Breeding data were collected opportunistically over a large area (ca. 170,000 km²) of temperate southwestern South Africa (29°-34°S; 17°-27°E) from 2000 to 2011. More focused studies took place along the west coast of the Western Cape Province and inland in the Northern Cape Province around Nieuwoudtville (31°19’S; 19°05’E) first from 2000 to 2002, and then from 2012 to 2014. Nests were located in and around national parks (i.e. South African National Parks – SANParks), provincial protected reserves (i.e. Cape Nature), or on private lands. They were spread across a mosaic of different biomes with diverse habitats and vegetation types, many of which are nationally and internationally protected and considered of high biological and ecological values (see e.g. Manning 2007). Climate across the study area varies between provinces: the west of the Eastern Cape, and Western Cape have a more temperate climate and a winter rainfall regime (April to September), while the coastal Northern Cape also experiences a winter rainfall regime but with more fluctuating temperatures (South African Weather Services: http://www.weathersa.co.za).

Black Harriers are ground-nesting birds and, unlike other raptor species, breeders rarely re-use the same nest over the years (Simmons et al. 2005). It is as yet unclear if the same individuals breed together as a pair year after year, although some evidence suggests this is not the case (Garcia-Heras et al., unpublished data). Breeding sites were located by observing areas where Black Harriers were previously known to breed and/or where perched adults were detected. As in other raptor species, the females take care of the chicks at the nests and perform all brooding, while the male captures and provides the food in the early nestling period (Simmons 2000, Redpath et al. 2002a). Thus, nests were located by following prey-carrying males and observing where females landed after a food pass (Simmons 2000).

After discovery, nests were visited regularly (usually 2–3 times per breeding event) where possible to assess nesting success and productivity. However, because of the extensive nature of the study area, not all breeding areas and nest sites were monitored consistently each year, and for some remote areas, nests sites were only visited once, or were last visited prior to fledging. During each nest visit, we noted the nest contents (i.e. number of eggs or nestlings) and, if the nests contained nestlings, a visual estimate of age was taken. In a subsample of nests, wing, tail and tarsus length (mm), and mass (g) of chicks were measured. Nest visits were kept as brief as possible (< 20 min) and an effort was made to leave the vegetation around the nest undisturbed. The location of nests was recorded using a global positioning system (GPS). A total of 490 nests were located between 2000 and 2014, although not all variables examined in this study were available for each breeding attempt, so sample size varies among analyses.

Lay dates were estimated by subtracting 31 days (Simmons et al. 2005) from hatch date, which was in turn estimated either directly when a clutch was found with an egg hatching or a newly hatched chick (aged 1–3 days old) or indirectly from nestling age. Nestling age was estimated either visually (see above) or through body measurements (using data from a subsample of nests that were visited more regularly, we could build growth curves of wing-length for this species, Garcia-Heras et al. unpublished data). Given the variation in precision of lay dates among nests, we finally attributed the laying date for each nest to a 15-day period (where 1 = 1–15 May, 2 = 16–31 May, etc., up to 15 = 1–15 December). For the sake of simplicity, we henceforth refer to these lay date periods as “lay date” even though they are not exact dates. Lay date could not be determined for nests located during the incubation period and visited only once or that failed before the second visit (n = 70), or for nests discovered after fledging or for breeding records without a precise visit date (n = 18); therefore, data from these nests were excluded from the breeding phenology analyses. Overall, lay date was estimated for 402 breeding events.

Clutch size was defined as the maximum number of eggs laid. When possible, nests were visited twice during the incubation period with the second visit timed to coincide with the estimated date of hatch. This ensured that we recorded the exact number of eggs laid per breeding event. Nests that were visited before the clutch was finished and that subsequently failed, or only during the nestling period were excluded from clutch size analyses. Clutch size was known for 191 breeding attempts.

Breeding output was measured in two ways, nesting success (known for n = 263 breeding attempts) and productivity (n = 261). Nesting success was classified as 1 for those nests where at least one young was raised to 35 days old, or 0 otherwise. Productivity was defined as the number of young reaching 35 days of age (range 0–4) for pairs that laid a clutch. Black Harriers fledge at approximately 40 days old (Simmons et al. 2005) but in many cases our last visit occurred before that age; however, in harriers, as in many other species there is usually little mortality during this late nestling stage (Redpath et al. 2002a). Thus, we assume that any nestlings alive at 35 days old would have fledged.

