance distribution of the tropical forest tree dataset<fnref n="122" /> compiled by Condit et al. (2002). This dataset includes 100 plots from Barro Colorado Island in Panama, the Yasuni National Park in Ecuador and the Manu Biosphere Reserve in Peru. The histogram shows that many species are found in the first octaves. Preston (1948) proposed that species abundances

<footnote n="121">¹²¹The general formula for the range of each octave *k* is $2^{k-1}$ to $2^k - 1$. The first octave contains the species with one individual, the second octave contains the species with two and three individuals, the third octave contains the species with four to seven individuals, and so on.</footnote>

<footnote n="122">¹²²The conditwebtable.xls file is available at www.science.org/doi/abs/10.1126/science.1066854.</footnote>
in natural communities follow a log-normal distribution. When the logarithm of species abundances forms a bell-shaped curve, this pattern suggests that most species are moderately abundant, with fewer species being either extremely rare or extremely common. In some cases, when the positive skew of the log-normal distribution is very pronounced, the number of species decreases logarithmically with abundance, indicating that most species have only a few individuals. Preston (1948) further concluded that because many species in an ecological community are rare, they are often not observed in traditional ecological surveys<fnref n="123" />. This means that traditional sampling methods tend to underestimate total species richness, because rare species are likely to be missed in small samples.

<figure><img src="image_211.png" /><figcaption>Figure 5.61: Species abundance distribution of tropical forest trees</figcaption></figure>

Empirical analysis of the number of species has led to the formulation of several probability models of species abundance. In 1932, Isao Motomura developed the resource (or niche) preemption model, which assumes that one species preempts a fraction $\kappa \in (0, 1)$ of the resource, a second species the same fraction $\kappa$ of the remaining resource $(1 - \kappa)$, and so on. Assuming that the abundance of species $i$ is proportional to its share of the resource, i.e., $(1 - \kappa)^{i-1} \kappa$, we obtain the geometric rank-abundance model<fnref n="124" />:

$$n_i = \frac{n\kappa (1 - \kappa)^{i-1}}{1 - (1 - \kappa)^S} \qquad (5.23)$$

where $S$ is the number of species and $n$ is the total number of individuals. Another approach was proposed by Fisher et al. (1943), who developed a model where the number of species is derived

$$\sum_{i=1}^{S} n_i = \frac{n\kappa}{1 - (1 - \kappa)^S} \sum_{i=1}^{S} (1 - \kappa)^{i-1} = \frac{n\kappa}{1 - (1 - \kappa)^S} \cdot \frac{1 - (1 - \kappa)^S}{1 - (1 - \kappa)} = n$$

<footnote n="123">¹²³This concept is often called the ‘invisible fraction’ in ecology.</footnote>

<footnote n="124">¹²⁴The scaling factor ensures that the sum of the individuals equals the total abundance:</footnote>
from the limiting form of the negative binomial distribution $NB(r, p)$, excluding zero observations. They demonstrated that the expected number of species with exactly $j$ individuals is given by:

$$s(j) = \alpha \frac{x^j}{j} \qquad (5.24)$$

where $x = (1-p)^{-1} p \in (0,1)$, $p$ is the success probability of the negative binomial distribution, and $\alpha$ is a scaling factor that depends on the parameters $r$ and $p$. Since the total expected number of species is $S = \sum_{j=1}^{\infty} s(j) = -\alpha \ln(1-x)$ and the total expected number of individuals is $n = \sum_{j=1}^{\infty} s(j) \cdot j = \sum_{j=1}^{\infty} \alpha x^j = \alpha x / (1-x)$, the parameters of the log-series distribution $LS(\alpha, x)$ can be determined by solving the following system of equations<fnref n="125" />:

$$\left\{ \begin{array}{l} S = \alpha \ln \left( 1 + \frac{n}{\alpha} \right) \\ x = \frac{n}{\alpha + n} \end{array} \right. \qquad (5.25)$$

In chronological order, the third and fourth important SAD models are the log-normal model of Preston (1948), seen earlier on page 443, and the broken-stick model formulated by MacArthur (1957). In the log-normal model, the probability that a species has $j$ individuals is given by $Pr\{X = j\}$ where $X \sim LN(\mu, \sigma^2)$. In practice, $j$ is discrete rather than continuous. Considering the disjoint intervals $[j - \frac{1}{2}, j + \frac{1}{2}]$, we get<fnref n="126" />:

$$s_j = S \frac{\Phi\left(\sigma^{-1}\left(\ln\left(j + \frac{1}{2}\right) - \mu\right)\right) - \Phi\left(\sigma^{-1}\left(\ln\left(j - \frac{1}{2}\right) - \mu\right)\right)}{1 - \Phi\left(\sigma^{-1}\left(\ln\left(\frac{1}{2}\right) - \mu\right)\right)} \qquad (5.26)$$

By construction we have $\sum_{j=1}^{\infty} s_j = S$. To calibrate $\mu$ and $\sigma$, we can fit the first moment — $\frac{n}{S} = e^{\mu + \frac{1}{2}\sigma^2}$ (or use the discrete version $\sum_{j=1}^{\infty} s_j \cdot j = n$ — or we can estimate $\mu$ and $\sigma$ using the method of maximum likelihood. The broken-stick model assumes that the total resources (or individuals) available in a community are divided randomly among species, leading to a characteristic pattern of abundances. The community is represented as a stick of fixed length, the stick is broken into $S$ segments at $S - 1$ randomly chosen points, and the lengths of the resulting segments are proportional to the abundances of the $S$ species — they follow a uniform distribution. The $i^{th}$ largest segment corresponds to a specific harmonic expectation based on the number of breaks. The abundance of the $i^{th}$ species is then given by $n_i = \frac{n}{S} \sum_{k=i}^{S} \frac{1}{k}$. May (1975) demonstrated that the corresponding species abundance distribution is:

$$s(j) = \frac{S}{n} (S-1) \left(1 - \frac{j}{n}\right)^{S-2} \qquad (5.27)$$

<footnote n="125">¹²⁵However, this approach ignores the distribution of the number of individuals ($n_1, n_2, \dots, n_S$). This means that two communities with the same number of species, the same total abundance, but different distributions of individuals among species will have the same species abundance distribution. To overcome this limitation, it is preferable to estimate the parameters $x$ using the maximum likelihood method with the following probability mass function: $Pr\{N = j\} = -\frac{x^j}{j \ln(1-x)}$ where $N$ is the random variable representing the abundance of a species. Once the parameter $x$ is estimated, the parameter $\alpha$ can be calculated as it serves as a scaling factor to fit the total number of species $S$.</footnote>

<footnote n="126">¹²⁶The normalization factor is needed because the support of the log-normal distribution is $(0, +\infty)$, while in this case we use the support $(\frac{1}{2}, +\infty)$.</footnote>
where $j \in [0, S]$. We verify that the total number of species is exactly equal to $S^{127}$. Figure 5.25 illustrates the four distributions when the number of species is $S = 15$ and the total abundance is $n = 80$. For the geometric model, the parameter $\kappa$ is set to 0.50. The parameters of the log-series model are determined by solving Equation (5.25), yielding $\alpha = 5.450$ and $x = 0.936$. In the case of the log-normal model, we set $\sigma = 1$, which implies $\mu = \ln(\frac{80}{15}) - 0.5 \times 1^2 = 1.174$. The broken-stick model is already parameterized using $n$ and $S$. The calibration of the parameters can be improved if the rank-abundance distribution ($n_1, \dots, n_S$) or the species abundance distribution ($s(1), \dots, s(n^+)$) is known, by using the method of maximum likelihood. However, this approach is applicable only for the first three models.

