Defining T_thresh values

In this study we use maximum daily air temperature (T_max), measured by weather stations, as our standard for defining heat waves, for the following reasons

1. T_max can be used as a proxy for biologically meaningful temperatures, as it will correlate with the range of operative environmental temperatures (T_e) available to an animal on any given day (for examples see 10,14,15).
2. T_max is collected by weather stations globally and used in climate change models.

We define “T_thresh” as a critical T_max threshold above which a species incurs measurable fitness costs. In this study we use T_thresh values for two southern Kalahari species drawn from the literature; southern pied babblers [10] and common fiscals (also known as fiscal shrikes[15]). Southern pied babblers (hereafter babblers) are arid-zone specialists with a Southern African range centered on the Kalahari Basin, whereas common fiscals (hereafter fiscals) are a widespread species which, though common in the periphery of the Kalahari Basin, tends to be absent from its centre [20] (Figure 1). T_thresh values and their biological relevance to the species in question are presented in Table 1. The studies from which our T_thresh values were drawn were each carried out at sites within the South African portion of the southern Kalahari (at Kuruman River Reserve: 26°58’S, 21°49’E; and Tswalu Kalahari Reserve: 27°13'S, 22°22'E).

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Figure 1. Range maps of focal species in relation to the Kalahari.

Southern African distribution of A: southern pied babblers and B: common fiscals, in relation to the Kalahari basin. Range maps drawn from South African Bird Atlas Project data [29].

https://doi.org/10.1371/journal.pone.0082492.g001

Temperature data

We selected a common analysis period of 1961–2010 of T_max data from the SAWS (South African Weather Service) climate database, in order to obtain the longest possible period with a reasonable number of available climate stations to cover the interior of the Northern Cape and western part of North-West province in South Africa (comprising the South African extent of the Kalahari bioregion and surrounding areas (the Bushmanland and Upper Karoo bioregions; Figure 2).

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Figure 2. Weather station locations.

Locations of weather stations used in this study, in relation to Kalahari and Upper Karoo/Bushmanland biotypes [36].

https://doi.org/10.1371/journal.pone.0082492.g002

The data sets of the selected stations were subjected to quality control to remove any values which were possibly erroneous, however this process excluded the colder months when it was highly unlikely that T_max greater than either shrike or babbler T_thresh would occur. The validity of anomalously high values were checked against the reports of the prevailing weather conditions around the time of observation, and also compared to the values measured at available stations close-by. Consequently, any suspicious data were removed from the time series.

After quality control of the data, the completeness of the time series was verified to comprise more than 80% of the years under consideration. For a particular year to be included, individual months had to have more than 90% of days of data. However, this provision did not apply to the colder winter, late autumn and early spring months.

A total of 14 weather stations were finally accepted for analysis. The SAWS climate number, station name, location in terms of latitude and longitude and altitude are presented for each station in Table 2. Weather station locations are plotted in Figure 2.

Heat wave trend analyses

The T_thresh values determined for babblers (T_thresh = 35.5°C) and fiscals (T_thresh = 33°C) were used as the threshold T_max above which heat wave conditions were considered to be reached. As we were interested in trends in the hottest days and periods of consecutive days during each year, we centred our investigations on the austral summer. Thus for the purposes of this work, we defined a ‘year’ as beginning on the 1^st July of one calendar year and ending on the 30^th June the following calendar year.

