Philippa Kaur

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In-flight dosing system
The system used in this work is detailed in Fig. 1. It can be broken down into three primary modules: (1) a “coarse tracking” system that uses a pair of stereoscopic cameras to identify the three dimensional location of a target subject, which is passed on to (2) the “fine tracking” system that uses a single higher speed camera and a fast scanning mirror (FSM) to keep the target in the middle of the field of view (FOV) of the camera using a proportional-integral-derivative (PID) control loop, and (3) the laser dosing system that fires the laser pulse, which is co-aligned with the fine tracking system to ensure the laser pulse is accurately applied to the subject even while it is moving. For both the coarse and fine tracking systems, subjects are identified by the size of their silhouettes generated from near infrared LED back-illumination or reflection. As demonstrated in Fig. 1b, the subject cages were 20 cm cubes constructed from clear acrylic, but with the side facing the tracking and dosing systems made of borosilicate glass, placed two meters away from the FSM. A high-speed video camera (Vision Research Phantom) was set up to record a small subset of experiments at 2000 frames per second as well. For all experiments, each cage contained only a single subject to be illuminated, or “dosed,” with the laser, and conditions were set such that a subject could be dosed only when it was at least 2.5 to 5 cm away from any wall of the cage to ensure the subject was flying normally, rather than taking wing or alighting.
Figure 1

Schematics and images of in-flight dosing setup. (a) Schematic (not to scale) of primary dosing system components and communication lines among them. Note that the mirror control and 3D position subsystems were run on separate kernels within the same PC. (b) Image of complete system setup including the test cage location, LED backlighting, and control electronics. Cameras for coarse and fine tracking, the fast scanning mirror (FSM), and dosing laser path are contained in the white circle and better seen in (c) close-up image of core tracking and dosing system components and alignment among them. Red line represents fine tracking image path, green line the dosing laser path (laser not shown), and yellow line the combined fine tracking and dosing laser path.

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Prior to commencing the laser dosing experiments, a number of parameters were defined to analyze the results, as detailed in Table 1 and Fig. 2. The key outcome, in line with our previous study15 and with WHO guidelines on insecticide treatments17, was whether the subjects were alive or disabled (i.e. dead or moribund) 24 h after the treatment. To characterize how well the subjects were tracked during the laser pulse, the tracking error was defined as how far the insect’s centroid was from the center of the fine tracking camera’s FOV. Other parameters defined in Table 1 relate to the “occlusion factor,” or how much of the laser beam’s energy (assuming a Gaussian profile) overlapped with the target’s outline, as demonstrated in Fig. 2. From the coarse tracking system’s output of xyz position of the target, we could also define velocity, speed, and both linear and angular acceleration of the subjects before, during, and after the laser pulse.
Table 1 Definitions of all parameters tracked or calculated for each in-flight dosing event.
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Figure 2

Representative fine tracking camera images of A. stephensi silhouettes. In each frame, the approximate outline of the thorax and abdomen is drawn (thick black lines) according to a set pixel intensity threshold. The centroid of this region is then calculated (intersection of red crosshairs) and compared with the center of the camera’s field of view (green dot) to determine the current tracking error and provide input to the fine tracking PID loop controlling the direction of the scanning mirror. The green circle around the green dot represents the spot size of the laser (2.5 mm diameter for all images shown here); occlusion factor represents how much of this laser spot (assuming a Gaussian profile) overlaps with the body of the subject. The images in (a) demonstrate a typical time course for a single subject in 1 ms intervals, and those in (b) show representative depictions of tracking errors ranging from 1 to 5 pixels for various subjects.

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Initial results with 532 nm laser
Initial experiments were conducted with A. stephensi subjects and a 532 nm laser (Verdi, Coherent) using parameters for power (3 W), pulse duration (25 ms), and laser spot diameter (2.5 mm) that were defined in our previous work15 as an optimal combination of potential cost and mortality performance. As seen in Supplemental Video 1, the system could be quite effective in disabling a flying mosquito with these parameters. Of note from the video, along with flight tracking data for numerous trials (not shown), the 25 ms pulse duration appeared to be short enough that the mosquitoes did not perceptibly alter their flight pattern during the pulse to throw off the tracking algorithm. From Fig. 3a,b, though, the initial system did not have consistent enough tracking performance, such that survival of the subjects was almost entirely dictated by how well the fine tracking system kept the target near the center of its FOV. From this initial experiment on a sample of 80 subjects, we set limits for mean and maximum tracking error during the laser pulse as 2 and 3 pixels, respectively, where each pixel represents ~ 250 μm. Figure 3c,d demonstrates that the system was able to achieve and maintain this performance after modifying the PID loop parameters, and that there was no longer any association between tracking performance and mortality at these conditions. These criteria were evaluated and assured for all mortality data reported in this manuscript (i.e. a Kruskal–Wallis test indicated no significant differences in tracking errors among subjects that survived or were disabled by the laser pulses). Further, Supplemental Fig. S1 shows that flight behavior, in particular speed and linear acceleration, did correlate with tracking accuracy, but that the system was able to meet the noted tracking requirements even at the extremes of flight behavior that could be observed in this study given the restricted flight volumes (though the typical values seen here largely align with available data for other Anopheles mosquitoes18,19).
Figure 3