Nest coordinates were incorporated in a geographical information system (QGIS Valmiera 2.2.0), projected on WGS84-UTM-34S as the coordinate reference system. Using this GIS, we calculated and identified the following variables for each nest: i) Altitude, from the Shuttle Radar Topography Mission (SRTM) 90 m Digital Elevation Database v4.1 (Srtm90m). ii) Region (coastal and interior-mountain) was defined using a combination of nest altitude (from SRTM) and distance to the coast. Coastal nests were defined as those located within 15 km from the coast and with a maximal altitude of 100 mASL (n = 328). Nests located further than 15 km from the coast and with an altitude higher than 100 mASL were considered as interior-mountain (n = 146). However, this classification excluded nine nests that were located higher than 100 mASL (average of 118 m), but within 15 km from the coast and for the purpose of our analysis these were classified as coastal. Another eight nests were located at an altitude lower than 100 m, but 45 km from the coast, and these were classified as interior-mountain. In both cases, we believe our classification to more accurately describe conditions for those 17 nests. This regional classification was initiated by Curtis et al. (2004) to explore regional differences in lay dates and productivity of Black Harriers. That study also further differentiated between nests in mountain and interior-lowland areas. However, overall sample size of interior-lowland areas was too small to allow meaningful comparisons, and so these two categories were grouped together as a single region (interior-mountain) for our study.

Weather data were obtained for the period 2000–2014 from 17 weather stations distributed throughout the study area (source: South African Weather Services: http://www.weathersa.co.za) (Figure 1). For some stations, weather data were lacking in certain months or years (due to technical problems or stations not being active at the beginning of our study period). For each weather station, we obtained daily rainfall (mm) and daily maximum and minimum temperature (°C). From these, we calculated monthly averages for daily temperature and daily rainfall for all the weather stations and years when data were available. Each weather station was classified as “coastal” or “interior-mountain” depending on its location, using the same criteria as for nests. We attributed to each nest the weather data from the nearest weather station located within the same region. An exception was however made for 18 nests located in interior-mountain regions, but for which the corresponding closest weather station was located 230 km away: for these we instead used the closest coastal weather station, as the distance between these nests and this weather station was relatively small (i.e. between 35 and 70 km away), and because they had a similar altitude. On the other hand, we excluded weather data entirely for 6 nests for which the distance between them and their closest weather station was further away than 120 km. Overall, our sample size included 475 nesting events with associated weather data from weather stations that were located on average 29 ± 22 (SD) km away from study nests (Figure 1).

Figure 1.

Location of study nests within South Africa for which weather data were available (n = 475), during 2000–2014. White circles: coastal nests; dark grey circles: interior-mountain nests; black triangles: weather stations that provided data for the purpose of the study.

All statistical analyses were conducted using R 3.2.3 (the R Foundation for statistical computing 2015).

To reduce the number of weather variables and to account for potential collinearity among them, we conducted a Principal Component Analysis (PCA) on monthly rainfall and temperature data for each station and study year. We selected the first four weather Principal Components (PCs) for subsequent analyses (a scree plot showed a marked drop in explained variance between the fourth and the fifth PC). PCs were chosen for analyses on the effect of weather on breeding parameters, rather than using raw weather data, because we did not have a strong a priori hypothesis of the time period over which weather may be more influential. Therefore, using raw data would have meant exploring the effect of a high number of potential explanatory variables (weather over different time periods). Furthermore, our PCs had clear biological meanings (see results), which helped in interpreting the relationships found. However, because PCs include information about weather in all months, in our discussion we placed most emphasis on the meaning of each PC for the months prior to the variable in question (e.g., for the relationship between lay date and weather, we focus on the meaning of each PC for the months prior to laying, not subsequently).

We investigated regional differences in the weather PCs using General Linear Mixed Models (GLMMs, statistical package lme4, Bates et al. 2012) that included the weather station identity as a random effect (to take into account the non-independence of the data coming from the same station) and the factors region (coastal vs. interior-mountain) and year (14 levels) as explanatory variables. This analysis was run on a data set that had only one data point for each weather station and year (n = 88).

To analyse factors affecting variation in breeding phenology, we used GLMMs that included year as a random effect, so that we could identify patterns that would describe what happens in an average year. The “lay date” of each nest (response variable) was fitted with a Gaussian distribution and an identity link function. The initial model included the explanatory variables of region and weather variables (the first four weather PCs). These models were conducted on a subsample of 393 nests for which both lay date and weather data were available.

