First assessment on the influence of wind farms and high-voltage networks on Ruddy-headed goose migration in Patagonia, Argentina

doi:10.21203/rs.3.rs-1638265/v1

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First assessment on the influence of wind farms and high-voltage networks on Ruddy-headed goose migration in Patagonia, Argentina

https://doi.org/10.21203/rs.3.rs-1638265/v1

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published 24 May, 2023

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Ruddy-headed goose Chloephaga rubidiceps has a migratory population that overwinters mainly in the Pampas region, Argentina, and breeds in Southern Patagonia. This population has decreased considerably, with less than 800 individuals remaining to date. We conducted the first assessment on the influence of environmental and anthropogenic impact (wind farms and high voltage networks) variables on Ruddy-headed goose migration pathways across the Patagonian coast by applying kernel density analyses and statistical procedures on satellite tracking data obtained from six Ruddy-headed geese during their migration pathways between 2015 and 2018. Five core distribution areas were identified during migration. During autumn migration, core areas were associated with high primary productivity and low elevation areas, while during spring migration they were located in the proximity of watercourses and waterbodies. We found that around 30% of the grid cells included in the high-density areas were located in the influence area of high voltage networks during both migrations. While 33% of the grid cells included in the high-density areas were in the influence zone of wind farms during autumn migration; while this applied to only 13% during spring migration.

We highlight areas of high risk along the distributional range of the species where large-scale patterns of collision mortality are likely to occur and mitigation measures should be prioritized. We suggest proactive measures that could mitigate future collisions with energy infrastructure because, given their threatened status, a few deaths may have a large effect on the small remnant population.

kernel density analyses

Ruddy-headed goose

high-risk areas

energy infrastructure

Patagonia

In the past decades, large wind farms have been built worldwide on land and offshore (Kleyheeg-Hartman et al. 2018). Wind power as an alternative source of renewable and clean energy to fossil fuels supports environmental sustainability and possibly provides part of the solution to global energy problems (Pryor and Barthelmie 2011, 2010). However, the collision of birds and bats with wind turbines is considered one of the main ecological downsides of wind power generation (Barrios and Rodríguez 2004; Drewitt and Langston 2006; Hüppop et al. 2006; Heuck et al. 2019). Factors that influence collision risk can be divided into three categories: those related to the species, to the configuration and location of structures and to the environment (Jenkins et al. 2010). Species-related factors comprise habitat use, body size, flight behaviour, age, sex, and flocking behaviour (Marques et al. 2020). Wind turbine location affects birds mainly by displacement as a result of disturbance, habitat fragmentation and/or habitat loss, as well as by collision with rotor blades (Farfán et al. 2009; Stevens et al. 2013; Watson 2018; Gómez-Catasús et al. 2018). There are many studies that quantified the effects of environmental factors, such as wind direction and speed, and landscape elements like topographic features, on avian collision rates with wind turbines (Heuck et al. 2019). Generally, risk assessment analysis combined models of habitat use based on bird movement with data on current or planned wind farms to make spatial predictions of collision risk (Mojica et al. 2016; Vasilakis et al. 2017). One of the assumptions of this analysis is that collision mortality increases in densely populated habitats (e.g. Golden eagle Aquila chrysaetos (Tapia et al. 2009); Griffon Vulture Gyps fulvus (Carrete et al. 2012); Cinereous Vulture Aegypius monachus (Vasilakis et al. 2017); White-tailed eagle Haliaeetus albicilla (Heuck et al. 2019)).

The Ruddy-headed goose, Chloephaga rubidiceps, which is the smallest of the five South American sheldgeese, has two separate populations: one sedentary, which resides in Falkland/Malvinas Islands, and one migratory (the mainland South American population) that overwinters mainly in Southern Buenos Aires province (Pampas region, Central Argentina) and breeds in Southern Patagonia (Argentina and Chile, Fig. 1) (Pedrana et al. 2020). New findings postulated that these two populations are genetically isolated as they do not share mtDNA haplotypes (Bulgarella et al. 2014) and have dissimilarities based on their nuclear DNA (Kopuchian et al. 2016). Still, the IUCN Red List considers the Ruddy-headed goose as least concern (BirdLife International 2016) due to the fact that this species has a large global population that is estimated to be 43,000–82,000 individuals (Woods and Woods 2006). While the Falklands/Malvinas population appears to be of least concern (Woods and Woods 2006; BirdLife International 2016), the population on mainland South America has decreased considerably and current estimates indicate that the population size is less than 800 individuals, around 10% of the estimated population in the 1900s (Cossa et al. 2017; Pedrana et al. 2018a). As a result, this population has been classified as critically endangered in Argentina (AA/AOP and DFS/SAyDS 2008) and endangered in Chile (CONAMA 2009), and was declared a ‘Natural Monument’ in Buenos Aires and Santa Cruz provinces (Argentina). In order to study the subsequent migration pathways and their interaction with energy infrastructure (power lines, wind turbines) and urban areas, six Ruddy-headed geese were captured in their main wintering grounds, equipped with satellite devices between 2015 and 2016, and their movements followed for at least three consecutive migration cycles (Fig. 1; Pedrana et al. 2020).