Influence of mean and maximum tracking errors on mortality outcomes. Initial mortality outcomes using LD90 conditions with a 532 nm laser from previous anesthetized work showed strong correlation with (a) mean and (b) maximum tracking errors over the 25 ms pulse duration. After setting and achieving new tracking error targets of less than 2 pixels mean and 3 pixels max (see Fig. 2b for depictions of overlap between the laser spot and the subject for various error magnitudes), identical experiments no longer showed any correlation with (c) mean or (d) maximum tracking errors. Moribund and dead outcomes were grouped in (c) and (d), labeled “Disabled,” and subsequent experiments since they both represent functional kills and are grouped as such in WHO guidelines for insecticide trials.

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Mortality results were analyzed as in Fig. 4. Each data point represents the mortality outcomes (i.e. percentage of targets that were dead or moribund at 24 h, assuming acceptable control mortality  2 × that of the other experiments at 445 or 532 nm. Note that this greater LD90 fluence value for the smaller spot still represented a lower pulse energy than required for the larger spots given the spot size differential.
Table 2 List of in-flight dosing experimental conditions and resulting LD90 fluence values for A. stephensi subjects.
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Figure 5

Dose–response curves for all experiments from Table 2, other than the two constant pulse energy tests. The x-axis is plotted on a log scale to allow clear visualization of the curves at both low and high fluence values. Individual data points and error bars not shown for the sake of clarity. The intersections of the dashed horizontal line at 90% mortality with the dose–response curves indicate the LD90 points, which are also presented in Table 2, along with pseudo R2 values for the logistic regression fits.

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Dosing with near infrared wavelengths
Two near infrared (NIR) laser sources were examined as well—a custom constructed 1,064 nm (1 μm) fiber laser with a maximum output of 30 W and a commercial 1,570 nm (1.5 μm) fiber laser (IPG Photonics) with a maximum deliverable output of 11 W. The right-hand side of Fig. 5 shows the dose–response curves for these laser sources in addition to those using visible wavelengths. All of the infrared experiments resulted in significantly higher LD90 fluence levels compared with the visible lasers, and given the log scale of Fig. 5, varying experimental conditions with the 1 μm source led to comparatively larger changes in LD90 compared with the visible sources. As with the blue diode, the small spot (1.5 mm) had the highest LD90 fluence, but unlike the visible lasers, the larger spot (4 mm) offered a lower LD90 fluence compared with the baseline 2.5 mm. For the 1.5 μm source, there was insufficient mortality at the maximum power available from this source with a 2.5 mm spot and 25 ms pulse duration, so Fig. 5 shows a curve using slightly longer pulse durations to make up the additional fluence required. From the available data, it appears that the LD90 fluence levels for the 1 μm and 1.5 μm sources are at least at reasonably comparable levels.
Effects of longer pulse durations with lower power
We then further explored the effects of increasing the pulse duration to determine whether this could relax the optical power required from the laser source, given that laser cost is correlated with output power. Figure 6a,b show the results for the 532 nm and 1,064 nm sources, respectively, with spot diameters of 2.5 mm and pulse duration set to 25, 50, or 100 ms. In all cases, the optical power was adjusted according to the pulse duration to supply a constant pulse energy, and therefore fluence at approximately the LD75 level determined from previous experiments, which was chosen to ensure a low likelihood of any data point being 100% mortality (i.e., a saturated signal). As evidenced from Fig. 6a and Table 3, at 532 nm there was a significant drop off in mortality at the longer pulse durations, although the effect is smaller going from 50 to 100 ms compared with the initial drop from 25 to 50 ms. Similar, but smaller magnitude effects were seen with the 1 μm source in Fig. 6b and Table 3. Supplemental Fig. S2 shows that the tracking accuracy for these experiments somewhat mirrors the mortality performance, with a notable decrement from 25 to 50 ms but no significant change from 50 to 100 ms.
Figure 6