GLMMs with year as a random effect were also used to explore clutch size, nesting success and productivity (response variables) in relation to region, lay date, and weather (explanatory variables). Initial models also included the interaction between region and lay date to look for regional differences in seasonal variations in breeding performance. For models where this interaction was significant, we re-ran the same model but without the interaction to test for differences between regions. Nesting success was fitted with a binomial distribution, and clutch size and productivity were fitted with a Gaussian distribution. Even though the latter may not be ideal for productivity data, using a Poisson distribution produced models with large dispersion parameters, whereas Gaussian models performed well and model residuals were normally distributed. Analyses of clutch size were conducted on a subsample of 183 breeding events for which clutch size, lay date and weather data were available. Analyses of variation in nesting success and productivity were conducted on a subsample of 223 and 222 breeding events, respectively, for which lay date and weather data were also available.

A stepwise backward procedure was performed for model selection (with the function drop1), and likelihood ratio chi² tests (LRT) on AIC differences were used to select the best models.

Samples sizes differed between regions and our slope estimates for the relationships between lay date and breeding parameters could be influenced by this or hinge on data from a few very early or very late nests (see Figure 3). In order to be confident that regional differences were not simply a consequence of these potential biases, we randomly selected a reduced and equal number of nests in each region and re-estimated the slope of the relationships and their 95% confidence intervals using a bootstrap analysis implemented in R 3.2.3. For the relationships between lay date and clutch size, our sample sizes included 144 and 42 nests in the coastal and interior-mountain regions, respectively, so we re-estimated the slope using 1000 random samplings of 30 nests from each region. For the relationships between lay date and productivity, our sample sizes included 163 and 64 nests in the coastal and interior-mountain regions, respectively, and we re-estimated the slope using 1000 random samplings of 50 nests from each region.

Study regions were characterized by different weather conditions (Figure 2a, b). Both regions experienced higher temperatures and little rain during summer months (December–March) than winter months (May–September). However, temperature differences between summer and winter were more pronounced in interior-mountain than in coastal regions. Additionally, coefficients of variation for both temperature and rainfall were greater from April to October in the interior-mountain than in the coastal region, indicating that weather conditions in interior-mountain regions at that time were more variable in space (among nest localities) or time (years) than those in coastal regions. Rainfall levels strongly decreased (by half) between August and September in both regions, coinciding with the peak of lay date in Black Harriers (Figure 2b, c).

Figure 2.

Monthly average temperature (a) and rainfall (b), according to region (coastal, white bars; interior-mountain, dark grey bars). Also presented are Coefficient of Variation (100× SD/Mean) for both climatic variables (dashed line for coastal, solid line for interior-mountain), as well as frequency distribution of breeding initiation (n = 402) (c) during the study period (2000–2014).

Figure 3.

Black Harrier breeding performance (a clutch size b nesting success c productivity) variation according to lay date and region (coastal nests: white circles/dashed line; interior-mountain nests: grey dark circles/ solid line). Lines represent modelled data from the GLMM results (Table 3). Raw data (circles) are also shown for illustration purposes and have been averaged over two consecutive 15-days periods. Sample sizes (number of nests) are indicated above the error bars. The point line represents the breeding success variation with laying date for both coastal and interior-mountain regions (this relationship did not differ between regions).

The PCA analysis on monthly rainfall and temperature data rendered four PCs explaining approximately 60% of the variance (Table 1). PC1 was positively related to temperature during all months, although the relationship was less marked during the winter months (June–August) when temperatures were overall lower (Figure 2a). PC2 was positively related to rainfall during all months, although the relationship was less marked during the summer and early autumn months (December–March), when rainfall levels were overall lower (Figure 2b). PC3 contrasted high temperatures in summer and early autumn (December–March) but low in late autumn and winter (May–August), with lower temperatures in summer and higher in autumn-winter. Therefore, this PC refers to temperature seasonality. Finally, PC4 identified situations with higher rainfall in summer and early autumn (December–March), but lower rainfall in late autumn and winter months (May–August), thus referring to rainfall seasonality.

All weather PCs varied significantly among years, but only PC1 and PC3 were significantly different between regions (Table 2). PC1 values were lower in the coastal region (LS means: -1.15 ± 0.70), indicating cooler temperatures (particularly in springs, summers and autumns) than in the interior-mountain (LS means: 0.93 ± 0.64). PC3 values were also lower in coastal than interior-mountain region (LS means: -1.43 ± 0.34 and 1.18 ± 0.31, respectively), indicating that temperature variation throughout the year was more pronounced in interior-mountain regions.