Pedrana et al. (2020) provided the first documentation of the complete migration cycles of this species using satellite technology and identified some stopover sites along their flyway, breeding and wintering grounds. The authors demonstrated that tracked individuals used the eastern Patagonia migration route along the Atlantic coast (Pedrana et al. 2018b, 2020). However, results on the Ruddy-headed goose migration routes also provided evidence for a range of potential threats associated with energy infrastructure (power lines, wind turbines) and urban areas. Commercial wind energy production has been operating in Argentina for the last decade. Currently, there are new wind farms installed all along the Atlantic coast, mainly along the Patagonian shoreline (Palmer et al. 2017). For instance, the Madryn Wind Farm (15 km from Puerto Madryn City, Chubut province, Argentina and 80 km from the UNESCO World Heritage Centre Peninsula Valdés) has 62 wind turbines, the biggest in Argentina and one of the largest in South America. Understanding migration across vast landscapes is essential for the identification of factors affecting the survival of this endangered population and especially for identifying potential hazard zones with human infrastructures along their migration routes. In addition, the impact of collision mortality on Ruddy-headed goose populations has been one of the main concerns of recent planning applications for wind farms (Palmer et al. 2017).

Given their threatened status and the potential vulnerability to wind energy infrastructure and the associated high-voltage network (hereafter HV network), there is an urgent need to delineate proactive measures that could potentially mitigate future collisions involving Ruddy-headed goose. In this context, we conducted the first assessment on the influence of environmental and anthropogenic impact variables (wind farms and HV networks) on Ruddy-headed goose migration pathways across the Patagonian coast by applying kernel density analyses and statistical procedures on satellite tracking data. Our specific objectives were: 1) To identify important areas of high density during their migration pathways thorough kernel density analyses and integrate this information in the spring and autumn migration maps; 2) to determine which environmental (topographic and vegetation indices and availability of waterbodies and watercourses) and anthropogenic-impact (proximity to wind farms and HV networks) variables affect kernel density estimation categories; and 3) to identify potential hazard zones along the species distributional range where conservation and mitigation measures should be prioritized.

Study area and Ruddy-headed geese tracking data

The mainland South American population of Ruddy-headed geese overwinters in the Southern Pampas located in the southeast of Buenos Aires province, Argentina (between 36° to 41° S and 63° to 58° W, Fig. 1) (Pedrana et al. 2020), a temperate grasslands ecosystem and one of the richest agricultural areas of the world (Soriano et al. 1991). In the wintering areas, this species is usually associated with Upland C. picta and Ashy-headed C. poliocephala geese (Pedrana et al. 2014, 2018a, b). All three species leave the Pampas region in August-September when they start the spring migration (Fig. 1) and are known to migrate in large numbers as indicated for example by bird ringing activities (Lucero 1992; Rumboll et al. 2005) or by call recordings at National Parks and reserves (Giusti et al. 2020). Ruddy-headed geese remain in their breeding areas, in southern Patagonia, during summer for at least seven months (Pedrana et al. 2020). During May and June individuals migrate during austral autumn from their breeding grounds to their wintering areas (Fig. 1).