Mortality results for experiments with constant pulse energy achieved by proportionally adjusting the power according to pulse duration (25, 50, and 100 ms). Dosing was performed with both the (a) 532 nm and (b) 1,064 nm lasers, both using a 2.5 mm spot size, and set to the approximate LD75 fluence value from the dose–response curves (solid curves with dashed line 95% confidence intervals of logistic regression fit) from the corresponding 25 ms experiments. Error bars on individual points represent 95% confidence intervals of exact binomial probabilities. Statistical differences among mortality at the various pulse durations summarized in Table 3. The slight offsets on the x axis in (a) stem from slight changes in the beam spot size as a function of power.

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Table 3 Results of χ2 tests for mortality equivalence among experiments with constant pulse energy but varied amounts of power and pulse duration (25, 50, and 100 ms).
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Comparing dosing performance across other insects
Using the same system and procedures, experiments were also run on SWD and ACP subjects as a way of cursorily exploring laser-insect interaction for species of interest besides A. stephensi. For SWD, a full dose–response curve was created for the 1 μm laser with typical exposure settings (2.5 mm spot diameter and 25 ms pulse duration). The LD90 fluence from this work was 8.6 J/cm2, compared with 12.9 J/cm2 for A. stephensi under the same conditions. Tracking performance on SWD subjects was comparable to that for A. stephensi, given that flight parameters such as speed and acceleration were comparable as well. With ACP subjects, inducing them to fly in the cages was very difficult. As such, we could not process a sufficient number of subjects to create a proper dose–response curve. Based on the limited data available, and as demonstrated in Supplemental Video 2, the system as set for the mosquito LD90 condition with the 1 μm laser, 2.5 mm spot diameter and 25 ms pulse duration was effective in tracking and disabling the ACP subjects, despite their very different flying behavior (greater speeds and accelerations, limited durations) relative to the other test species.
Longer range dosing system demonstration
As a final proof of principle test, a long-range version of the system from Fig. 1 was configured. This system worked at a distance of 30 m instead of 2 m, facilitated primarily by modifying the optics used for the coarse and fine tracking systems. Given the reduced flexibility of this system, it was only used with the 1 μm laser with a 2.5 mm spot diameter and 25 ms pulse duration. Rather than acquiring new dose–response curves, which was impractical given the setup’s location in a building ~ 30 km away from the insect-housing chamber, we verified the efficacy of the system using the LD90 conditions established by short-range dosing. Supplemental Video 3 shows this system dosing an A. stephensi subject from the same stock as used for short-range testing, and Supplemental Video 4 shows the same for a wild-sourced Culex pipiens mosquito obtained in a local pond. Note that the wild C. pipiens was substantially larger in size than the lab-reared A. stephensi. In all of the limited number of tests of this system for both species (10 A. stephensi and 5 C. pipiens), the mortality rate was 100%. More

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Total P and available P
Fertilizer application significantly (P  0.05). The highest increase in available P concentration in NPK + S treatment observed in the 60–100 cm soil depth, with the increase of 111% and 115% in 60–80 cm and 80–100 cm soil depths, respectively, over CK treatment.
Figure 2

Effect of long-term application of chemical fertilizer, organic manure, and straw on total phosphorus (P) and available P concentrations. * indicates significant difference at P  3.6%) was associated with OM treatment, especially at the 0–20 and 20–40 cm soil depths (Fig. 3). The PAC values under NPK, NPK + S and OM treatments increased by 7.6%, 4.5% and 11.5% in the 0–20 cm soil depth and 4.2%, 1.3%, and 5.8% in 20–40 cm soil depth, respectively as compared to the CK treatment. However, PAC value for soil depth below 40 cm showed the trend, NPK  More

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

References

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Díaz, S. et al. Science 366, eaax3100 (2019).

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Leclère, D. et al. Nature https://doi.org/10.1038/s41586-020-2705-y (2020).

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Montesino Pouzols, F. et al. Nature 516, 383–386 (2014).

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Kok, M. T. J. et al. Biol. Conserv. 221, 137–150 (2018).

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Leclère, D. et al. Towards Pathways Bending the Curve of Terrestrial Biodiversity Trends Within the 21st Century https://doi.org/10.22022/ESM/04-2018.15241 (Int. Inst. Appl. Syst. Analysis, 2018).

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Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. Science 360, 791–795 (2018).

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Driscoll, D. A. et al. Nature Ecol. Evol. 2, 775–781 (2018).

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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).

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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.
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