Lay date (n = 393 nests) was remarkably well spread through the year, spanning 8 months, from mid-May to mid-December, and followed a unimodal distribution in each region (Shapiro normality test, w = 0.98, p = <0.0001, n = 287 for coastal region; w = 0.95, p = 0.0009, n = 106 for interior-mountain region) with a peak during mid-August to end of September (Figure 2c).

Lay date was negatively associated with weather PC2 (slope = -0.26 ± 0.07) and PC4 (slope = -0.27 ± 0.10; Table 3), indicating that laying occurred relatively earlier under rainier conditions, particularly when rain was more intense in autumn, winter and spring, and when summers preceding laying were wetter. Lay date was also significantly different between regions (Table 3): nests located in coastal regions had overall earlier lay dates (LS means: 8.40 ± 0.22, 15–30 August) than those located in mountain regions (LS means: 9.36 ± 0.28, 1–15 September). Individuals in coastal regions also laid over a more extended period, with breeding events in this region occurring up to two months earlier and one month later than in the interior-mountain regions (Figure 2c).

Clutch size averaged 3.58 ± 0.64 eggs (range: 2–5; n = 183 nests). Clutch size varied with rainfall (PC2, Table 3), with larger clutches being associated with rainier conditions (slope = 0.08 ± 0.04), particularly in autumn, winter and spring. Clutch size also varied significantly with the interaction between lay date and region (Table 3): clutch size declined markedly as the season progressed, but this decline was more pronounced in the interior-mountain (slope: -0.25 ± 0.07) than in the coastal region (-0.05 ± 0.03) (Figure 3a). Bootstrapping analyses using 1000 random samplings of 30 nests from each region indicated very little overlap between the estimates of slopes for each region (95% confidence intervals of -0.17 to 0.05 for coastal nests and of -0.39 to -0.15 for interior mountain nests). When removing the interaction region x lay date from the model, clutch sizes were not significantly different between regions (LRT = 0.37, P = 0.54).

In total, 31% of nests (n = 223) monitored during the study period failed to produce fledglings. Nesting success declined significantly with lay date (Table 3; slope: - 0.40 ± 0.10), and this decline was similar between regions (non-significant region × lay date interaction; Figure 3b). Once controlling for lay date, no significant differences in nesting success were found between regions, nor any relationships between nesting success and weather variables (Table 3).

Productivity among monitored nests averaged 1.66 ± 1.30 fledglings (range 0 - 4 fledglings, n = 222 nests). Productivity was positively associated with weather PC2 (Table 3; slope = 0.12 ± 0.05), indicating that productivity increased in rainier conditions. Productivity also declined as lay date increased (Table 3; Figure 3c). As for clutch size, there was an indication that this seasonal decline in productivity differed between study regions (marginally significant region × lay date interaction; Table 3; P = 0.09), with a steeper decline in the interior-mountain region (slope = - 0.40 ± 0.12) than in the coastal region (slope = -0.20 ± 0.05; Figure 3c). Bootstrapping analyses using 1000 random samplings of 50 nests from each region showed that there was some overlap between the estimates of the calculated slopes for each region (Mean, SD, and 95% confidence intervals: -0.20 ± 0.05, -0.30 to -0.08 for coastal nests; and -0.35 ± 0.14, -0.59 to -0.12 for interior-mountain nests). When removing the interaction region x lay date from the model, there was no significant difference in productivity between regions (LRT = 0.002, P = 0.98).

Most strikingly, we found that seasonal declines varied among regions for clutch size and also (less markedly) for productivity. The seasonal decline in these parameters was progressive and moderate in coastal regions but much more abrupt in interior-mountain regions. Thus, clutch size and productivity were overall higher in interior-mountain than in coastal regions early in the season (until September), but differences were not found or values were higher in coastal regions for nests initiated from October onwards (Figure 3ac). Interestingly, we did not find a significant difference between regions for nesting success, suggesting that regional differences in declines in productivity may simply result from differences in clutch size patterns. Additionally, this suggests that differences between regions are more influential early in the breeding cycle. Ultimately, neither clutch size nor productivity were, on average, significantly different between regions, indicating that differences between regions early and late in the season balanced each other out.