We caught six individuals of Ruddy-headed goose using foot-noose carpets in their wintering area (38°33’S; 59°42’W) between 8–28 August 2015 and between 20–21 July 2016 (for more details see Pedrana et al. 2020). Directly after capture, birds were equipped with solar-powered satellite transmitters (Solar PTTs, Model 23GS, 23–28 g solar PTT, North Star ST LLC, USA) which were attached to the birds’ back using a Teflon harness (Humphrey and Avery 2014). PTTs weighed 0.033 Kg, including harness, representing less than 1.8% of the adult body mass (min-max = 1.61–2.14 Kg, n = 6, Pedrana et al. 2020), well under the maximum of 3% recommended to avoid any adverse effects of the application on bird behaviour (Casper 2009). Birds were released as soon as possible after marking, typically within 20 minutes, back at their capture locations. PTTs were programmed to transmit with a duty cycle of 6h on/ 18h off (local time, GMT-3). Geographical locations were provided by the Argos service with location accuracy (class designation) calculated using the Kalman filtering method (ARGOS 2016). For this study, we only used Argos locations (high class positions) with accuracy classes 1, 2 and 3 (accuracy ≤ 1,000 m). Positional data was incorporated into a Geographical Information System.

Kernel density estimation (KDE)

For this study, we defined wintering grounds as places used by Ruddy-headed goose from May to August inside the southeast of Buenos Aires province boundaries (Tres Arroyos and San Cayetano districts; Fig. 1). The breeding grounds were considered as sites used by the species from September to May in Patagonia. For the spring (southbound) migration, we considered all positions between the last positions in the wintering grounds until arrival at the breeding grounds; and for the autumn (northbound) migration, we considered tracks from the last position obtained in their breeding ground until arrival in the wintering area.

We identified high use areas during spring and autumn migrations using kernel density estimators (KDE) (Wood et al. 2000). The KDE provides a probability indicating the relative proportion of the distribution within a particular area. After a prospective first passage time analysis (Lascelles et al. 2016) and taking into account the foraging range of the species and the maximum distance travelled by the birds (Pedrana et al. 2014, 2020), a smoothing parameter (h) of 15 km was selected over a grid with cell size of 1 km².

We categorized KDE into three percentile regions corresponding to a) 50% kernel which was defined as core region (hereafter core area), b) 75% kernel which was defined as the focal region (hereafter focal area), and c) 95% kernel which was considered as the range area (dispersal area) of the locations (Wood et al. 2000). Core areas were used as a proxy for the definition of important areas during migration pathways. All analyses were performed using adehabitat package (Calenge 2006; Calenge et al. 2018) in R (R Development Core Team 2017), Geospatial Modelling Environment (GISGeography 2015) and ArcGIS 10.8 (ESRI, Redlands, CA, USA).

Environmental and anthropogenic predictors

As environmental predictors, we calculated the minimum distance of the grid cell to the nearest waterbody (lakes, ponds and flooded areas), and moving water (rivers and streams) in a GIS (Table 1). We acquired topographic parameters (altitude) from the Shuttle Radar Topography Mission and normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board NASA’s Terra and Aqua spacecraft (Table 1). NDVI satellite images were provided every 16-day at 250-m spatial resolution (http://modis-land.gsfc.nasa.gov/vi.html). We calculated Mean NDVI per year using 24 scenes of satellite from 2005–2008. Then we summed all four mean NDVI map and divided by 4 to obtain the overall mean NDVI (Table 1).

Table 1

Description of the environmental and anthropogenic predictors used to identify important areas of high-density during Ruddy-headed goose *Chloephaga rubidiceps* migration pathways.
Variable name	Description and source
Altitude	Mean elevation in meters above sea level of the focal cell obtained from the Shuttle Radar Topography Mission Elevation (SRTM, http://www2.jpl.nasa.gov)
D_water	Minimum distance to the closest waterbody (km), calculated using the “distance-tool” in Idrisi Selva
D_stream	Minimum distance to the closest body of moving water (km), calculated using the “distance-tool” in Idrisi Selva
NDVI	Normalized difference vegetation index (NDVI) derived from MODIS sensor, calculated using 24 scenes per year (from 2015 to 2018) (http://modis-land.gsfc.nasa.gov/vi.html).
Wind_farm	Categorical variable indicating if the kernel cell lay inside the wind farm polygon (In), inside the 20-km buffer (Buffer) or outside the area of influence of the wind farm (Out).
HV_network	Categorical variable indicating if the kernel cell lay inside the high-voltage network (In), inside the 5-km buffer (Buffer) or outside the area of influence of the high-voltage network (Out)

For the anthropogenic predictors, we used the location and extent of each wind farm and HV network and generated a buffer of 5. We built a categorical variable indicating whether the grid lay inside the wind farm or HV network (In), inside the buffer zone around wind farm or HV network (Buffer) or outside the area of influence of wind farm or HV network (Out) (Table 1).

