Ecology

NEWS AND VIEWS
09 September 2020

How can the decline in global biodiversity be reversed, given the need to supply food? Computer modelling provides a way to assess the effectiveness of combining various conservation and food-system interventions to tackle this issue.

Brett A. Bryan &

Brett A. Bryan is at the Centre for Integrative Ecology, Deakin University, Melbourne, Victoria 3125, Australia.
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Carla L. Archibald

Carla L. Archibald is at the Centre for Integrative Ecology, Deakin University, Melbourne, Victoria 3125, Australia.

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Nature is in trouble, and its plight will probably become even more precarious unless we do something about it1. Writing in Nature, Leclère et al.2 quantify what might be needed to reverse this deeply worrying path while also feeding people’s increasingly voracious appetites. The authors’ answer is to team ambitious conservation measures with food-system transformation in the hope of reversing the trend of global terrestrial biodiversity loss.

By nature, we mean the diversity of life that has evolved over billions of years to exist in dynamic balance with Earth’s biophysical environment and the ecosystems present. Nature contributes to human well-being in many ways, and the services it provides, such as carbon sequestration by plants or pollination by insects, could impose a vast cost if lost3. Although the slow and long-term decline of Earth’s biodiversity4 is often overshadowed by climate change, and more recently by the COVID-19 pandemic, the loss of biodiversity is no less of a risk than those posed by the other challenges. Many would argue that the effect of biodiversity losses could surpass the combined impacts of climate change and COVID-19.
More and more, the realization is growing that, as a planet, we are what we eat. Human demand for food is accelerating with the ever-increasing global population (projected to approach 10 billion by 2050), and each successive generation is wealthier and consumes more resource-intensive diets than did the previous one5. Trying to balance this rapidly rising demand against the limited amount of land available for crops and pasture sets agriculture and nature (Fig. 1) on a collision course6. As Leclère and colleagues show, a bold and integrated strategy is required immediately to turn this around.

Figure 1 | A bean field bordering a rainforest reserve near Sorriso, Brazil.Credit: Florian Plaucheur/AFP/Getty

Taking a long view out to the year 2100, Leclère et al. present a global modelling study assessing the ability of ambitious conservation and food-system intervention scenarios to reverse the decline, or, as they call it, “bending the curve”, of biodiversity losses resulting from changes in agricultural land use and management. Projections of future land use and biodiversity are uncertain, and when these models are combined, this uncertainty is compounded. One of the great innovations of Leclère and colleagues’ work is in embracing this uncertainty by combining an ensemble of four global land-use models and eight global biodiversity models and measuring the performance of future land-use scenarios in terms of higher-level model-independent metrics such as the amount of biodiversity loss avoided.
Importantly, the study also included a baseline (termed BASE) scenario — the world expected without interventions — and Leclère et al. used this to gauge the effectiveness of the intervention scenarios. Although it is not a focus of the paper, it’s worth pausing to ponder the sobering picture painted by this business-as-usual future largely bereft of birdsong and insect chirp.
Choosing to act now can make a difference to nature’s plight. Most (61%) of the model combinations run by the authors indicated that implementing ambitious conservation actions led to a positive uptick in the biodiversity curve by 2050. Such conservation actions included: extending the global conservation network by establishing protected nature reserves; restoring degraded land; and basing future land-use decisions on comprehensive landscape-level conservation planning. This comprehensive conservation strategy avoids more than half (an average of 58%) of the biodiversity losses expected if nothing is done, but also leads to a hike in food prices.

When conservation actions were teamed with a range of equally ambitious food-system interventions, the prognosis for global biodiversity in the model was improved further. Including both supply- and demand-side measures, these approaches included boosting agricultural yields, having an increasingly globalized food trade, reducing food waste by half, and the global adoption of healthy diets by halving meat consumption. These combined measures of conservation and food-systems actions avoided more than two-thirds of future biodiversity losses, with the integrated action portfolio (combining all actions) avoiding an average of 90% of future biodiversity losses. Almost all models predicted a biodiversity about-face by mid-century. These food-system measures also avoided adverse outcomes for food affordability.
Leclère and colleagues’ work complements the current global climate-change scenario framework (tools for future planning by governments and others, including scenarios called shared socio-economic pathways, which integrate future socio-economic projections with greenhouse-gas emissions), and represents the most comprehensive incorporation of biodiversity into this scenario framing7 so far. However, a major limitation of the present study is that it does not consider the potential impact of climate change on biodiversity. This raises an internal inconsistency because, on the one hand, the baseline scenario considers land-use, social and economic changes under approximately 4 °C of global heating by 21008, yet, on the other hand, it does not consider the profound effect of warming on plant and animal populations and the ecosystems they comprise9. Also absent from the models were other threats to biodiversity, including harvesting, hunting and invasive species10. Although Leclère and colleagues recognized these limitations and assigned them a high priority for future research, unfortunately for us all, omitting these key threats probably means that the authors’ estimates of biodiversity’s plight and the effectiveness of integrated global conservation and food-system action are overly optimistic. To truly bend the curve, Leclère and colleagues’ integrated portfolio will need to be substantially expanded to address the full range of threats to biodiversity.
Although the models say that a better future is possible, is the combination of the multiple ambitious conservation and food-system interventions considered by Leclère et al. a realistic possibility? Achieving each one of the conservation and food-system actions would require a monumental coordinated effort from all nations. And even if the global community were to get its act together in prioritizing conservation and food-system transformation, would such efforts come in time and be enough to save our planet’s natural legacy? We certainly hope so.

