Combining remote sensing and tracking data to quantify species' cumulative exposure to anthropogenic change

Abstract Identifying when and where organisms are exposed to anthropogenic change is crucial for diagnosing the drivers of biodiversity declines and implementing effective conservation measures. Accurately measuring individual‐scale exposure to anthropogenic impacts across the annual cycle as they move across continents requires an approach that is both spatially and temporally explicit—now achievable through recent parallel advances in remote‐sensing and individual tracking technologies. We combined 10 years of tracking data for a long‐distance migrant, (common cuckoo, Cuculus canorus), with multi‐dimensional remote‐sensed spatial datasets encompassing thirteen relevant anthropogenic impacts (including infrastructure, hunting, habitat change, and climate change), to quantify mean hourly and total accumulated exposure of tracked individuals to anthropogenic change across each stage of the annual cycle. Although mean hourly exposure to anthropogenic change was greatest in the breeding stage, accumulated exposure to changes associated with direct mortality risks (e.g., built infrastructure) and with climate were greatest during the wintering stage, which comprised 63% of the annual cycle on average for tracked individuals. Exposure to anthropogenic change varied considerably within and between migratory flyways, but there were no clear between‐flyway differences in overall exposure during migration stages. However, more easterly autumn migratory routes were significantly associated with lower subsequent exposure to anthropogenic impacts in the winter stage. Cumulative change exposure was not significantly associated with recent local‐scale population trends in the breeding range, possibly because cuckoos from shared breeding areas may follow divergent migration routes and therefore encounter very different risk landscapes. Our study highlights the potential for the integration of tracking data and high‐resolution remote sensing to generate valuable and detailed new insights into the impacts of environmental change on wild species.

Summary statistics describing final tracking dataset filtered and categorised into seasons Table S2 Post-hoc tests of multiple comparisons carried out on models assessing effect of season on mean hourly and accumulated change exposure Table S3 Summaries of linear and generalized additive models assessing the influence of autumn flyway longitude on autumn and winter accumulated change exposure

Section S1
The following is adapted from Buchan et al. (2022): Composite mapping is complicated by the possibility that risks associated with exposure to certain change layers might be increasive but non-additive, meaning that the presence of multiple spatially contiguous change metrics may increase the total potential risk, but to a lesser degree than would be implied by direct summation of values (Kennedy et al., 2019).To account for this, we grouped change layers whose impacts were likely to be correlative or non-independent (e.g., human population density, roads and urbanization), and combined them using fuzzy algebraic sums (Theobald, 2013).The fuzzy algebraic sum of a set of values between 0 and 1 is given by 1 minus the product of (1 -x), where x is each member of the set, such that the final fuzzy summed value is less than the literal sum of its parts, and tends towards a maximum value of 1 (Bonham-Carter, 1994).In cases where change metrics were independent and therefore truly additive (e.g., threat posed by hunting pressure) we used simple linear summation.
While in Buchan et al. 2022, per-species susceptibility weightings are used to create relative vulnerability scores, these are not used here as we are quantifying exposure for a single species; the formulae are adapted/simplified accordingly: In all cases, where  is a cell: Where  (1 ≤  ≤ 5) indicates one of five non-independent direct mortality layers: {urbanisation, population density, roads, windfarms, powerlines}. !,# is therefore the value for change layer  in cell . ! and  !are, respectively, the nocturnal lights layer and the hunting value for small-bodied bird species in cell .() denotes the number of layers within , in this case five.
Where ℎ (1 ≤ ℎ ≤ 2) indicates one of the two independent habitat layers: {pesticides, urbanisation}, with  !,' being the value for layer ℎ in cell . ! is fertilizer in cell .(ℎ) denotes the number of layers within ℎ, in this case two.