Trends in five ‘heat wave indices’ were analysed for each T_thresh:

1. Frequency of days per year on which T_max ≥ T_thresh (units: days);
2. Total number of heat waves per year (heat wave frequency; a heat wave was defined as one or more consecutive days on which T_max ≥ T_thresh);
3. Maximum duration of heat waves, defined as the maximum number of consecutive days per year on which T_max ≥ Tthresh; units: days. (We did not analyse trends in average duration because heat wave duration has a systematically non-normal distribution, with short heat waves in early summer followed by longer heat waves in the peak of the season. The additional short heat waves early on in a longer hot season will cancel out the effects of longer heat waves mid-season, making the average duration an uninformative measure);
4. Average duration of intervals between heat waves, defined as the mean number of consecutive days on which T_max < T_thresh and calculated taking into consideration only the periods between the first and last heat waves of the year (units: days) ; and
5. Intensity of heat waves, calculated as the sum of the daily differences, in degrees Celsius, between T_max and T_thresh for all the days in the heat wave:

where n = total number of days in the heat wave and by definition, all T_max ≥ T_thresh. Average intensity of heat waves in a year was calculated by taking the mean intensity of all heat waves recorded for that year; maximum heat wave intensity was the highest intensity value for a heat wave calculated for that year (units: degree days).

Trend analyses of the indices were based on linear regression, with significance tested with Student's t-test at the 5% level. Once obtained, trend estimates (slope of the regression line) were multiplied by 10 to indicate the trend per decade, which is common practice in climatological trend analysis.

Decadal trend estimates were averaged across all weather stations for each heat wave index, then plotted along with their 95% confidence intervals (CIs) in order to assess which indices showed the strongest trends (steepest rates of increase) across the region. Trends for each weather station were plotted on a map of South Africa using QGIS 1.8 allowing visualisation of spatial patterns.

Overall trends

We found positive mean trends for all heat wave indices (both T_thresh values), except for the duration of intervals between heat waves (Figure 3). There was very little difference in the magnitude of the trends for heat waves defined for fiscals versus babblers. Strongest trends for both species were in the number of days per year on which T_max > T_thresh, and in maximum heat wave intensity. Trends for increases in frequency and average intensity of heat waves were also statistically significant (95% CIs did not contain zero), but had smaller estimates (i.e. slower rates of increase). The faster rate of increase in maximum than in average heat wave intensity suggests that while heat waves were becoming more intense on average between 1961 and 2010, variance in intensity was also increasing. Maximum heat wave duration showed a significant (babblers) or near significant (fiscals) positive trend for both species, although estimate sizes were small (<0.5 days/decade increase in duration; Figure 3).

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Figure 3. Decadal change in heat wave indices, averaged across weather stations.

Effect sizes (averaged slope estimates from trend analyses) and 95% confidence intervals for decadal change in six heat wave indices, averaged across 14 weather stations in the southern Kalahari. Units for the effect sizes vary between the heat wave indices and are indicated on the x-axis to aid comparison of like with like estimates. Heat waves were defined using T_thresh values relevant to common fiscals (black circles) and southern pied babblers (white circles).

https://doi.org/10.1371/journal.pone.0082492.g003

The duration of intervals between heat waves (the number of consecutive days on which T_max < T_thresh) showed a negative mean trend across all the weather stations, for both species, with 21.4% of stations recording a significant reduction in interval duration for each species (Tables 3 and 4). However, the average effect size was small and confidence intervals contained zero, meaning there was uncertainty as to the overall direction of the trend (Figure 3).

Geographical patterns

Strongest warming trends in the number of days T_max > T_thresh, the frequency of heat waves and the average and maximum heat wave intensity, were all found in northwest of the study area: i.e. the western Karoo and the southern Kalahari proper (examples given in Figure 4). Maximum duration of heat waves showed small, significant increases also in the western Karoo (fiscals) and southern Kalahari (babblers); in the order of 0.1 to 1.5 days/decade. Weather stations in the Northwest Province did not record warming trends, and in one case even recorded a significant cooling trend. Tables 3 (fiscals) and 4 (babblers) provide trend estimates for each weather station.

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Figure 4. Decadal trends in the number of days T_max > T_thresh, and in maximum heat wave intensity.