doi: 10.1038/d41586-020-02502-2

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All sites contained syngenetic permafrost in which the active layer and the uppermost permafrost have experienced numerous freeze-thaw cycles since formation during the Holocene22. Permafrost organic matter radiocarbon ages ranged from 7850 ± 30 to 830 ± 20 y B.P. (Supplementary Data), with the western sites containing the oldest SOM and the northern Hudson Bay peatlands containing the youngest SOM.
For organic layers, permafrost soil C:N atomic ratios (14.50 [12.61–19.67], median [25th–75th]) were lower and H:C atomic ratios (0.13 [0.13–0.14]) were greater relative to the active layer, 23.89 [19.33–29.50] and 0.14 [0.12–0.15], respectively (Supplementary Fig. 1). Similarly for mineral layers, permafrost C:N ratios were lower (12.0 [3.23–18.09]) and H:C ratios higher (0.24 [0.15–0.86]) compared to the active layer, 16.21 [13.67–18.87] and 0.17 [0.15–0.21], respectively. For both thermal layers, in organic layers C:N ratios were higher and H:C ratios were lower than in mineral layers.
These stoichiometric properties are typical of boreal and tundra soils (Supplementary Fig. 1)23. The higher C:N and very low H:C ratios of the organic layers relative to mineral layers suggest higher contents of condensed aromatic structures originating from peat24. Permafrost layers displayed lower C:N properties suggesting different SOM composition (e.g., lignin, tannins, lipids, sugars or amino acids) and an enrichment in microbial biomass relative to the active layer24. The absence of a downward trend of C:N and H:C within the permafrost (Supplementary Fig. 1), except at Daring Lake, indicates that soil development and microbial processing were effectively halted soon after permafrost aggradation23.
Active layer and permafrost yields of DOC and nitrogen
DOC content correlated with soil C content in both active layer (r2 [log–log] = 0.748, P  More

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Genome structure of DhNV
The circular genome of DhNV is 119,754 bp and contains 106 hypothetical ORFs, with 56 on the positive strand and 50 on the negative strand (Fig. 1). Fifty-nine of the ORFs met our comparative e-value threshold of  More

Multiple statistical methods have been developed to estimate the effect on birds and bats as a result of wind energy during the last 20 years26,27,28,29,30,31. Some of these studies are focused on the conservation status of the species32, the incidence factor of the wind turbines19,33, demographic parameters34,35, behavioural12 and also morphological parameters of the species36,37. In any case, it is essential to group all types of affections in order to be able to establish a global quantification that can be adapted to each species and to each specific wind farm. In other words, it can be obtained from a mathematical algorithm that allows quantifying the effect on each species, taking into account the characteristics of each wind farm3.
Furthermore, the formula that reflects the effect on the species must consider aspects related to the wind farm itself (type and distribution of turbines, occupation of the territory, etc.) and those related to the species, both in terms of its degree of threat and social importance, as well as its special sensitivity to the presence of the wind farm. According to this, the affection to each species must respond to the following formula:

$${text{AS}}_{{text{I}}} = {text{WF}}left( {{text{SS}}_{{text{I}}} + {text{VS}}_{{text{I}}} } right)$$

where ASI = Affection to species i, WF = Constant derived from the characteristics of the wind farm, SSI = Sensitivity of the species i to the presence of the wind farm, VSI = Social value of species i.
The index of affection, therefore, will be the product of multiplying the obtained values by the wind farm with those of each species that is present in the area.
Wind farm value constant (WF)
The impact value of the wind farm will be determined by the characteristics of the wind farm and also be influenced by both the characteristics of the wind farm (VWF) and its location (UF). At the same time, the VWF will be determined both by the affection of each wind turbine (WT) and by the distribution within the wind farm (extension and lines of turbines).