𝐶𝑙𝑖𝑚𝑎𝑡𝑒 𝑐ℎ𝑎𝑛𝑔𝑒
Where  ),! is anomaly in precipitation in cell  during month ,  ),! is anomaly in precipitation variability,  ),! is anomaly in temperature and  ),! is anomaly in temperature variability.The following describes the model syntax for each of the analyses in the manuscript.
Between-season exposure: Model type Relevant tables/figures Mean direct mortality change exposure (log) ~ season + (1|birdID) Linear mixed effects Figure 3; Tables 1 and S2 Mean habitat change exposure (log) ~ season + (1|birdID) Linear mixed effects Figure 3; Tables 1 and S2 Mean climate change exposure (log) ~ season + (1|birdID) Linear mixed effects Figure 3; Tables 1 and S2 Migratory route: Accumulated autumn direct mortality change exposure (log) ~ longitude at 35° N Linear and additive Figure 4; Table S3 Accumulated autumn habitat change exposure (log) ~ longitude at 35° N Linear and additive Figure 4; Table S3 Accumulated autumn climate change exposure (log) ~ longitude at 35° N Linear and additive Figure 4; Table S3 Accumulated winter direct mortality change exposure ~ longitude at 35° N Linear and additive Figure 4; Table S3 Accumulated winter habitat change exposure (log) ~ longitude at 35° N Linear and additive Figure 4; Table S3 Accumulated winter climate change exposure ~ longitude at 35° N Linear and additive Figure 4; Table S3 Accumulated winter direct mortality change exposure ~ longitude at first winter fix Linear and additive Figure A1; Table A5 Accumulated winter habitat change exposure (log) ~ longitude at first winter fix Linear and additive Figure A1; Table A5 Accumulated winter climate change exposure ~ longitude at first winter fix Linear and additive Figure A1; Table A5 Breeding site abundance change: Mean site abundance change ~ accumulated autumn direct mortality change (log) Linear Figure S3; Table 2 Mean site abundance change ~ accumulated autumn habitat change (log) Linear Figure S3; Table 2 Mean site abundance change ~ accumulated autumn climate change (log) Linear Figure S3; Table 2 Mean site abundance change ~ accumulated winter direct mortality change Linear Figure S3; Table 2 Mean site abundance change ~ accumulated winter habitat change Linear Figure S3; Table 2 Mean site abundance change ~ accumulated winter climate change (cubed) Linear Figure S3; Table 2 Mean site abundance change ~ accumulated spring direct mortality change (log) Linear Figure S3; Table 2 Mean site abundance change ~ accumulated spring habitat change (log) Linear Figure S3; Table 2 Mean site abundance change ~ accumulated spring climate change Linear Figure S3; Table 2 Mean site abundance change ~ accumulated breeding direct mortality change (cubed) Linear Figure S3; Table 2 Mean site abundance change ~ accumulated breeding habitat change (log) Linear Figure S3; Table 2 Mean site abundance change ~ accumulated breeding climate change (log) Linear Figure S3;

Figure
Figure S1 Schematic illustrating how the stages of the annual cycle were defined

Figure
Figure S2 Common cuckoo population abundance change maps

Figure S1 -
Figure S1 -Schematic illustrating the geographic and behavioural criteria by which we defined the four

Figure S3 -
Figure S3 -Plot showing the relationship between mean breeding season mortality change exposure

Figure S4 -
Figure S4 -Autumn migration (green) and wintering tracks (yellow) of tagged common cuckoos from

Figure S5 -
Figure S5 -Scatterplots showing the relationship between mean hourly direct mortality (a), habitat change (b) and climate change (c) exposure in the autumn

Table S1 -
Summary statistics describing final tracking dataset filtered and categorised into seasons

Table 2 Table S2 -
Outputs of post-hoc tests of multiple comparisons carried out on models assessing the effect of season on mean hourly and accumulated change exposure for each of the three change types: direct mortality, habitat change, climate change.

Table S3 -
Summary of linear and generalized additive models and associated likelihood ratio tests assessing the influence of autumn flyway longitude on autumnand winter accumulated change exposure scores.Models in bold are presented in Figure3.Estimated degrees of freedom (edf) are presented for generalized additive models.