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Hottentot Buttonquail Turnix hottentottus: Endangered or just overlooked?

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Abstract

There is remarkably little documented information in the scientific literature on any of the 18 species of buttonquail as they are very difficult to observe in the wild. This lack of information has hampered informed conservation decision making. We undertook the first biome-wide survey for the fynbos endemic Hottentot Buttonquail Turnix hottentottus , using flush transect surveys covering 275 km. We used location data for sightings as well as from records reported by the bird-watching community and modelled distribution using MaxEnt. Encounters were restricted to the fynbos biome, and the top contributors to our prediction of suitable habitat were habitat transformation, slope and time since fire. We obtained a density estimate of 0.032 individuals per hectare which, across an estimated median range of 27,855 km ² , provides a population estimate of 89,136 individuals. Given the extent of the range and the population estimate we suggest the IUCN Red List status could be ‘Vulnerable’, rather than ‘Endangered’. Agricultural and alien-vegetation encroachment means that the future of the species is certainly under threat and further studies are needed to inform conservation management.
Bird Conservation International, page 1 of 8. © BirdLife International, 2018
doi:10.1017/S0959270918000059
Short Communication
Hottentot Buttonquail Turnix hottentottus:
Endangered or just overlooked?
ALAN T. K. LEE, BRIAN REEVES and DALE. R. WRIGHT
Summary
There is remarkably little documented information in the scientific literature on any of
the 18 species of buttonquail as they are very difficult to observe in the wild. This lack of
information has hampered informed conservation decision making. We undertook the first
biome-wide survey for the fynbos endemic Hottentot Buttonquail Turnix hottentottus, using
flush transect surveys covering 275 km. We used location data for sightings as well as from
records reported by the bird-watching community and modelled distribution using MaxEnt.
Encounters were restricted to the fynbos biome, and the top contributors to our prediction of
suitable habitat were habitat transformation, slope and time since fire. We obtained a density
estimate of 0.032 individuals per hectare which, across an estimated median range of 27,855 km2,
provides a population estimate of 89,136 individuals. Given the extent of the range and the
population estimate we suggest the IUCN Red List status could be ‘Vulnerable’, rather than
‘Endangered’. Agricultural and alien-vegetation encroachment means that the future of the
species is certainly under threat and further studies are needed to inform conservation
management.
Introduction
The Hottentot Buttonquail Turnix hottentottus is one of 18 species of Turnicidae; a group of cryp-
tic, small, terrestrial birds (Debus and Bonan 2016). Hottentot Buttonquail is considered to be
endemic to the fynbos biome of South Africa (Taylor et al. 2015), which is a fire driven
Mediterranean-type ecosystem (Cowling et al. 1997). It is the only Turnix species reported from
the fynbos.
Remarkably little is known about the Hottentot Buttonquail. It is, like other buttonquails,
assumed to be polyandrous (Dean 2005). There is distinct sexual dimorphism, with males gener-
ally drab, but females with contrasting white belly and chestnut, reddish brown chest and face
(Arizaga et al. 2011). Little is known about its breeding ecology, with published notes referring to
Hottentot Buttonquail actually referring to Black-rumped Buttonquail T. nanus (e.g. Masterson
1973).
Taxonomically the species was considered conspecific with the Black-rumped Buttonquail
(Dowsett and Dowsett-Lemaire 1993), while Sibley and Monroe (1990) suggested the two taxa
were separate species. The latter taxonomic treatment is supported by their allopatric ranges
as well as differences in habitat preference and plumage (Dean 2005) and currently accepted by
BirdLife International (2016) as of 2014, BirdLife South Africa (Lotz 2014) and the International
Ornithologists’ Union (Gill and Donsker 2014).
A. T. K. Lee et al. 2
From the conservation perspective, the species has variously been described as: ‘on the
brink of extinction’ (Brooke 1984); ‘possibly extinct’ (Debus 1996); ‘possibly critically endan-
gered’ from c.20102013 (Lotz 2013). At the same time it was classified as ‘Least Concern’
globally while lumped with T. nanus (BirdLife International 2004); and as of 2014 ‘Endangered’
both globally (BirdLife International 2016) and nationally (Taylor et al. 2015). The most
recent listings were partly based on an extrapolation by Lee (2013) of a density estimate
obtained from point counts provided by Fraser (1990) to a possible global population of 400
individuals. By contrast, a survey in 1994 by Ryan and Hockey (1995) on the Cape Peninsula
suggested that area alone may hold 350 (100560) birds, making it one of the most common
bird species in restionaceous fynbos. However, the sparsity of records from the ongoing
South African Bird Atlas Project (SABAP2) was highlighted by Lee (2013). Thus, informed
decisions requiring population size, population trend and range, on the conservation status
and for species management purposes, have been hampered by a general lack of knowledge of
this species.