Decadal trends in: the number of days exceeding T_thresh for A: fiscals and B: babblers; and the maximum intensity of heat waves for C: fiscals and D: babblers (degree days). The strongest trends for both indices occurred in the northwest of the study area. Circles denote weather station locations, labelled with the appropriate trend estimate. Circle size is proportional to the strength of the trend (effect size), black circles indicate positive trends, and white circles indicate negative trends. Fiscal distribution is shown in light and dark grey on maps A and C; babbler distribution is shown in light and dark grey on maps B and D [29]. Light grey regions: SABAP1 reporting rates < 70%; dark grey regions: SABAP1 reporting rates > 70% (see text for explanation).

https://doi.org/10.1371/journal.pone.0082492.g004

Implications of calculating heat wave trends using T_thresh values

From a purely climatological perspective this study is largely consistent with the results from previous studies (e.g. 17), showing that the northwestern region of South Africa underwent probably the strongest warming in the country during the last five decades. In general, ‘hot’ weather stations that recorded high numbers of days annually on which T_max > T_thresh between 1961 and 2010, showed stronger decadal warming trends than ‘cool’ weather stations. For example, Figure 5 presents the trend estimates for the average number of days / decade with T_max > 35.5°C (babbler T_thresh), plotted against the annual average number of days on which T_max > 35.5°C. The strong positive relationship apparent in this graph is likely due to the sine-wave shape of yearly temperature traces, meaning hot regions will, as a matter of course, appear to be warming faster than cool regions when trend analyses are based on absolute thresholds (e.g. T_thresh values). From a climatological viewpoint, therefore, such analyses produce less-meaningful results for climate change assessments, than those based on the historical distribution of temperatures, e.g. number of days above the 90^th percentile of a long-term historical base period; as such an index takes into account the inherent climatological properties of the measuring location [21]. From a biological viewpoint, however, each day above a biologically meaningful T_thresh is important in terms of its impact on fitness; regardless of the historical pattern of such days in the area. Birds in hot areas may therefore experience stronger effects of warming trends generally, as the physiological responses of endotherms to temperature are not linear, but invariably contain thresholds above which costs of thermoregulation rapidly increase [6,22,23].

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Figure 5. Rates of warming are highest in already-warm areas.

Relationship between linear trend of days with T_max > 35.5°C and average number of days with T_max > 35.5°C, showing the tendency for areas that already experience large numbers of hot days (defined as T_max > T_thresh) each summer to show more rapid gains in additional hot days under a regime of climate warming than cooler areas. Each data point represents a single weather station used in this study.

https://doi.org/10.1371/journal.pone.0082492.g005

Biological implications of trends

Although all heat wave indices we investigated (with the exception of intervals between heat waves) showed warming trends, by far the most rapid increases we identified were in the numbers of days T_max > T_thresh (presumably the trend underlying the other increases we observed) and in the maximum intensity of heat waves. Variance in heat wave intensity also increased during the study period (as rates of change in maximum intensity outstripped average intensity), particularly in already-hot areas such as Twee Rivieren and Vanwyksvlei. These trends entail greater uncertainty in heat wave intensity and potential for intensity extremes. This may have significant biological implications, should these trends continue.

High intensity heat waves can be lethal for endotherms (like birds) if temperatures overwhelm their capacity for physiological thermoregulation. This can happen because the metabolic costs of thermoregulation increase rapidly above upper critical temperature limits [23]. Furthermore, the costs of dissipating heat via evaporative water loss increase exponentially when ambient temperature exceeds body temperature for small birds [24]. McKechnie and Wolf [6] estimate that a small bird (~25 g) can survive for only 5.5 hours without access to water at 45°C air temperature. Even with access to replacement water, high intensity heat waves may still cause mortality if evaporative water loss is insufficient to counteract heat gain from the environment (for possible examples, see 5,25). When temperatures remain below lethal intensity, increases in the frequency and heat intensity of days on which T_max > T_thresh can still have severe consequences for individuals and populations. For example, in Scleroporus lizards in Mexico, high temperatures force the animals to cease activity and take refuge in rock crevices. Increasingly high T_max days correspond to an increased length of time per day when temperatures are above T_thresh for the lizards, reducing the time available for foraging and breeding activity and eventually resulting in population declines and extinctions [14].