$${text{WF}} = {text{VWF}} + {text{UF}}$$

Value of the wind farm (VWF)
To calculate the overall effect of the wind farm not only is necessary to know the effect of each turbine but also its distribution in space. It is relevant to assess the distances between the wind turbine and if they are or not operating because when the turbines are very close together, the risk of moving between them is greater than in wind farms with more separate wind turbines38 and to know the number of rows in which the turbine are distributed. Crossing a wind farm with a single line of wind turbines is easier than those wind farms with several consecutive rows39. For this reason, the global affection of the wind farm (VWF) is understood as:
1.
The individual value of each of the wind turbine (WT) multiplied by the number of existing turbines.

2.
The total area occupied by the wind farm (AWF); in this way, it is not only considered the whole area of affection but also is established the density of the wind turbines.

3.
The number of rows that are included in the wind farm.

Based on the preceding information, the proposed formula for assessing the characteristics of the wind farm is:

$${text{VWF}} = left( {left( {{text{Ni}}*{text{WTi}}} right)/{text{AWF}}} right)^{{{1}/{text{F}}}}$$

where Ni: Number of wind turbines. WTi: Incidence of each wind turbines (the WT value will be the same for all unless in the same wind farm there were different types of wind turbines with different affection areas). AWF: Total surface of the area of study understood as the area formed by the vertical rectangle created between the furthest wind turbines from the same front line and the height of them. In the case of wind farms with more than one row, the total surface area is calculated as the sum of the surfaces of each row. F = Number of lines forming the wind farm.
Of these variables considered in the previous formula, it is only necessary to develop the affection inherent to each wind turbine (WT) that has to be calculated considering both the area of the turbine’s affection and the rotation speed of the blades.

$${text{WT}} = {text{AFM}}*{text{BRS}}$$

where AFM: Area of affection of each wind turbines, BRS: Blade rotation speed.
The area of affection of each turbine is the surface of the circumference formed by the blades (a), plus the surface of the triangle formed by the blades with the ground when they form an angle of 60° with the support tower (b), minus the intersection of both surfaces (c) (Fig. 3):

$${text{AFM}} = {text{a}} + {text{b}}{-}{text{c}}$$

Figure 3

Scheme and values to calculate the area of affection of each wind turbine. The area of affection of each turbine is the surface of the circumference formed by the blades (a), plus the surface of the triangle formed by the blades with the ground when they form an angle of 60° with the support tower (b), minus the intersection of both surfaces (c).

Full size image

Figure 4

Zoning scheme of risk areas. ZONE I: Corresponds to the free height between the ground and the blades. ZONE II: This zone corresponds to the area of the circumference formed by the blades when turning. ZONE III: Corresponds to the free height above the blades so that this interval is above the previous interval.

Full size image

Being: a = πr2   b = (sen60°*r)(L − cos 60° * r), where r is the length of the blades and L the height of the support tower. c = ((πr2/3) − ((sen60° * r)(cos 60° * r))).
To calculate the affection of the rotation speed of the blades (SB) it is assumed that the greater the rotation speed, the greater the turbulence and the greater the risk for the fauna19,40. In any case, this incidence is not linear but exponential since from a certain speed the affection can be considered high. To calculate this value, we established the following formula:

$${text{BRS}} = {1} + {text{Log}}left( {{text{SB}}} right)$$

Given that the value of the wind Farm (VWF) is the quotient between the sum of the areas affected by each turbine and the total area occupied, the value generally will be less than 1. In cases where the value is greater (when the surface area of the turbine multiplied by the rotation speed of the turbine is greater than the total surface of the area) it will be equal to 1. i.e., the maximum surface area affected cannot be greater than the surface area occupied by the total wind farm.
Location in the natural environment (UF)
Many works show the importance of selecting the location of the wind farm to minimize its impact on birdlife. However, it is possible that wind farms may be authorized in sensitive areas or in areas with poor environmental conditions (predominance of fog) or that have synergistic effects with adjacent wind farms. In this sense and as indicated in the introduction, there are four factors that can influence this impact: low visibility, proximity to sensitive areas, location in migratory crossings and synergies with other wind farms. Therefore, the value of this variable should be at least the same as that established for the previous variable (VWF). In this regard, it is proposed that the maximum value of the variables used to compute this factor should also be 1.