Seasonal declines in breeding performance can be explained by differences in the quality of individuals breeding early or late and/or by a worsening of environmental conditions as the breeding season progresses (Verhulst et al. 1995, Verhulst and Nilsson 2008). The overall seasonal decline observed in the Black Harrier population may reflect a difference in the quality of individuals breeding earlier vs. later in the season (with e.g. older and more experienced birds breeding earlier in the season). However, the observed regional differences in the seasonal declines are unlikely to be explained by differences in individual quality alone, particularly for a mobile species like the Black Harrier (evidence from satellite tagged birds indicate that the same individual can breed in both the coastal and the interior-mountain regions in different years, Garcia-Heras et al., unpublished data). Our results thus indicate that changes in environmental conditions likely play an important role in explaining seasonal changes in breeding performance, and furthermore that this degradation in environmental condition is stronger in interior-mountain than in coastal regions.

Temperature was overall higher in coastal regions until August, when clutch sizes were smaller there, but the opposite pattern was found from October onwards, when clutch sizes were greater in coastal regions. Temperature variation could thus be an indicator of the temporal variation in quality of environmental conditions among regions. However, temperature (PC1) did not significantly influence clutch size (or any other breeding performance parameter), so differences are likely to be related to other factors, such as food availability or habitat quality. Black Harriers mostly feed on small mammals (ca. 65% of the diet), particularly on Four-Striped Mouse (Rhabdomys pumilio) and African Vlei Rats (Otomys sp.) (Jenkins et al. 2013, Garcia-Heras et al. unpublished data), so the smaller clutch size in interior-mountain areas for pairs starting to lay late in the season may reflect lower small mammal availability there at that time. Population dynamics and breeding output of the striped mouse are known to vary strongly with rainfall (see Taylor and Green 1976, Meynard et al. 2012, Rymer et al. 2013). This, together with our results (relationship between PC2 and both clutch size and productivity), suggests that greater rainfall during autumn and winter could positively influence the abundance of small mammals such as Four-Striped Mouse, which may in turn influence breeding performance in Black Harriers, as found for other species (Korpimäki 1992, Salamolard et al. 2000, Redpath et al. 2002b). Future studies should investigate the relationship between Black Harrier’s breeding and food availability, and how this varies in space and time.

Black Harriers showed a remarkably extended breeding period, with the onset of laying spread over 8 months (mid-May to mid-December). A wide spread in timing of breeding has been reported in other raptors from the Southern Hemisphere [e.g., 8 months for the Black Sparrowhawk (Accipiter melanoleucus), Martin et al. 2014], including other harrier species, such as the African Marsh Harrier Circus ranivorus (9 months, Simmons 2000, Simmons et al. 2005), the Cinereous Harrier (Circus cinereus) (7 months, del Hoyo et al. 1994) or the Spotted Harrier (Circus assimilis) (5 months, del Hoyo et al. 1994). This contrasts to what is usually observed in harrier species breeding in the Palearctic, for which the timing of breeding rarely exceeds 3 months [e.g., Pallid Harrier (Circus macrourus), Montagu’s Harrier (Circus pygargus), Hen/Northern Harrier (Circus cyaneus/hudsonius), (Schipper 1979, Simmons et al. 1986, Arroyo et al. 1998, Simmons 2000, Amar et al. 2005, Terraube et al. 2009). These large scale differences in the extent of the timing of breeding are likely related to climate, the Northern Hemisphere being in general characterized by a more pronounced seasonality in rainfall and temperature regimes (Garcia and Arroyo 2001, Redpath et al. 2002a) than the Southern Hemisphere, limiting in time the conditions that are suitable for successful breeding, and also suggest that conditions for breeding are suitable over a longer time for Black Harriers, as for other African raptors.

Nevertheless, we found a clear seasonal peak, with most laying (ca. 50% of clutches) occurring between mid-August and the end of September. This, together with the strong seasonal decline in breeding performance observed, indicates that optimal timing for breeding is limited for this species, despite the overall large extended breeding period. This peak coincides with a sharp drop in rainfall levels and an increase in temperature (Figure 2), suggesting that high rainfall levels may impair laying. However, models showed that, overall, laying occurred earlier under more rainy conditions (negative relationships with PC2 and PC4), and particularly if rainfall was greater in autumn-spring periods and when summers preceding laying were wetter. The latter may reflect the east-west rainfall conditions in South Africa, as laying is earlier in eastern locations, where rainfall levels are overall higher particularly in winter months. This may also be associated with food availability: wetter summers may contribute to a greater primary productivity and subsequent rains just before (April–May) and during the time of breeding may lead to greater food abundance during Black Harrier’s whole breeding cycle (see above).