The two studies from which we drew our estimates of babbler and fiscal T_thresh values [10,15]; provide some indication of how these species are likely to respond to high intensity heat waves. The costs of T_max exceeding babbler T_thresh (35.5°C) are manifest in terms of the inability of the birds to recoup body mass lost overnight. Du Plessis et al. [10] found that the differences between body mass gained during the day and body mass typically lost overnight increased in a linear fashion as T_max increased above T_thresh, at least for T_max < 41°C (no data was collected at T_max > 41°C). Babblers began losing body mass during the course of the day when T_max > ~39.5°C. The authors propose that increased duration of heat waves may affect body mass in babblers in a cumulative manner, with implications for survival and breeding success. Increased intensity of heat waves seems likely to have a similar effect. However, the long-term effects of increased duration or increased intensity of heat waves, or, alternatively, extremely intense heat waves, are currently unknown for this species. Furthermore, the period of time babblers require to ‘recover’ from a hot weather event is also unknown.

Fiscal T_thresh (33°C) was estimated on the basis of the negative impacts of T_max > T_thresh during the nestling period on fledgling mass in this species [15]. Reduced body mass at fledging often correlates with reduced survival probability and future breeding success in birds (reviewed by [26]). For fiscals, the impacts of a single day with T_max > T_thresh during the nestling period on fledging body mass depended on the degree to which T_thresh was exceeded. For example, each day over 33°C reduced fledging body mass by an average of 0.7 ± 0.4 g, whereas each day over 36°C reduced body mass by 1.3 ± 0.3 g, and each day over 38°C reduced body mass by 5.3 ± 1.8 g (data are means ± 1se, [15]). The effect of high T_max days therefore seems to increase in an accelerating, rather than linear, manner as the intensity of heat increases. In addition, both the number of days during the nestling period on which T_max > T_thresh, and the timing of those days with respect to the stage of growth of the nestlings, appear to be important in determining the outcomes for fledging mass. However, no data exists for T_max > 38°C, and, as with the babblers, the long term fitness implications of the observed effects have not been measured.

Geographical patterns

The strongest and most significant increases in all heat wave indices, for both babblers and fiscals, occurred in the northwest of the study area. The weather stations at Twee Rivieren, Upington, Pofadder and Vanwyksvlei in particular consistently recorded strong trends, especially in the number of hot days and maximum heat wave intensity. By contrast, the weather stations at Mafikeng and Vryburg in the northeast of the study area recorded very small, in some cases negative, trends in all heat wave indices. This conforms to the findings of other climate change analyses for the region [17].

The rapidly-warming region we identified around Twee Rivieren, Upington, Pofadder and Vanwyksvlei is outside of the South African range of the southern pied babbler. Babblers are ‘thornbush specialists’ reliant on tall woodland stands for nesting sites [27,28] and their absence from the southwestern corner of the Kalahari basin, including the area mentioned above, is likely due to the lack of sufficient habitat there. The region around Mafikeng and Vryburg, however, does fall within the babbler’s range. If temperatures remain stable in this area into the future (as they did between 1961 and 2010), while temperatures in the core of the babbler’s range in Botswana increase (as predicted by [18]) this area could potentially become a climate refuge for the species, and may deserve protection as such.

Common fiscals, unlike babblers, are found throughout our study area. They are a habitat generalist, common across South Africa but largely absent from the central Kalahari basin in Botswana [20]. Reporting rates of fiscals (% of repeated surveys that recorded fiscals in the area: a proxy for population density) in the first Southern African Bird Atlas (SABAP1, [29])

are lowest in the arid periphery of the Kalahari and along the Namibian coast. Intriguingly, reporting rates are also relatively low in the region from Twee Rivieren to Vanwyksvlei compared to elsewhere in South Africa (see Figure 4). It was in this region that we recorded the most rapid warming trends in heat wave indices as defined by fiscal T_thresh. This raises the tantalizing possibility that fiscal populations in this area may have already been affected by increased temperatures; and that, if trends continue and the birds are unable to adapt, the population in this area may become increasingly vulnerable. However, fiscals are widespread and loss from parts of the southern Kalahari and Bushmanland is unlikely to cause the species to become at risk of extinction within South Africa as a whole. More concerning, perhaps, is the potential, unknowable, impact on Kalahari ecosystems of losses of ‘peripheral’ species with low T_thresh values, like fiscals.