Statistical framework

For spring and autumn migration we aimed to determinate which environmental and anthropogenic predictors were most likely to affect the frequency of grid cells of each KDE categories (50%= core area, 75%= focal area, and 95%= dispersal area) using Chi-squared test for categorical variables and Analysis of Variance (ANOVA) to compare the three categories using quantitative variables, such as altitude, NDVI, and distance to waterbodies and streams (Table 1). In the case of wind farm categories, we gathered two levels (In + Buffer) in only one level due to the small number of observations in each group. Since the normality and homogeneity of the variance’s assumptions were not satisfied, the nonparametric version of the ANOVA, the Kruskal-Wallis test, was performed. After obtaining statistically significant Kruskal-Wallis results, we used Pairwise Wilcoxon test to determine which pair of variables were significant. All analysis were executed using stats, ggplot2 and ggstatsplot packages in R.

Kernel density maps

During autumn migration, we identified five core areas of distribution and all of them were located in the south and southeast of Buenos Aires province (Fig. 2a, 3a). Thus, important areas were exclusively located near their wintering grounds at the end of the autumn migration period (Fig. 2a, 3a).

In contrast, during spring migration we identified five core areas of distribution (Fig. 2b, 3b): Two of them were located in southern Buenos Aires province near the main wintering grounds and three of them in Santa Cruz province (southern Patagonia) near the breeding grounds in the Magallanes region, southern Chile.

Influence of environmental and anthropogenic variables on autumn and spring migration

During autumn, we found a significant relationship between KDE and HV network categories (X² = 206.34, df = 4, p = 1.63e-43, V-Cramer = 0.09). More than 30% of the grid cells included in the core and focal areas were overlapped with the In and Buffer zone of the HV network (Fig. 4a). We obtained an important relationship between KDE categories and wind farm categories (X² = 220.16, df = 2, p = 1.56e-48, V-Cramer = 0.14). 33% of the grid cells included in the core and focal areas were inside the wind farm (In + Buffer, Fig. 4b). Based on the Kruskal-Wallis test, we concluded that at least one KDE category differed in terms of altitude (X² = 550.59, p < 2.2e-16), NDVI (X² = 545.84, p < 2.5e-16), and proximity to watercourses (X² = 70.45, p = 5.035e-16) and waterbodies (X² = 28.889, p = 5.332e-07). Based on the Pairwise Wilcoxon test, we determined that all three KDE categories were significantly different taking into account altitude, NDVI, distance to watercourses and waterbodies (Appendix A, p < 0.001). Moreover, core areas were mainly located in regions with low elevation terrain and high primary productivity (high NDVI) (Fig. 5a).

During spring, we established a significant relationship between KDE and HV network categories (X² = 902.60, df = 4, p = 4.55e-194, V-Cramer = 0.13). Around 30% of the grid cells included in the core and focal areas, were inside or in the buffer zone of the HV network (Fig. 6a). We found a significant association between KDE and wind farm categories (X² = 120.74, df = 2, p = 6.04 e-27, V-Cramer = 0.07). It appeared that most of the grid cells were outside the influence of the wind farms, although nearly 15% of them were associated to the core and focal areas that spatially overlapped with the influence of this human infrastructure (Fig. 6b). Moreover, we found that the three KDE categories differed regarding their relationships to altitude (X² = 550.59, p = 2.4e-12), NDVI (X² = 414.75, p = 3.2e-10), proximity to watercourses (X² = 63.66, p = 1.94e-14) and proximity to waterbodies (X² = 374.45, p = 2.3e-157). The results from the post-hoc tests showed that core and focal areas were not significantly different to distance to waterbodies (KDE 50 vs. 75: p = 0.87) and watercourses (KDE 50 vs. 75: p = 0.66) and all three KDE categories differed in terms of altitude and NDVI (For all KDE categories comparisons: p < 0.01, see Appendix A). During spring migration, core and focal areas were located mainly in regions closest to watercourses and waterbodies in comparison to dispersal areas (Fig. 5b).