<figure><img src="image_212.png" /><figcaption>Figure 5.62: Species abundance models</figcaption></figure>

**Sampling** The species accumulation curve (SAC) is a graphical representation that illustrates how the number of observed species in a particular environment increases with additional sampling effort. It is also referred to as the species discovery curve or the collector's curve. The x-axis represents the sampling effort, such as the number of samples, individuals, cells, or plots surveyed, while the y-axis represents the cumulative number of species recorded. Let $n_i(s)$ be the number of

$$ \int_{0}^{n} s(j) dj = S(S-1) \int_{0}^{n} \left(1 - \frac{j}{n}\right)^{S-2} \frac{dj}{n} $$

Using the change of variable $u = \frac{j}{n}$, we obtain the desired result:

$$ \int_{0}^{n} s(j) dj = S(S-1) \int_{0}^{1} (1-u)^{S-2} du = S(S-1) \left[ -\frac{(1-u)^{S-1}}{S-1} \right]_{0}^{1} = S $$

<footnote n="127">¹²⁷Specifically, we have:</footnote>
individuals of species i recorded in the s^th^ sample. The SAC function is defined as:

$$SAC(m) = \sum_{i=1}^{S} 1 \left\{ \sum_{s=1}^{m} n_i(s) \geq 1 \right\}$$

where $m \le m_s$ is the number of samples, and $m_s$ is the total number of samples available. SAC(m) counts the number of species present in the m samples. When a sample represents an individual, SAC(m) is the expected number of species among m individuals selected at random. Hurlbert (1971, Equation (13), page 581) shows that<fnref n="128" />:

$$SAC(m) = \sum_{i=1}^{S} \left( 1 - \frac{\binom{n-n_i}{m}}{\binom{n}{m}} \right)$$

where $n_i$ is the abundance (number of individuals) of the i^th^ species and n is the total abundance of the community. In this case, the species accumulation curve is often referred to as the species rarefaction curve. In the Barro Colorado Island census, the database contains 21 457 trees and 225 species, located in 50 1-ha plots. For each plot, the species and the number of individuals per species are recorded. Figure 5.63 shows the distribution of individual trees across the 50 plots. The species rarefaction curve is given in Figure 5.64. In addition to the theoretically expected number of species, the 95% confidence interval is also reported.

<figure><img src="image_213.png" /><figcaption>Figure 5.63: Number of individuals per plot (BCI trees)</figcaption></figure>

More generally, the species accumulation curve is represented as the parametric curve (SAC(1),...,SAC($m_s$)), where the sample set at iteration m is constructed by adding one sample to the set used in the previous iteration $m-1$, selected from the $m_s-m+1$ remaining samples. Thus, the species accumulation curve is sensitive to the order in which the $m_s$ samples are arranged. For

<footnote n="128">¹²⁸The binomial coefficient $\binom{n-n_i}{m}$ is zero when $m > n - n_i$.</footnote>
<figure><img src="image_214.png" /><figcaption>Figure 5.64: Species accumulation curve (BCI trees)</figcaption></figure>

instance, samples may be ordered by size, either from smallest to largest or vice versa. Alternatively, the historical order of sampling, from first to last, may be considered. Considering the BCI dataset, we discover 77 tree species if we start by sampling the 11th plot, and 109 tree species if the first sample is the 39th plot (Figure 5.65). To eliminate the sensitivity of the results to the ordering of the samples, we can use a third approach, which consists of generating random permutations of the entire sample set (1, ..., $m_s$), computing the SAC function for each permutation, and performing a Monte Carlo simulation. By repeating this process $r$ times and averaging the resulting curves, a more robust estimate of the species accumulation curve can be obtained. Figure 5.66 shows the species accumulation curve for trees in the Barro Colorado Island census. The mean curve (in red) is estimated using the Monte Carlo method with 1000 replications. The 99% confidence interval is also shown in green.

The previous estimator of the species accumulation curve has some bias because it does not account for the underestimation of rare species. To address this problem, more sophisticated approaches are often used, in particular the estimators developed by Anne Chao. The *Chao1* estimator is a non-parametric method for estimating species richness from abundance data (Chao, 1984). It uses the frequency of rare species (singletons and doubletons) in the dataset to estimate the likely number of undetected species. The *Chao2* estimator is a variant of *Chao1* designed for use with presence-absence data across multiple samples (Chao, 1987). Instead of focusing on individual counts, it examines how often species are detected in different samples. The formula for this estimator is as follows:

$$S_{\text{Chao2}}(m_s) = S_{\text{obs}}(m_s) + \frac{(m_s - 1)}{m_s} \left( \frac{q_1(q_1 - 1)}{2(q_2 + 1)} \right)$$

where $S_{\text{obs}}(m_s) = \text{SAC}(m_s)$ is the total number of species observed in the set of $m_s$ samples, $q_m$ is the number of species present in exactly $m$ samples — $q_1$ and $q_2$ are then the number of singleton
<figure><img src="image_215.png" /><figcaption>Figure 5.66: Species accumulation curve (BCI trees)</figcaption></figure>

<figure><img src="image_216.png" /></figure>
and doubleton species. In Figure 5.66 we have added the *Chaos2* estimator to compare with the traditional empirical estimator. In this case, the number of tree species for the 50 ha patch is estimated to be 237 instead of 225.

The species accumulation curve is widely used to estimate the number of species in a region larger than the sampled area. This approach, known as extrapolation, was popularized by Robert K. Colwell in numerous published research papers (Colwell and Coddington, 1994; Gotelli and Colwell, 2001; Colwell et al., 2004). For example, Cazzolla Gatti et al. (2022) used this method to estimate the number of tree species on Earth. They first fitted a log-series model, then estimated the species accumulation curve, corrected it with the *Chao2* estimator, and finally extrapolated it by considering the lower 95% confidence bound. Their results indicate that there are 73 271 tree species globally, with the following distribution: 11 874 in Africa, 16 262 in Eurasia, 11 129 in North America, 31 110 in South America, and 8 232 in Oceania. Returning to the BCI tree database, we assume that the number of species follows the exponential model: SAC($m$) = $\theta_1(1 - \exp(-\theta_2m^{\theta_3}))$. Using the species accumulation curve, we fit the parameters ($\theta_1, \theta_2, \theta_3$) using the method of non-linear least squares. We find $\hat{\theta}_1 = 241.064$, $\hat{\theta}_2 = 0.562$ and $\hat{\theta}_3 = 0.517$. Since the 50 plots corresponds to 50 hectares, we would need to sample 15.6 × 100 = 1 560 plots to cover the entire area of Barro Colorado Island, which spans 15.6 km². We estimate that there are about 241 different tree species<fnref n="129" />, suggesting that 16 species remain to be discovered in the island (Figure 5.66).