Analysing trends in other climate parameters

We have focused on analysing warming trends relevant to the fitness of the two study species. However, temperature is not the only climate variable showing changing trends under climate change. One variable that potentially has enormous biological significance is rainfall. The two papers from which we drew our T_threshs did not contain any information regarding potential rainfall thresholds (R_threshs) affecting aspects of the life-history of the focal species. Furthermore, it is uncertain whether species are likely to respond physiologically and behaviourally to rainfall patterns in the same manner as they respond to temperature. However, if it can be shown for any species that such a rainfall threshold exists (or for that matter, thresholds in any other climate variable, or combination thereof, for which long term data sets are available), then our threshold technique could be applied to these variables as well.

Implications for conservation

The T_thresh approach facilitates examination of warming trends relevant to any fitness correlate for which data exists from the species of interest. In our examples, exceeding T_threshs affected the ability of adult babblers to maintain body mass, and caused fiscals to fledge from the nest at a smaller body mass [10,15]. The result for fiscals is important, because they breed in response to rainfall, as do many arid-zone birds [30]. They are therefore not likely to be able to shift breeding dates earlier to avoid increasingly hot summer temperatures, as some temperate species have been documented to do, e.g. [31,32].

In some instances, data might be available for more than one temperature threshold (e.g. a number of known T_threshs exist, relevant to different aspects of the life history of a species). Managers can choose which T_thresh to use based on the magnitude of associated fitness costs, or based on which T_thresh is lowest. Under some circumstances, population modelling may also be of use in deciding among multiple T_threshs: the T_thresh related to the life-history stage identified as having most impact on population trajectories can then be chosen. Even in cases where data is sparse and only a single T_thresh is known (as in the babbler example), analysis of warming trends with respect to this T_thresh will still provide more biologically meaningful information than standard climate analyses. This is because we can be confident that any heat wave defined using a T_thresh above which known fitness costs are incurred will affect the fitness of the focal species.

In our study, applying the T_thresh technique to analyses of past climatic trends allowed us to ascertain that the frequency, duration and intensity of hot weather events, as relevant to our two focal species, all increased between 1961 and 2010 in our study area. Furthermore, we were able to show that maximum heat wave intensity had increased faster than other indices, including average intensity. This suggested that increased variance in intensity, with the corollary of increased likelihood of intensity extremes, was the aspect of warming most likely to have already affected our study species. Potentially in support of this, SABAP1 reporting rates for fiscals in the most rapidly-warming region of our study area were lower than in other areas of their range which experienced weaker warming trends [20]. Should the trends we identified in this study continue into the future, then the T_thresh technique predicts we should focus our research efforts on the form of each species’ response to high intensity heat waves, and the longer term implications for these for the life-history of individuals.

As illustrated by southern pied babblers and common fiscals, the T_thresh method allows identification of the heat wave trends with the most immediate likelihood to impact focal species of interest, and geographical patterns in these trends. We chose to investigate trends within a single region, but this approach could be applied across entire species ranges, provided data is available, allowing both areas of greatest risk and potential climate refugia to be identified. For species that are already flagged as at risk due to climate change induced warming (e.g. the Ethiopian bush-crow Zavattariornis stresemanni, [33]; or European birds with low temperature tolerances, [34]), the application of this approach could guide both the collection of data on species’ responses to appropriate aspects of temperature rise, and also aid significantly in conservation planning.