High-risk areas of conflict with anthropogenic infrastructures

Individuals travelled mostly over the sea during autumn migration in contrast to spring migration, where tracked Ruddy-headed geese encountered at least two high-risk areas of conflict within terrestrial wind farms (Fig. 2a). During spring migration, the analysis of core and focal region distribution in relation to wind farms indicated that one high-risk area is located at the southern tip of Buenos Aires province, near the main wintering grounds, and another one located to the north of Santa Cruz province (Fig. 2b).

Concerning high-risk areas of conflict within HV network areas during autumn migration, the most important one was located to the south of Buenos Aires province (Fig. 3a). HV networks run all along the Atlantic Coast generally overlapping with spring migration pathways of Ruddy-headed goose (Fig. 3b).

Traditionally, conservation measures for endangered species have been applied based on land use and water quality changes. As recent as 2021, Lambertucci and Speziale (2021) introduced the concept of aerial habitat and aeroconservation. These authors proposed to include aspects of the airspace, extending from the land or water surface to the upper limit of the troposphere, in conservation, biodiversity and managements assessments (Lambertucci et al. 2015). Migratory species, such as Ruddy-headed goose, use airspace to travel between breeding and wintering grounds every year. Therefore, the identification and impacts occurring in this newly defined habitat might be crucial for the survival of these species. To the best of our knowledge, this study is the first to identify potential high-risk areas situated along the flyway of the Ruddy-headed geese based on satellite tracking data.

During autumn, important areas during migration pathways were mainly located in the south of Buenos Aires province. These areas in the Pampas region were categorized by Pedrana et al. (2014) as highly suitable for all sheldgeese species and are characterized by low elevation, high mean productivity and proximity to streams, lakes and/or ponds. Similarly, Pedrana et al. (2020) found that stopover sites of Ruddy-headed goose were also located in southern Buenos Aires province and that they mostly used areas far away from urban areas and mainly characterized by a low elevation terrain interspersed with streams and lakes. In Argentina, Martin et al. (1986) and Blanco et al. (2003) also described this association between sheldgeese and productive grounds and proximity to waterbodies, which may indicate the distribution of higher-quality foraging grounds and their strict dependence on water in their roosting sites. These relationships have been described for other geese species all around the world, which feed and rest in the proximity of waterbodies and wetlands that offer them not only a suitable place to feed, but also provide protection from potential predators (Carboneras 1992; Summers and Mc Adam 1993).

During spring migration, we found that important areas during migration pathways were located near the wintering grounds; the same was reported during autumn migration, and also near the breeding grounds in southern Patagonia. These areas were associated with low elevation, high mean productivity and proximity to waterbodies and streams. Most of these areas were categorized by Pedrana et al. (2011) in Patagonia and Pedrana et al. (2014) in the Pampas region as medium to high suitability areas for sheldgeese occurrence. Likewise, other studies found these associations of sheldgeese with wetlands and streams (Summers 1985; Martin et al. 1986; Summers and McAdam 1993). Although it is not clear that geese respond to topography per se, altitude might be correlated to certain landscape components or soil quality features (more productive environments typically occur in lowlands) which may have implications for goose habitat selection (Wisz et al. 2008).

In this study, a third of the overall area during spring and autumn migration categorized as high-density was located in the influence of the HV network, indicating a potential threat to Ruddy-headed geese. Rees et al. (2005) documented the same behaviour in Whooper Swans Cygnus c. cygnus. Some groups, such as seaducks are known to be particularly sensitive to disturbance from wind farms (Percival et al. 1997; Drewitt and Langston 2006), which could be the case of the Ruddy-headed goose.

The main negative effects of wind farms and HV networks may occur through disturbance, displacement during construction and maintenance as well as from improved road access as a result of the wind farm development, especially in areas where there was little human activity before these human infrastructures were built (Drewitt and Langston 2006, 2008, Bernardino et al. 2018, Marques et al. 2020). Given that the Ruddy-headed goose population is listed in Appendixes I and II of the Convention on the Conservation of Migratory Species of Wild Animals (CMS), more work is required to understand the extent to which the displacement of this species might translate into a significant population-level impact. It is important to mention that the satellite devices deployed in our study did not register the flight height. Future studies should take flight height into account to calculate the probability that birds might fly in the rotor-swept zone of wind turbines, a proxy for collision hazard (Péron et al. 2017).