**Species-area relationship vs. endemics-area relationship** We consider a region (or area) A where the total number of species and the number of individuals per species are known exactly to be $S$ and $n_i$. The species-area relationship can be expressed as the equality: SAR($A$) = $S$. Our goal is to determine the expected number of species $S_a$ of a subregion (or subarea) $a$ contained within the larger region $A$. Assuming random placement of individuals within the area, we can show that the species-area relationship becomes<fnref n="130" />:

$$SAR(a) = S_a = S - \sum_{i=1}^{S} \left(1 - \frac{a}{A}\right)^{n_i}$$

where $n_i$ is the number of individuals of species $i$ in region $A$. We define two types of endemism as follows:

* Global endemism: A species is globally endemic to $A$ if it is found exclusively in region $A$ and nowhere else.

* Local endemism: A species is locally endemic to $a$ if it occurs exclusively in subarea $a$ and not in the complementary area $A - a$.

Under the assumption of random placement, the endemics-area relationship (EAR) is<fnref n="131" />:

$$EAR(a) = E_a = \sum_{i=1}^{S} \left(\frac{a}{A}\right)^{n_i}$$

where $E_a$ is the expected number of species endemic to the subregion $a$. In Table 5.45, we report the expressions for SAR($a$) and EAR($a$) under different models (He and Legendre, 2002; Green and

$$SAC(1560) = \hat{\theta}_1 (1 - \exp(-\hat{\theta}_2 \times 1560^{\hat{\theta}_3})) \approx 241$$

<footnote n="129">¹²⁹We have:</footnote>

<footnote n="130">¹³⁰The proof is given in Question 1 of Exercise 5.5.3 on page 568.</footnote>

<footnote n="131">¹³¹The proof is given in Question 2 of Exercise 5.5.3 on page 568.</footnote>
<table><caption>Table 5.45: Species-area and endemics-area relationships for various species abundance models</caption><thead><tr><th>Model</th><th>Specification</th><th>SAR (a)</th><th>EAR (a)</th></tr></thead><tbody><tr><td>Random placement</td><td>n<sub>i</sub> is known</td><td>S<sub>a</sub> = S - &sum;<sub>i=1</sub><sup>S</sup> (1 - <sup>a</sup>&frasl;<sub>A</sub>)<sup>n<sub>i</sub></sup></td><td>E<sub>a</sub> = &sum;<sub>i=1</sub><sup>S</sup> (<sup>a</sup>&frasl;<sub>A</sub>)<sup>n<sub>i</sub></sup></td></tr><tr><td>Most even</td><td>n<sub>i</sub> = <sup>n</sup>&frasl;<sub>S</sub></td><td>S<sub>a</sub> = S (1 - (<sup>a</sup>&frasl;<sub>A</sub>)<sup>S</sup>)</td><td>E<sub>a</sub> = S (<sup>a</sup>&frasl;<sub>A</sub>)<sup>S</sup></td></tr><tr><td>Most uneven</td><td>n<sub>i&lt;S</sub> = 1, n<sub>S</sub> = n - S + 1</td><td>S<sub>a</sub> = 1 + (S - 1) <sup>a</sup>&frasl;<sub>A</sub></td><td>E<sub>a</sub> = (S - 1) <sup>a</sup>&frasl;<sub>A</sub> + (<sup>a</sup>&frasl;<sub>A</sub>)<sup>n-S+1</sup></td></tr><tr><td>Geometric</td><td>n<sub>i</sub> = <sup>n&kappa;(1 - &kappa;)<sup>i-1</sup></sup>&frasl;<sub>1 - (1 - &kappa;)<sup>S</sup></sub></td><td>S<sub>a</sub> = <sup>a</sup>&frasl;<sub>A</sub> + (S - s) &middot; (<sup>a</sup>&frasl;<sub>A</sub>)<sup>n-S</sup></td><td>E<sub>a</sub> = S - s (<sup>a</sup>&frasl;<sub>A</sub>) - (S - s) (<sup>a</sup>&frasl;<sub>A</sub>)<sup>n-S</sup></td></tr><tr><td>Mixed even-uneven</td><td>n<sub>i&le;s</sub> = 1, n<sub>i&gt;s</sub> = <sup>n - s</sup>&frasl;<sub>S - s</sub></td><td>S<sub>a</sub> = S - &sum;<sub>j</sub>s<sub>j</sub>(1 - <sup>a</sup>&frasl;<sub>A</sub>)<sup>j</sup></td><td>E<sub>a</sub> = &sum;<sub>j</sub>s<sub>j</sub>(<sup>a</sup>&frasl;<sub>A</sub>)<sup>j</sup></td></tr><tr><td>Random placement</td><td>s(j) is known</td><td>S<sub>a</sub> = S - &sum;<sub>j</sub>s<sub>j</sub>(1 - <sup>a</sup>&frasl;<sub>A</sub>)<sup>j</sup></td><td>E<sub>a</sub> = &sum;<sub>j</sub>s<sub>j</sub>(<sup>a</sup>&frasl;<sub>A</sub>)<sup>j</sup></td></tr><tr><td>Log-series</td><td>s(j) = &alpha;<sup>x<sub>j</sub></sup>&frasl;<sub>j</sub></td><td>S<sub>a</sub> = &alpha; ln(1 + <sup>x</sup>&frasl;<sub>1 - x</sub><sup>a</sup>&frasl;<sub>A</sub>)</td><td>E<sub>a</sub> = -&alpha; ln(1 - <sup>x</sup>&frasl;<sub>A</sub>)<sup>a</sup></td></tr><tr><td>Broken-stick</td><td>s(j) = <sup>S</sup>&frasl;<sub>n</sub>(S - 1) (1 - <sup>j</sup>&frasl;<sub>n</sub>)<sup>S-2</sup></td><td>S<sub>a</sub> = <sup>S ln(1 - <sup>a</sup>&frasl;<sub>A</sub>)</sup>&frasl;<sub>ln(1 - <sup>a</sup>&frasl;<sub>A</sub>) - <sup>S</sup>&frasl;<sub>n</sub></sub></td><td>E<sub>a</sub> = - <sup>S<sup>2</sup></sup>&frasl;<sub>n ln(<sup>a</sup>&frasl;<sub>A</sub>) - S</sub></td></tr><tr><td>Truncated negative binomial distribution</td><td>s(j) = <sup>&Gamma;(&gamma; + j)</sup>&frasl;<sub>&Gamma;(j + 1)</sub> &Gamma;(&gamma;)<sup>j</sup></td><td>S<sub>a</sub> = <sup>S</sup>&frasl;<sub>1 - (1 + &phi;)<sup>a</sup>&frasl;<sub>A</sub></sub> &Gamma;(&gamma; - j)</td><td>E<sub>a</sub> = S <sup>(1 + &phi;)(1 - <sup>a</sup>&frasl;<sub>A</sub>)</sup>&frasl;<sub>1 - (1 + &phi;)<sup>a</sup></sub></td></tr></tbody></table>

$n_i$ is the number of individuals of species i, $s_j$ is the number of species with j individuals, $\alpha$ and $x$ are the parameters of the log-series model, $\kappa$ is the resource preemption parameter, and $\gamma$ and $\phi$ are the shape and scale parameters of the TNBD model.