Visibility (VI): This variable measures the frequency of days with low visibility (fog, intense rain, etc.) compared with the total number of observation days (total number of days with low visibility/total number of observation days). The maximum value is 0.25.

Proximity to sensitive areas (ZS): Sensitive areas are those in which occur high concentrations of individuals, either because they are breeding areas, feeding areas, resting areas or roosts. Protected areas such as IBAS or LICs may also be considered. Not all species have the same radius of action, so setting a minimum radius of affection can only be established randomly. For example, for some species a radius of influence of 10 km is small but for others can be large. In any case and for having a uniform criterion, it will be considered that a sensitive area is close to the wind farm when it is located less than 10 km4, in this case, the value of this variable will be 0.25 and if they are between 10 and 50 km the value will be 0.15 while if it is more than 50 km is considered that the location of the wind farm does not influence these areas (value 0).

Migratory passes (MP): Migratory passes are those areas used by avian fauna for their daily or migratory movements. If the wind farm is located in one of these Migratory passes, the effect will be high so it will be valued with a maximum value (0.25) and the value will be minimal (0) if this is not the case.

Proximity to other wind farms (PWF): It is relevant to include this variable because of the proximity of different wind farms cause negative synergistic effects on the species by limiting the length of possible free corridors of wind turbines. In this way, the location of another wind farm less than 3 km away is considered very negative (0.25), between 3 and 5 km (0.15), between 5 and 10 km (0.10) and more than 10 km (0), it does not affect. If there is more than one wind farm in the area, the value will increase 0.25 if it is between 3 and 5 km and 0.15 if it is between 5 and 10 km.

$${text{UF}} = {text{VI}} + {text{ZS}} + {text{MP}} + {text{PWF}}$$

where WT: Value related to the location of the wind farm. VI: Predominant visibility in the area. ZS: Presence of sensitive areas in the vicinity of the wind farm. MS = Incidence of the wind farm in migratory crossings. PWF: Proximity to wind farms.

The possible maximum value for the wind farm location will be 1.
Therefore, the possible maximum value inherent in the characteristics and location of the wind farm will be 2. Substituting the values in the proposed formula:

$${text{WF}} = {text{VWF}} + {text{UF}}$$

And considering the values obtained for each mill, the wind farm in general and its location, the result is the following formula:

$${text{WF}} = left( {left( {{text{Ni}}*{text{IMi}}} right)/{text{AWF}}} right)^{{{1}/{text{F}}}} + left( {{text{VI}} + {text{ZS}} + {text{MP}} + {text{PWF}}} right)$$

Affection on the species
Not all species have the same sensitivity to the presence of the wind farm, being some of them more sensitive than others (25). On the other hand, the incidence on endangered species is not the same as that on species with stable populations in the area so, it is necessary to differentiate two types of variables related to the species: those related to the special sensitivity of each species to the presence of these infrastructures (SS) and the one inherent to its degree of threat, conservation or socioeconomic interest (VS). The affection value of each species will be the sum of the values of each type of variable. Therefore, the value of this section will be:

$${text{Affection}};{text{to}};{text{the}};{text{species}} = left( {{text{SS }} + {text{ VS}}} right)$$

Sensitivity of the species to the wind farm (SS)
These variables will be considered as the impact of the wind farm on each species due to its morphological, ethological, historical and demographic characteristics, etc. It is the closest thing to what could be understood as collision risk since it assesses the different characteristics of each bird (morphological, ethological, demographic, etc.) based on the risk of colliding with wind turbines and, valuing more those characteristics that enhance the probability of collision.
Bird size will be considered in this variable. A higher percentage of affection is detected on large birds in the majority of the recorder monitoring of the incidence of wind farms. However, this value seems to be overestimated since the detectability of carcasses of small birds and bats is lower as they remain less time on the ground30,41,42.
On the other hand, small birds show much less resistance to wind flows generated by the blades so it seems logical to think that the affection on this group of birds and on bats is higher. For this reason, a greater impact on small species has been assessed.
As a reference size, those birds smaller than or equal to a turtledove have been considered as small birds; medium-sized birds are those whose sizes are between a turtledove and a heron while those larger than a heron are considered as large birds. Considering these aspects:
The behaviour of different species will influence their risk of collision increasing the possibility of being affected by wind turbines38, for example, species that tend to go in groups show a greater risk of collision. The phenological characteristics of species are also important, for example, those species that are only in passage (prenuptial and postnuptial) will be little time in the study area but as they are not accustomed to the presence of wind turbines, probability of collision is high and possibly increased by going in large groups. Breeding species in the area are more dangerous because the young ones, still inexperienced in flight, show high risks of collision1. In other words, variables reflected in this section are related to the time the species spends in the area38, its dexterity in flight and its gregariousness. Together with these variables, the type of flight carried out by each species has been also considered: direct flights avoid staying longer in the area while cycloid or indirect movements increase the possibility of collision.