Other adverse effects may include habitat loss/degradation, displacement from feeding areas, barrier effects, and disturbance (Powlesland 2009). For example, in Denmark, (Larsen and Madsen 2000) found that wintering Pink-footed geese Anser brachyrhynchus maintained a distance of about 100 m from single turbines or rows of turbines, and about 200 m from clusters of turbines. Similarly, Fox et al. (2006) highlighted the impact of the wind farm on reducing the local abundance of birds by displacing individuals to already occupied or otherwise unsuitable habitat.

Travelling over sea during autumn migration might decrease collision risk. Although, the new offshore wind farm building intentions that are increasing along the Patagonian Shelf, might cause a negative effect on Ruddy-headed goose migratory population (Palmer et al. 2017). In contrast, during spring migration, Ruddy-headed geese tracked flew over land and encountered at least two high-risk areas of conflict within terrestrial wind farms and HV networks: one located near the wintering grounds in Buenos Aires province and another one located in southern Patagonia, Santa Cruz province, Argentina. Large birds with poor manoeuvrability (such as swans and geese) are generally at higher risk of colliding with structures such as turbines and power-lines, which may directly impact on population sizes through additional mortality (Brown et al. 1992). Equally, these human-built structures may be perceived by birds as a barrier, demanding additional energy invested in flight to avoid the obstacle. Thus, potentially increasing energy expenditure can affect breeding success and survival (Madsen et al. 2014; Peschko et al. 2020). Even low numbers of anthropogenic induced fatalities of certain species can be additive to other causes of mortality and significantly reduce their population (Watson 2018). Moreover, turbines may disturb the foraging and breeding of waterfowl (Drewitt and Langston 2006) resulting in habitat loss (Larsen and Madsen 2000). Therefore, if we add collision risk to birds that are of conservation concern, such as the Ruddy-headed goose, a few deaths may have a large effect on a small remnant population.

Although we tracked almost 1% of the migrating population, our findings should be taken with caution. However, our results are essential for future management plans to prevent potential human-sheldgeese conflicts to escalate along their migration route. As mentioned before, Rudy-headed geese gather in large groups to start migration. So, even though we equipped only six individuals and tracked them for at least three consecutive migration cycles, we expect that more individuals were migrating together with the tracked ones. Complementary to tracking data, we believe it is important to perform ground surveys in breeding and wintering areas as well as in the stopovers. Another important finding associated to the deployment of satellite transmitters was the identification of four stopovers inside national parks boundaries (e.g. Monte León National Park, Bosque Petrification de Jaramillo National Park, Makenke Marine Interjurisdictional Park and The Patagonia Coastal Marine Interjurisdictional Austral) in agreement with Chebez et al. 1998. This information highlights the importance of protected areas along the Argentinean coastline. After these findings, a new agreement was signed with the National Park Administration to develop a monitoring program of these areas to record the areas used by the species during autumn and spring migration (Agreement EX-2020-73020515-APN-DGA#APNAC: INTA - APN Agreement Project for Ruddy-headed goose monitoring program). Furthermore, it is important to monitor protected areas as well as potential high-risk areas to record collision with wind turbines or HV lines. Further studies should include increasing numbers of sampled individuals and gather more information on migratory flyways or local flight paths, which would improve our understanding of wind farms and HV networks on Ruddy-headed geese. There is clear evidence that the probability of bird collisions with turbines and HV networks depends critically on species behaviour, especially flight behaviour, topographical factors and weather (Barrios and Rodríguez 2004; Drewitt and Langston 2008; Bernardino et al. 2018; Heuck et al. 2019; Marques et al. 2020). Therefore, further studies should focus on these factors.

During the development of the "Workshop on Good Environmental and Social Practices for the Wind Sector" held in Buenos Aires in March 2017, it was concluded that some birds’ species have high priority according to the risk of impact that wind farms can cause on them. These species were categorized according to their conservation status (IUCN Red List of Threatened Species and BirdLife International). In addition to the Ruddy-headed goose, other high-priority Patagonian species found on this list include the Andean condor, Vultur gryphus, the Hooded Grebe, Podiceps gallardoi, the Ashy-headed and Upland goose, the Chilean Flamingo, Phoenicopterus chilensis, the Magellanic plover, Pluvianellus socialis and the Red knot, Calidris canutus. Consequently, the potential hazard zones proposed on this study may also contribute to the conservation of other endangered species facing the same threats along the migration pathways. Ruddy-headed geese could act as umbrella species determining the minimum size for conservation areas, selecting sites to be included in reserve networks, and setting minimum standards for the composition, structure, and processes of ecosystems.