Source: He and Legendre (2002, Table 1, page 1187), Green and Ostling (2003, Table 1, page 3091) & Author's calculations.
Ostling, 2003). According to He and Hubbell (2011), the expected number of species lost due to the loss of area a can be calculated as:

$$ \mathcal{L}oss(a | A) = S_A - S_{A-a} = S - \left( S - \sum_{i=1}^{S} \left( 1 - \frac{A-a}{A} \right)^{n_i} \right) = \sum_{i=1}^{S} \left( \frac{a}{A} \right)^{n_i} = E_a $$

Thus, $E_a$ can be interpreted as the number of species lost if habitat area a is destroyed. This gives the following identity:

$$ SAR(A) = SAR(A-a) + EAR(a) $$

which states that the number of species in region A is equal to the sum of the species found in subregion A-a and the species locally endemic in subregion a.

He and Legendre (2002) used the tree dataset from the Pasoh Forest Reserve in Malaysia. The research area consists of a 50-ha plot (500 × 1000 m), established in 1986 and resurveyed several times between 1990 and 2016. The 1987 survey recorded 335 356 individual stems and 814 tree species. He and Legendre (2002) calibrated several species abundance models<fnref n="132" />. Figure 5.67 shows the estimated species-area relationship for each model compared to the empirical species-area relationship derived from the recorded data. We observe that the empirical curve lies between the broken-stick and geometric models. The endemics-area relationship is plotted in Figure 5.68. We restrict the analysis to surface areas larger than 1 ha because EAR(a) ≈ 0 when a ≈ 0. This is not the case for the function SAR(a). As noted by He and Hubbell (2011), there is an asymmetry between the two curves. In fact, it is easier to discover a species quickly when exploring a patch, whereas the extinction of a species requires the destruction of a large proportion of the patch. He and Hubbell (2011) concluded that species-area relationships always overestimate extinction rates due to habitat loss:

> "Here we show that extinction rates estimated from the SAR are all overestimates. [...] These overestimates are due to the false assumption that the sampling problem for extinction is simply the reverse of the sampling problem for the SAR. The area that must be added to find the first individual of a species is in general much smaller than the area that must be removed to eliminate the last individual of a species. Therefore, on average, it takes a much greater loss of area to cause the extinction of a species than it takes to add the species on first encounter, except in the degenerate case of a species having a single individual. [...] Only in a very special and biologically unrealistic case, when all species are randomly and independently distributed in space, is it possible to derive the EAR from the SAR." (He and Hubbell, 2011, page 368).

In this context, the choice of the power-law model can create some bias. Recall that $S_A = cA^z$. We can deduce that $S_{A-a} = c(A-a)^z$ and $E_a = S_A - S_{A-a} = cA^z - c(A-a)^z$. It follows that:

$$ \mathcal{L}oss_{species} = \frac{E_a}{S_A} = 1 - \frac{(A-a)^z}{A^z} = 1 - (1 - \mathcal{L}oss_{habitat})^z \quad (5.28) $$

We retrieve Equation (5.19) on page 435. At first glance, it seems that the approach based on the endemics-area relationship is coherent with the species-area relationship. In fact, the issue lies in the calibration of the endemics-area curve, which generally produces a value of z smaller than

<footnote n="132">¹³²The estimated parameters are $κ = 0.009927$ for the geometric model, and $α = 100.3014$ and $x = 0.999701$ for the log-series model. The other models are completely determined by the number of individuals ($n = 335 356$) and the number of species ($S = 814$).</footnote>
<figure><img src="image_217.png" /><figcaption>Figure 5.67: Species-area relationship (Pasoh Forest Reserve)</figcaption></figure>

Source: He and Legendre (2002, Figure 2a, page 1191) & Author's calculations.

<figure><img src="image_218.png" /><figcaption>Figure 5.68: Endemics-area relationship (Pasoh Forest Reserve)</figcaption></figure>

Source: Green and Ostling (2003, Figure 1a, page 3093) & Author's calculations.
<figure><img src="image_219.png" /><figcaption>Figure 5.69: Comparison of species-area and endemics-area curves (Barro Colorado Island)</figcaption></figure>

Source: He and Hubbell (2011, Figure S1) & Author's calculations.

the value obtained with the species-area curve. As a result, we deduce that the extinction rate is overestimated:

$$ \mathcal{L}oss_{species} = 1 - (1 - \mathcal{L}oss_{habitat})^{z_{EAR}} \le 1 - (1 - \mathcal{L}oss_{habitat})^{z_{SAR}} $$

To illustrate this bias, He and Hubbell (2011) considered nine datasets, including those from the Barro Colorado Island and the Pasoh Forest, and found that $z_{EAR} \le z_{SAR}$ in all cases. For example, for the Pasoh Forest Reserve, they found $z_{EAR} = 0.0536$ and $z_{SAR} = 0.124$. Figure 5.69 shows the empirical and theoretical SAR and EAR curves, along with the calibrated power-law models. We verify that $Loss_{species}$ is overestimated when $z_{SAR}$ is used instead of $z_{EAR}$. On page 435 we estimate that 50% of habitat loss should reduce the number of species by 15.91%, while 90% of habitat loss leads to 43.77% of species loss because we use the value $z_{SAR} = 0.25$ suggested by Pimm et al. (1995). If we consider a value of $z_{EAR} = 0.10$, which is the median value obtained by He and Hubbell (2011), 50% of habitat loss should reduce the number of species by 6.70%, while 90% of habitat loss leads to 20.57% of species loss (Table 5.41, page 435). The numbers are quite different.

**Remark 61** Censuses of tree data are abundant and valuable for studying relationships between habitat and species richness. Many of these datasets (including those from Barro Colorado Island and the Pasoh Forest Reserve) are available at http://ctfs.si.edu/datarequest, the data portal of ForestGEO (Forest Global Earth Observatory). ForestGEO is a global network dedicated to the study of forests and their ecosystems. Hosted by the Smithsonian Institution, it is one of the most comprehensive forest monitoring programs in the world. The network includes 78 forest research sites in the Americas, Africa, Asia, Europe, and Oceania, that monitor the growth and survival of approximately 7 million trees representing nearly 13 000 species.
Box 5.15: Forest definitions adopted by major international environmental and forestry organizations

* United Nations Food and Agriculture Organization (2000)
  Land with tree crown cover (or equivalent stocking level) of more than 10% and an area of more than 0.5 ha. Trees should be able to reach a minimum height of 5 m at maturity *in situ*.