Seasonality: It considers the number of months in which the species is detected in the area. The maximum value is 1 if the species is sedentary (12 months) so each month is valued as 0.083.

Phenology: Marks the periods in which the species is present in the area. It is considered that species present in the breeding season or in passage show a greater risk than those that are only wintering. In this sense, if the species is in the breeding season will be valued with 0.75, only in winter 0.25 and 0.5 only in passage. When it appears in all seasons or in three of them, the value will be maximum (1). The value of the station will be also maximum if appears in two periods.

Flight height: In order to calculate flight height with risk for each species, the characteristics of each wind farm are considered. That is to say, they have to be adjusted to each wind farm since the interval of each zone will vary according to these ones. In this sense, for example, small birds that fly at lower altitudes can be located in the lower zone or not depending on the wind farm, just as large birds can be located in the area of the blades or above. In this sense, three zones have been established (Fig. 4):

ZONE I: Corresponds to the free height between the ground and the blades so, this interval goes from 0 m to the height resulting from subtracting the size of the blade from the length of the support tower. Value 0.5

ZONE II: This zone corresponds to the area with the greatest risk of collision since it is equivalent to the circumference formed by the blades when turning. Therefore, the interval will go from the previous height to its sum with the diameter of the circumference formed by the blades. Value: 1.

ZONE III: Corresponds to the free height above the blades so that this interval is above the previous interval. Value: 0.

When a species presents different flight heights, the one more frequent and that presents the greater risk will be selected.

Type of flight: Direct flights are considered to have a lower risk of collision than those that cause a longer stay in the area. The values will be 0.25 in direct flights and 0.5 in indirect flight.

Flock size: The risk of collision is considered higher when species show large groups so the following classification is established: One individual: 0.25; groups of 2–5 individuals: 0.5; groups of 6–10 individuals: 0.75; groups of more than 10 individuals: 1.

Historical variables (Maximum value 2).

A variable related to mortality detected in previous studies has also been included. Those species that are systematically detected in the mortality reviews of these infrastructures or exist high figures of mortality due to collision in specific wind farms should be considered.

Species with previous collision data (usual 2; medium 1; scarce 0.5; no record 0). This value is established at the discretion of the technician who performs the assessment, but as a habitual criterion, it can be considered as usual when the species appears in most of the studies (more than 30% of the studies), between 15 and 30% of the studies on average; and it will be classified as scarce if it only appears between 1 and 15% of the studies.

The last variables considered are related to the incidence on population parameters of each species. It has been considered that the species with reproductive strategy R suffer a lower incidence on the populations (although the mortality may be higher) since their reproductive efficiency partly solves this loss. However, species with K strategy suffer enormously when the mortality of young individuals is high. On the other hand, those species that frequently use the area where the wind turbines are located will show a higher probability of collision than those that are less common and those species that have high abundances in the area have also a higher probability of impact than those with few individuals16.

Survival-Fertility (type K or R) (K = 0.5; R = 0.2).

Frequency: This variable measures the frequency with which each species appears in the area in relation to the rest of the species present (total number of presences of the species/total number of presences detected). The maximum value is 1.

Abundance of the species in the area (number of individuals detected of the species i/total number of individuals detected of all species) (maximum value 1).

Species value (VS)
This value will include the conservation and socio-economic importance of the species (including the hunting value or social interest of some species). The affections on those species that are in a situation of greater risk of extinction must be considered in a relevant way, since the loss of a few individuals can represent the unfeasibility of the population. In this respect, both the degree of threat and the legal cataloguing of the different species have been considered.
The maximum value of this variable is much higher than the rest of variables since those species with the maximum protection value or degree of threat will have a value of 9. The cataloguing according to the Red Books will relate to the value established in Table 143. It has also been considered necessary to assess the socio-economic importance of some species. In this regard, it is taken into account not only the importance of hunting, which is relevant for some species of birds, but also its social importance, that is to say, those species which have conservation or recovery plans established in areas close to the different administrations or which are especially valued by the population, although their threat level is not very high (colonies of birds especially loved by the local population, etc.).
Table 1 Values given to the different classifications or threat level.
Full size table More

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