Management implications

A greater understanding of the mechanisms involved in influencing the collision risk for this endangered species is essential if the situation is to be managed effectively. As the number of wind farms increases, the need for effective mitigation measures becomes more and more important, particularly in the case of a species where there are serious conservation concerns.

Mitigation measures such as making wind farm and HV network areas unattractive for foraging – while offering attractive habitat outside these places – will probably reduce the amount of time the birds spend inside risk areas as suggested by (Grajetzky and Nehls 2017). Other approaches used to minimize turbine collision include temporary turbine shut-downs upon approach by threatened species, such as the California Condors Gymnogyps californianus (Watson 2018).

Our results suggest that high-density areas of Ruddy-headed goose are characterised mainly by low-lands with high productivity indices near watercourses and waterbodies. Therefore, the new installation of wind farms and power networks should avoid areas with these environmental conditions along the Atlantic Coast. Hence, it is important to evaluate the landscape configuration before and after construction against the behavioural trade-offs the birds have to make to utilize patches, such as foraging patch quality and perceived predation risks as well as travel costs (in terms of energetic costs and predation risk) (Jensen et al. 2008; Tombre et al. 2008). In this sense, applying predictive models and future scenarios could help predict land-use and climate changes (Nicholson et al. 2019). The planning of new wind farms should consider these future scenarios to reduce long-term negative effects, help assess cumulative impacts, and develop efficient management plans.

Ruddy-headed geese perform spring migration mainly over land, and this might be seen as a huge threat for the species. Spring migration is also faster than autumn migration and tracked geese arrive at their breeding sites less than two weeks after departing their wintering grounds (Pedrana et al. 2020). Therefore, one mitigation measure might be to shutdown turbines during this period (between 12 August and 15 September). Understanding when and how environmental predictors affect the decision to start spring migration could help to improve this mitigation measure.

Wind energy production is necessary to mitigate the effects of global warming. However, increasing wind power production needs to be accompanied by scientifically justified mitigation measures to minimise negative effects on wildlife. Therefore, there is an urgent need to identify important areas, and to designate them as protected areas so that informed decisions can be made on the location of wind farm developments. For instance, core areas during migration such as the ones identified in this study along the Patagonia coastline should not be considered for new developments. Research and monitoring should be implemented by the national government and the wind energy industry, in consultation with relevant experts, to expand our understanding of the impacts of wind farms. This will be an iterative process that will inform decision-making, appropriate site selection and wind farm design. Some of the topics that should be encompassed in research and monitoring requirements are the following: effects and potential population level impacts on birds of disturbance displacement, barriers to movement, collision mortality, habitat loss or damage and the effectiveness of different wind farm layouts and turbine designs to provide mitigation.

Acknowledgements We thank Nicolas Charadia, Leandro Olmos, Juan Pablo Isacch, Gabriel Castresana, Alejandro Leiss, Pablo Rojas, Melina Lunardelli and Daniel MacLean for their generous help with fieldwork and logistical support. This work was funded by Antarctic Research Trust, INTA (PNNAT-1128053) and National Agency for Science and Technology, Argentina (PICT 2012- 0192). Procedures for capture and handling were approved by the Buenos Aires Provincial Agency for Sustainable Development (OPDS), Argentina (N° 0023352/18).

Author contributions Each author provided the following contributions: JP conceived the study, JP and AG wrote the manuscript, JP, AG, KP, and LB conducted field work. All authors contributed to the manuscript editing and approved the final draft.

Data availability The datasets analysed during the current study are available from the corresponding author on reasonable request

Conflict of interest The authors declare they have no conflicts of interest (both financial or non-financial) relevant to the content of this article, and that the study was conducted in accordance with the ethical standards of their own institutional and national research committee

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First assessment on the influence of wind farms and high-voltage networks on Ruddy-headed goose migration in Patagonia, Argentina

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Abstract

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Introduction

Methods

Study area and Ruddy-headed geese tracking data

Kernel density estimation (KDE)

Environmental and anthropogenic predictors

Statistical framework

Results

Kernel density maps

Influence of environmental and anthropogenic variables on autumn and spring migration

High-risk areas of conflict with anthropogenic infrastructures

Discussion

Management implications

Declarations

References

Additional Declarations

Supplementary Files

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