* United Nations Framework Convention on Climate Change (2002)
  Minimum area of 0.05–1.0 ha of land with tree crown cover (or equivalent stocking level) of more than 10–30% with trees that have the potential to reach a minimum height of 2–5 m at maturity *in situ*.

* United Nations Convention on Biological Diversity
  Land area of more than 0.5 ha, with a tree canopy cover of more than 10%, which is not primarily under agriculture or other specific non-forest land use.

* United Nations Convention to Combat Desertification (2000)
  Dense canopy with multi-layered structure including large trees in the upper story.

* International Union of Forest Research Organizations (2002)
  Land area with a minimum 10% tree crown coverage (or equivalent stocking level), or formerly having such tree cover and that is being naturally or artificially regenerated or that is being afforested.

Source: Chazdon et al. (2016, Box 1, page 542).

**Forest loss** Among the various types of habitat loss, forests are particularly important because they are biodiversity hotspots, home to numerous endemic species. These ecosystems provide essential services, including climate regulation, water cycle maintenance, and soil stabilization. In addition, forests are complex, interconnected networks where species are highly interdependent. Loss of forest habitat can trigger trophic cascades, destabilizing entire ecological systems by disrupting some fundamental biological relationships. However, the concept of forest loss is somewhat vague and requires a clear definition of what constitutes a forest and what does not. According to the Oxford English Dictionary, a forest is defined as “*a large area of land that is thickly covered with trees*”, while the Cambridge Dictionary defined it as “*a large area of land covered with trees and plants, usually larger than a wood*”. However, despite its common usage, there is no universally agreed-upon definition of the word forest. The concept of a forest extends far beyond a simple description of tree coverage and must encompass various dimensions of biodiversity, particularly when aiming to measure ecosystem functions or address key aspects of forest conservation biology (Zalles et al., 2024). For instance, these aspects include forest loss, degradation, afforestation, reforestation, and other related processes. In Box 5.15, we report the forest definitions of five international organizations collected by Chazdon et al. (2016). While the definition depends on the purpose and the organization, the FAO definition is the most widely used globally. The FAO definition is practical because it uses measurable parameters such as tree height, canopy cover, and area. Here is the latest version used for the 2025 FRA<fnref n="133" />:

<footnote n="133">¹³³ A forest is defined by the presence of trees and the absence of dominant non-forest land uses. It includes young or regenerating areas that are expected to reach a height of 5 meters and a canopy cover of 10%. Forests also include mangroves, shelterbelts, bamboo, palms, and specialty plantations such as rubber and cork oak. Protected areas, forest infrastructure and abandoned shifting cultivation areas with tree regeneration are included. Agricultural tree</footnote>
"Land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use." (FAO, 2023a, Section 1a, page 7).

Another success factor is that data on forest status and trends are easily available thanks to the Global Forest Resources Assessment (FRA), which is collected and published by the FAO every five years<fnref n="134" />.

<figure><img src="image_220.png" /><figcaption>Figure 5.70: Proportion and distribution of global forest area by climatic domain in 2020</figcaption></figure>

Source: FAO (2020, page 1).

According to FAO (2020), the world has a total forest area of 4.06 billion hectares, which represents 31% of the total land area. This area corresponds to 0.52 ha per person. Tropical forests<fnref n="135" /> account for 45% of the world's forests, followed by boreal forests (27%), temperate forests (16%) and subtropical forests (11%), while polar forests account for less than 1% (Figure 5.70). More than 50% of the world's forests are located in just five countries: Russia, Brazil, Canada, the United States and China. In Table 5.46, we present the forest area of the top 20 countries, changes by decade, the distribution of the world's forest area in 2020, and the proportion of primary forest<fnref n="136" /> in 2020. For instance, Brazil had 589 million hectares of forest in 1990, which decreased to 497 million hectares in 2020. The country experienced forest area losses of 6.42%, 7.17%, and 2.92% between 1990–2000, 2000–2010, and 2010–2020, respectively. In 2020, Brazil accounted for 12.24% of the world's forest area, and 43.53% of its forests were classified as primary forests. Figure 5.71 shows the percentage of land area covered by forests. Globally, forests cover 31.1% of the total land area, but there are significant differences between countries. Seven countries have more than 90% of their land area covered by forest: Suriname (97.41%), French Guiana (97.36%), Guyana (93.55%),

systems such as orchards, fruit plantations and agroforestry with crops are excluded.

<footnote n="134">¹³⁴The FRA website is www.fao.org/forest-resources-assessment while the FRA data are available at https://fra-data.fao.org/assessments/fra/2020.</footnote>

<footnote n="135">¹³⁵The climatic groups are defined as follows: tropical (all months without frost; in marine areas above 18°C), subtropical (eight months or more above 10°C), temperate (four to eight months above 10°C), boreal (up to three months above 10°C) and polar (all months below 10°C).</footnote>