The strong associations between timing of breeding, temperature and rainfall also indicate that climate change may further influence shifts in breeding phenology of southern African birds (Simmons et al. 2004, Cunningham et al. 2013), including raptors (Martin et al. 2014), most notably in the southern and western regions where a warming trend has been detected during the past 50 years, and rainfall is predicted to decline (Hockey et al. 2011, Cunningham et al. 2015). Our results highlight that weather conditions, and most notably rainfall regime, play an important role in determining the timing of breeding of Black Harriers, and likely shapes the regional differences encountered in lay date. However, the timing of breeding may also depend on the seasonal fluctuation of other variables responsive to environmental cues that change with weather conditions, but that were not tested here. These more likely would be either variations in food supply (Perrins 1970, Verhulst 2008) or arrival dates from the non-breeding grounds, as suggested for other raptor species (Newton 1998, Ketterson et al. 2015).

We also found differences in lay date between regions: Black Harriers breeding in coastal regions started laying on average about 15 days earlier, and clutches occurred over a more extended period than those breeding in interior-mountain (Figure 2c). These patterns suggest that optimal conditions for breeding might be achieved at different times in different geographical zones, but also indicate that suitable conditions for breeding may last longer in coastal than in interior-mountain regions.

Black Harriers have been described as Fynbos specialists (Curtis et al. 2004), due to a greater number of breeding events in this vegetation type along the coast and a higher productivity in coastal regions compared with interior-mountain regions. However, our results, based on a larger sample size over a longer study period, differ from those of that study: while the number of monitored breeding events was indeed larger along the coast, we found that overall productivity was equal in interior-mountain and coastal regions. This was mainly explained by the regional differences in seasonal decline observed in breeding parameters, with clutch size and productivity being greater in interior-mountain regions early in the season, but the subsequent decline being more abrupt. Environmental conditions in interior-mountain regions might be more suitable than in coastal regions, but only for a limited period of about 1.5 months. Before, they seem to be unsuitable to allow breeding, and afterwards, they quickly deteriorate inducing a reduced breeding performance later in the season. Weather conditions at the beginning of the harrier breeding season (until October) also appeared much more variable among sites and years in interior-mountain regions (greater coefficient of variation; Figure 2ab). This implies that, even if average conditions are better at that time, in certain years or regions, conditions may not be suitable for breeding. In coastal regions, environmental conditions remain more stable throughout the harrier breeding season, which allows productive breeding to occur over a longer period of time (4 months). Thus, the more stable weather in coastal regions within and among years may mean that it is overall a safer choice for Black Harriers to breed there than in interior-mountain regions. This may explain why breeding Black Harriers were more commonly found at the coast (i.e. 3 times more breeding events along the coast than in interior-mountain regions; Curtis et al. 2004), although we recognise that these figures do not control for search effort.

Recent changes in climate conditions within Africa during the last decades (Hockey et al. 2011, Kruger and Sekele 2012, Cunningham et al. 2015) may exacerbate the differences among regions and present a challenge for species like the Black Harrier. Indeed, a shift in rainfall and temperature patterns has occurred in South Africa and most notably in the south-west of the country, where most harriers breed: temperatures are getting warmer with less rain falling inland (the same pattern is expected in the western part of the Northern Cape Province, where many “interior-mountain” Black Harrier nests occur), while the opposite trend is expected along the coast (Cunningham et al. 2015). In addition, anthropogenic modifications in land use during the last century in South Africa such as the conversion of the Fynbos vegetation into agriculture or urbanization, might also negatively affect the Black Harrier population (Curtis et al. 2004). Only two records have mentioned Black Harriers breeding in cultivated areas (Steyn 1982, Chadwick 1997), which suggests that the species might not be capable of adapting to breed in non-natural habitats (Curtis et al. 2004, Jenkins et al. 2013), contrary to Palearctic harriers (Arroyo et al. 2002, Millon et al. 2002) and other south African raptors (Murgatroyd et al. 2016). Further land use change may “force” even more Black Harriers to breed along the coast, in Fynbos vegetation, where environmental conditions remain more stable within and among years in comparison to other available sites (e.g., Karoo biome in interior-mountain regions) that may become drier and colder. This highlights the importance that the coastal Fynbos may have for the stability and sustainability of the Black Harrier population in the future. Conservation measures have already prioritized the protection of Fynbos vegetation, through the creation of national parks and private reserves, and should continue in order to conserve the species in the long term.