<footnote n="136">¹³⁶Primary forests are old-growth natural forests that have been largely undisturbed by human activity and have retained their original biodiversity and ecological processes.</footnote>
<table><caption>Table 5.46: Forest area (top 20 countries and six world regions)</caption><thead><tr><th rowspan="2">Country/region</th><th colspan="4">Value (in million ha)</th><th colspan="3">Variation by decade (in %)</th><th rowspan="2">Geographical distribution in %</th><th rowspan="2">Cumulated</th><th rowspan="2">Primary forest in %</th></tr><tr><th>1990</th><th>2000</th><th>2010</th><th>2020</th><th>2000</th><th>2010</th><th>2020</th></tr></thead><tbody><tr><td>Russia</td><td>809</td><td>809</td><td>815</td><td>815</td><td>0.04</td><td>0.72</td><td>0.02</td><td>20.09</td><td>20.09</td><td>31.30</td></tr><tr><td>Brazil</td><td>589</td><td>551</td><td>512</td><td>497</td><td>-6.42</td><td>-7.17</td><td>-2.92</td><td>12.24</td><td>32.32</td><td>43.53</td></tr><tr><td>Canada</td><td>348</td><td>348</td><td>347</td><td>347</td><td>-0.14</td><td>-0.14</td><td>-0.11</td><td>8.55</td><td>40.87</td><td>59.13</td></tr><tr><td>United States of America</td><td>302</td><td>304</td><td>309</td><td>310</td><td>0.36</td><td>1.71</td><td>0.35</td><td>7.63</td><td>48.50</td><td>24.31</td></tr><tr><td>China</td><td>157</td><td>177</td><td>201</td><td>220</td><td>12.64</td><td>13.34</td><td>9.65</td><td>5.42</td><td>53.92</td><td>5.21</td></tr><tr><td>Australia</td><td>134</td><td>132</td><td>130</td><td>134</td><td>-1.54</td><td>-1.72</td><td>3.44</td><td>3.30</td><td>57.22</td><td></td></tr><tr><td>Congo (DRC)</td><td>151</td><td>144</td><td>137</td><td>126</td><td>-4.47</td><td>-4.68</td><td>-8.03</td><td>3.11</td><td>60.33</td><td>65.59</td></tr><tr><td>Indonesia</td><td>119</td><td>101</td><td>100</td><td>92</td><td>-14.56</td><td>-1.60</td><td>-7.55</td><td>2.27</td><td>62.60</td><td>48.56</td></tr><tr><td>Peru</td><td>76</td><td>75</td><td>74</td><td>72</td><td>-1.51</td><td>-1.66</td><td>-2.32</td><td>1.78</td><td>64.38</td><td></td></tr><tr><td>India</td><td>64</td><td>68</td><td>69</td><td>72</td><td>5.71</td><td>2.82</td><td>3.83</td><td>1.78</td><td>66.16</td><td>21.76</td></tr><tr><td>Angola</td><td>79</td><td>78</td><td>72</td><td>67</td><td>-1.96</td><td>-7.14</td><td>-7.69</td><td>1.64</td><td>67.80</td><td>40.15</td></tr><tr><td>Mexico</td><td>71</td><td>68</td><td>67</td><td>66</td><td>-3.13</td><td>-2.10</td><td>-1.87</td><td>1.62</td><td>69.42</td><td>48.77</td></tr><tr><td>Colombia</td><td>65</td><td>63</td><td>61</td><td>59</td><td>-3.42</td><td>-3.07</td><td>-2.74</td><td>1.46</td><td>70.88</td><td></td></tr><tr><td>Bolivia</td><td>58</td><td>55</td><td>53</td><td>51</td><td>-4.68</td><td>-3.66</td><td>-4.24</td><td>1.25</td><td>72.13</td><td></td></tr><tr><td>Venezuela</td><td>52</td><td>49</td><td>48</td><td>46</td><td>-5.53</td><td>-3.35</td><td>-2.68</td><td>1.14</td><td>73.27</td><td>97.06</td></tr><tr><td>Tanzania</td><td>57</td><td>54</td><td>50</td><td>46</td><td>-6.48</td><td>-6.93</td><td>-8.42</td><td>1.13</td><td>74.40</td><td>62.32</td></tr><tr><td>Zambia</td><td>47</td><td>47</td><td>47</td><td>45</td><td>-0.76</td><td>-0.76</td><td>-4.03</td><td>1.10</td><td>75.50</td><td></td></tr><tr><td>Mozambique</td><td>43</td><td>41</td><td>39</td><td>37</td><td>-5.05</td><td>-5.38</td><td>-5.72</td><td>0.91</td><td>76.40</td><td></td></tr><tr><td>Papua New Guinea</td><td>36</td><td>36</td><td>36</td><td>36</td><td>-0.33</td><td>-0.27</td><td>-0.89</td><td>0.88</td><td>77.29</td><td></td></tr><tr><td>Argentina</td><td>35</td><td>33</td><td>30</td><td>29</td><td>-5.19</td><td>-9.48</td><td>-5.43</td><td>0.70</td><td>77.99</td><td></td></tr><tr><td>Africa</td><td>743</td><td>710</td><td>676</td><td>637</td><td>-4.41</td><td>-4.79</td><td>-5.82</td><td>15.68</td><td>15.68</td><td>19.30</td></tr><tr><td>Asia</td><td>585</td><td>587</td><td>611</td><td>623</td><td>0.34</td><td>4.01</td><td>1.92</td><td>15.34</td><td>31.03</td><td>13.79</td></tr><tr><td>Europe</td><td>994</td><td>1002</td><td>1014</td><td>1017</td><td>0.80</td><td>1.17</td><td>0.34</td><td>25.07</td><td>56.09</td><td>25.22</td></tr><tr><td>North and Central America</td><td>755</td><td>752</td><td>754</td><td>753</td><td>-0.39</td><td>0.24</td><td>-0.20</td><td>18.54</td><td>74.64</td><td>41.62</td></tr><tr><td>Oceania</td><td>185</td><td>183</td><td>181</td><td>185</td><td>-0.89</td><td>-1.26</td><td>2.34</td><td>4.56</td><td>79.20</td><td>1.41</td></tr><tr><td>South America</td><td>974</td><td>923</td><td>870</td><td>844</td><td>-5.24</td><td>-5.69</td><td>-2.98</td><td>20.80</td><td>100.00</td><td>35.38</td></tr><tr><td>World</td><td>4236</td><td>4158</td><td>4106</td><td>4059</td><td>-1.85</td><td>-1.24</td><td>-1.15</td><td>100.00</td><td>200.00</td><td>26.61</td></tr></tbody></table>

Source: FAO (2020), https://fra-data.fao.org/assessments/fra/2020 & Author's calculations.
<figure><img src="image_221.png" /><figcaption>Figure 5.71: Percentage of land area covered by forest, by country (2020)</figcaption></figure>

Source: Our World in Data, https://ourworldindata.org/forest-area.

<figure><img src="image_222.png" /><figcaption>Figure 5.72: Distribution of global forest area (2020)</figcaption></figure>

Source: Our World in Data, https://ourworldindata.org/forest-area.
Micronesia (92.03%), Gabon (91.32%), Solomon Islands (90.14%), and Palau (90.02%). Some of the world's largest countries also have a significant proportion of their land area covered by forest. For example, Russia (49.78%), China (23.34%), the United States (33.87%), Canada (38.15%), and Brazil (59.42%) all have significant forest cover. Conversely, other large countries have relatively low forest cover, such as Argentina (10.44%), Kazakhstan (1.28%), and Algeria (0.82%). Among the largest European countries (those with a land area of more than 20 million hectares), forest cover also varies widely. Most of these countries have at least 30% forest cover, including Belarus (43.19%), Germany (32.73%), Spain (37.17%), Finland (73.74%), France (31.51%), Italy (32.52%), Norway (40.05%), Poland (30.97%), Romania (30.12%) and Sweden (68.69%). However, some countries fall below this threshold, such as Ukraine (16.73%) and the United Kingdom (13.19%).

Table 5.47 presents some key forest characteristics. Globally, 17% of forests are located in protected areas, representing more than 700 million hectares. Among the six world regions, South America has the highest proportion of forests in protected areas (30.5%), followed by Africa (24.8%). Natural (regenerative) forests account for 92.4% of the global forest area, while 7.2% consists of planted forests. In addition, the majority of the world's forests are publicly owned (71.5%), compared to 21.7% under private ownership, with the remainder classified as unknown. Public ownership predominates in all regions, especially in Europe (88.2%), largely due to Russia, where 100% of forests are publicly owned.

We have seen that the world lost 177 million hectares of forest between 1990 and 2020. Focusing on the period 2015–2020, the annual forest loss was about 5 million hectares. This figure, representing the net change, is the difference between the forest expansion rate and the deforestation rate. In Table 5.47, we present these two statistics together with the reforestation rate, all expressed in 1000 hectares per year. For some countries, data on forest expansion, deforestation, and reforestation<fnref n="137" /> are missing (e.g., the United States, Australia, Congo, Angola), which introduces bias into the global rates. However, estimates from FAO (2020, page 4) indicate that the annual net loss of 5 million hectares is the result of a deforestation rate of 10 million hectares per year offset by a forest expansion rate of 5 million hectares per year. Although there is a declining trend in forest loss in the short term, the average rate of forest loss has accelerated in the long term. In fact, between the invention of agriculture and the beginning of the Industrial Revolution, we lost 2% of forest area every 5 000 years. Since 1700, as shown in Table 5.48, this rate has accelerated, especially since 1950.

The GFA dataset is not dedicated to the study of forest loss, although it contains a lot of information on this topic. To obtain information at a higher resolution than regional and country levels, and at a higher frequency than the 5-year period (for example, to obtain statistics on the Amazon rainforest on quarterly basis), other sources of information are available:

* The Global Forest Review (GFR) is an initiative of the World Resources Institute (WRI) that provides data, analysis and insight on the world's forests (https://research.wri.org/gfr/global-forest-review). It serves as a resource for tracking the state of the world's forests, understanding deforestation trends, and evaluating forest conservation efforts. GFR uses many datasets derived from geospatial data and maps.

* Global Forest Watch (GFW) is an online platform, available at www.globalforestwatch.org and developed by the World Resources Institute (WRI), that provides near real-time

<footnote n="137">¹³⁷Forest expansion refers to the growth of forest on land previously used for other purposes, involving a change of land use from non-forest to forest. It includes both afforestation (planting of trees on land that was not previously forested) and natural expansion of forests. Deforestation is the conversion of forested land to other land uses, whether this change is caused by human activities or by natural processes. Reforestation is the reestablishment of forest on land that is still classified as forest, typically through planting or deliberate seeding. Unlike forest expansion, it does not involve a change in land use.</footnote>
<table><caption>Table 5.47: Forest characteristics, ownership and annual change (top 20 countries and six world regions)</caption><thead><tr><th>Country/region</th><th>Forest area (10<sup>6</sup> ha)</th><th>Protected (%)</th><th>Regenerative (%)</th><th>Planted (%)</th><th>Private ownership (%)</th><th>Public ownership (%)</th><th>Expansion (10<sup>3</sup> ha/year)</th><th>Deforestation (10<sup>3</sup> ha/year)</th><th>Reforestation (10<sup>3</sup> ha/year)</th><th>Coverage (%)</th><th>Net change (10<sup>3</sup> ha/year)</th></tr></thead><tbody><tr><td>Russia</td><td>815</td><td>2.3</td><td>97.7</td><td>2.3</td><td>0.0</td><td>100.0</td><td>76</td><td>0</td><td>945</td><td>100.0</td><td>76</td></tr><tr><td>Brazil</td><td>497</td><td>30.1</td><td>97.7</td><td>2.3</td><td>44.2</td><td>55.8</td><td>243</td><td>1696</td><td>257</td><td>100.0</td><td>-1453</td></tr><tr><td>Canada</td><td>347</td><td>8.5</td><td>94.8</td><td>5.2</td><td>8.2</td><td>91.4</td><td>0</td><td>38</td><td>427</td><td>100.0</td><td>-38</td></tr><tr><td>United States of America</td><td>310</td><td>10.2</td><td>91.1</td><td>8.9</td><td>58.1</td><td>41.9</td><td></td><td></td><td>691</td><td>0.0</td><td>-60</td></tr><tr><td>China</td><td>220</td><td>13.8</td><td>61.5</td><td>38.5</td><td>41.3</td><td>58.7</td><td>2070</td><td>133</td><td>273</td><td>100.0</td><td>1937</td></tr><tr><td>Australia</td><td>134</td><td>18.0</td><td>98.2</td><td>1.8</td><td>32.3</td><td>67.0</td><td></td><td></td><td></td><td>0.0</td><td>182</td></tr><tr><td>Congo (DRC)</td><td>126</td><td>19.3</td><td>100.0</td><td>0.0</td><td>0.0</td><td>100.0</td><td></td><td></td><td></td><td>0.0</td><td>-1101</td></tr><tr><td>Indonesia</td><td>92</td><td>56.2</td><td>95.1</td><td>4.9</td><td>1.1</td><td>91.2</td><td>71</td><td>650</td><td>7</td><td>100.0</td><td>-579</td></tr><tr><td>Peru</td><td>72</td><td>31.6</td><td>98.5</td><td>1.5</td><td>16.0</td><td>84.0</td><td>6</td><td>179</td><td></td><td>100.0</td><td>-173</td></tr><tr><td>India</td><td>72</td><td>19.4</td><td>81.6</td><td>18.4</td><td>18.5</td><td>81.5</td><td>935</td><td>668</td><td></td><td>100.0</td><td>266</td></tr><tr><td>Angola</td><td>67</td><td>2.8</td><td>98.8</td><td>1.2</td><td>0.0</td><td>100.0</td><td></td><td></td><td>0</td><td>0.0</td><td>-555</td></tr><tr><td>Mexico</td><td>66</td><td></td><td>99.8</td><td>0.2</td><td>79.3</td><td>3.6</td><td>38</td><td>166</td><td>159</td><td>100.0</td><td>-128</td></tr><tr><td>Colombia</td><td>59</td><td>21.0</td><td>99.3</td><td>0.7</td><td>30.4</td><td>66.0</td><td>1</td><td>199</td><td>497</td><td>100.0</td><td>-199</td></tr><tr><td>Bolivia</td><td>51</td><td>24.5</td><td>99.9</td><td>0.1</td><td>0.1</td><td>99.9</td><td>4</td><td>243</td><td>3</td><td>100.0</td><td>-239</td></tr><tr><td>Venezuela</td><td>46</td><td>98.6</td><td>97.1</td><td>2.9</td><td>2.3</td><td>97.7</td><td>18</td><td>108</td><td>4</td><td>100.0</td><td>-90</td></tr><tr><td>Tanzania</td><td>46</td><td>62.3</td><td>98.8</td><td>1.2</td><td>7.3</td><td>4.1</td><td>5</td><td>474</td><td>27</td><td>100.0</td><td>-469</td></tr><tr><td>Zambia</td><td>45</td><td>71.0</td><td>99.9</td><td>0.1</td><td>6.4</td><td>26.2</td><td>2</td><td>190</td><td>4</td><td>100.0</td><td>-188</td></tr><tr><td>Mozambique</td><td>37</td><td>41.4</td><td>99.8</td><td>0.2</td><td>0.2</td><td>99.8</td><td>28</td><td>267</td><td>2</td><td>100.0</td><td>-239</td></tr><tr><td>Papua New Guinea</td><td>36</td><td>3.8</td><td>99.8</td><td>0.2</td><td>99.9</td><td>0.1</td><td>0</td><td>34</td><td>0</td><td>99.4</td><td>-34</td></tr><tr><td>Argentina</td><td>29</td><td>6.7</td><td>95.0</td><td>5.0</td><td>4.4</td><td>0.0</td><td>30</td><td>135</td><td>17</td><td>100.0</td><td>-105</td></tr><tr><td>Africa</td><td>637</td><td>24.8</td><td>98.2</td><td>1.8</td><td>5.5</td><td>71.4</td><td>404</td><td>2149</td><td>353</td><td>44.0</td><td>-3969</td></tr><tr><td>Asia</td><td>623</td><td>21.9</td><td>78.2</td><td>21.7</td><td>21.6</td><td>76.8</td><td>3477</td><td>1922</td><td>1221</td><td>118.2</td><td>1316</td></tr><tr><td>Europe</td><td>1017</td><td>4.9</td><td>91.3</td><td>7.4</td><td>9.1</td><td>88.2</td><td>255</td><td>69</td><td>1593</td><td>56.0</td><td>331</td></tr><tr><td>North and Central America</td><td>753</td><td>9.5</td><td>93.7</td><td>6.2</td><td>35.2</td><td>60.5</td><td>79</td><td>372</td><td>1322</td><td>84.2</td><td>-348</td></tr><tr><td>Oceania</td><td>185</td><td>15.7</td><td>97.1</td><td>2.6</td><td>47.2</td><td>52.2</td><td>16</td><td>40</td><td>56</td><td>-15.0</td><td>163</td></tr><tr><td>South America</td><td>844</td><td>30.5</td><td>97.6</td><td>2.4</td><td>31.9</td><td>61.6</td><td>460</td><td>2962</td><td>947</td><td>100.9</td><td>-2481</td></tr><tr><td>World</td><td>4059</td><td>17.3</td><td>92.4</td><td>7.2</td><td>21.7</td><td>71.5</td><td>4691</td><td>7514</td><td>5492</td><td>56.6</td><td>-4987</td></tr></tbody></table>

Source: FAO (2020), https://fra-data.fao.org/assessments/fra/2020 & Author's calculations.
<table><caption>Table 5.48: Distribution of habitable land on Earth (excluding glaciers and deserts)</caption><thead><tr><th>Time</th><th>Forests</th><th>Cropland</th><th>Grazing land</th><th>Wild grassland and shrubs</th><th>Urban and built-up land</th></tr></thead><tbody><tr><td>10 000 years ago</td><td>57%</td><td></td><td></td><td>42%</td><td></td></tr><tr><td>5 000 years ago</td><td>55%</td><td></td><td>1%</td><td>44%</td><td></td></tr><tr><td>1700</td><td>52%</td><td>3%</td><td>6%</td><td>38%</td><td></td></tr><tr><td>1900</td><td>48%</td><td>8%</td><td>16%</td><td>27%</td><td></td></tr><tr><td>1950</td><td>44%</td><td>12%</td><td>31%</td><td>12%</td><td>1%</td></tr><tr><td>2020</td><td>37%</td><td>16%</td><td>31%</td><td>14%</td><td>2%</td></tr></tbody></table>

Source: Our World in Data, https://ourworldindata.org/forest-area & Author's calculations

monitoring of global forest change. It uses advanced satellite technology, big data, and open access to empower users to monitor forests and take action against deforestation.

* Academic studies are extensively documented on specific regions and provide high-level analysis of data provided by GFA, GFR, GFW and other datasets. They may include a new way of observing forests, for example using satellite imagery<fnref n="138" />, an in-depth analysis of forms of forest degradation<fnref n="139" />, etc.

According to the Global Forest Review, the top 10 countries with the highest global tree cover loss between 2001 and 2023 are Russia (83.7 Million hectares or Mha), Brazil (68.9 Mha), Canada (57.5 Mha), the United States (47.9 Mha), Indonesia (30.8 Mha), the Democratic Republic of Congo (19.7 Mha), China (12.1 Mha), Malaysia (9.2 Mha), Australia (9 Mha), and Bolivia (8 Mha). However, tree cover loss does not always equate to deforestation. In some cases, such as commercial forestry, tree cover loss is temporary because forests are allowed to regrow after harvesting. This is particularly true in countries such as Russia and Canada, which have a permanent tree cover loss rate of 0%. Similarly, the United States, China and Australia have permanent loss rates of less than 5%. In contrast, countries such as Indonesia, Malaysia and Bolivia have permanent tree cover loss rates of more than 80%, indicating significant deforestation. Brazil and the Democratic Republic of the Congo fall between these two extremes, with permanent forest loss rates of 71% and 35%, respectively. Together, these 10 countries are responsible for 71% of the global tree cover loss of 488 Mha between 2001 and 2023. Curtis et al. (2018) propose a classification of forest loss into five main drivers (Box 5.16). Forestry accounts for 32% of global forest loss between 2001 and 2023, followed by shifting agriculture and wildfires, each contributing 23%. Commodity-driven deforestation accounts for 21%, while urbanization is responsible for less than 1%. According to the Global Forest Review, the drivers of forest loss vary significantly from one region to another (Figure 5.73). While commodity-driven deforestation is the leading cause of forest loss in Latin America and Southeast Asia, its impact is minimal in other regions, which are dominated by wildfires and forestry, with the exception of Africa, where 95% of forest loss is attributed to shifting agriculture.

The Global Forest Review also provides many interesting figures for understanding deforestation. For example, we learn that seven commodities account for 57% of all agricultural-related tree cover

<footnote n="138">¹³⁸Three notable examples are the studies by Hansen et al. (2013b) and Potapov et al. (2022), which use a dataset of land cover at a 30-meter spatial resolution derived from NASA's Landsat program, and the study by Lesiv et al. (2022), which is based on a dataset of forest management practices at a 100-meter resolution for the year 2015, derived from the European Space Agency's PROBA-V satellite imagery program.</footnote>

<footnote n="139">¹³⁹See for example Lapola et al. (2023), who studied four primary disturbances contributing to Amazon forest degradation: timber extraction, fire (sometimes used for land clearing, which can escape control and damage surrounding forests), edge effects (resulting from deforestation and habitat fragmentation), and extreme drought.</footnote>
Box 5.16: Drivers of forest loss

* Forestry

Large-scale forestry operations in managed forests or tree plantations where future regrowth is likely. Regrowth may occur through natural regeneration or tree planting.

* Commodity-driven deforestation

Long-term permanent conversion of forest and shrubland to non-forest land for commodity production, including agriculture, mining, or oil and gas production.

* Wildfire

Burning of vegetation without visible human conversion or agricultural activity afterward. Some of these fires occur naturally, but others are set by humans. In humid tropical forests, fires are not natural to the ecosystem and are almost always set by humans, usually to clear land for agriculture.

* Shifting agriculture

Agricultural practices in which forests are cleared, used for agricultural production for a few years, and then temporarily abandoned to allow trees to regrow. Shifting agriculture involves many different types of smallholder farming practices.

* Urbanization

Permanent conversion of forests to human settlements for the expansion and intensification of existing urban centers.

Source: Curtis et al. (2018, pages 1108-1109).

<figure><img src="image_223.png" /><figcaption>Figure 5.73: Drivers of tree cover loss by region (2001–2023)</figcaption></figure>

Source: https://research.wri.org/gfr/global-forest-review.