We conducted the first biome-wide survey of the Hottentot Buttonquail in order to estimate
population size and range. We used occurrence records to conduct modelling using the maximum
entropy or MaxEnt method (Phillips et al. 2006) to identify the species’ potential range. We used
this model to identify climate and habitat variables that limit distribution. We then used encoun-
ter rates from our surveys to estimate density and modelled range to extrapolate population
estimates.
Methods
Study area
The fynbos biome (fynbos; roughly synonymous with the Cape Floral Kingdom or Cape
Floristic Region) comprises one of only six floral kingdoms in the world and is contained
entirely within the political boundaries of South Africa (Cowling 1995). It is mostly restricted
to the Western and Eastern Cape provinces in the Cape Fold Mountains (Figure 1). Owing to its
exceptional plant species richness and high level of endemism, as well as high levels of animal
diversity and endemism, it is recognised as one of the world’s 25 biodiversity ‘hotspots’
(Myers et al. 2000). Vegetation is dominated by three characteristic families: Proteaceae,
Ericaceae and Restionaceae. The region experiences significant winter rainfall, although sum-
mer rainfall can predominate in the eastern regions (Cowling et al. 1997). The fynbos biome
is a fire-driven ecosystem, with most plant species adapted to an intermittent fire return
interval of 640 years (Cowling 1992). Conversion to agriculture, urbanisation and the invasion
of a variety of alien plant types pose major conservation threats to environmental integrity of the
area (Rebelo and Siegfried 1990).
Surveys to determine presence and abundance of Hottentot Buttonquail
In order to determine the presence and abundance of Hottentot Buttonquail we conducted 131
‘flush’ surveys across the fynbos biome from October 2015 to February 2016, with 275 km of
survey lines covering a combined sample area of 802 ha. The survey period was planned to coin-
cide with the breeding season, as birds have been reported to call during this period (Tarboton
2011, Lee 2013). The flush survey was a multiple-observer survey with observers walking in
a line spaced ideally no more than 5 m apart. The length of the survey line was noted, and
area calculated as the number of observers x 5 x length. Median transect length was 1.8 km
(0.212.3 km). Due to a limited budget, we recruited participants on an opportunistic basis,
and so teams ranged in size from two to 12. At least one of the authors was involved in all
surveys to confirm species identification. We calculated density (individuals/ha) as the subset
of all individuals observed within the transect line.
Hottentot Buttonquail status 3
Range estimation through species distribution modelling using MaxEnt
We created a presence-only dataset based on our encounters as well as additional sighting infor-
mation. A national public call for sightings through various media outlets yielded no responses.
In addition to survey locations, we obtained 23 locations from previous fieldwork encounters and
personal contacts, for a total of 61 presence locations. In order to address spatial autocorrelation,
we randomly subsampled the data so that the distance between points was at least 1,000 m. This
left 40 locations for model fitting and testing.
We examined the Worldclim database (Hijmans et al. 2005) of 19 climatic variables, and
excluded the derived variables with correlation coefficients of r > 0.5 for the area encompassing
our modelling domain of the fynbos biome (Mucina and Rutherford 2006; approximately S30.4°
to S34.7° and E17.8° to E26.5°; Figure 1), thus retaining the following variables: Bio 1 (Annual
Mean Temperature), Bio 2 (Mean Diurnal Range), Bio 3 (Isothermality), Bio 4 (Temperature
Seasonality), Bio 8 (Mean Temperature of Wettest Quarter), Bio 15 (Precipitation Seasonality)
and Bio 18 (Precipitation of Warmest Quarter). In addition we derived slope from a digital eleva-
tion model that was created using 20 m contours and spot heights. We used the MODIS Collection
6 burned area product (MCD64A1; Giglio, et al. 2015) to determine vegetation age (years since
last fire), with years since fire > 15 grouped into a single category. Habitat transformation was
obtained from a reclassification of the 2014 National Landcover Layer (GTI South Africa 2015;
https://egis.environment.gov.za/national_land_cover_data_sa) in which we defined landcover
categories 19 as intact habitat and categories 1072 as transformed habitat (Table S1 in the
online supplementary material). All variables were prepared for input into MaxEnt by converting
them to ASCII rasters with a 1,000 m resolution using the “Extract by Mask” tool in the Spatial
Analyst extension for ESRI ArcMap 10.0.
We ran the models in MaxEnt version 3.3.3k using logistic output format. Twenty percent
of the observations were set aside by means of random subsampling for model testing. The
default “auto features” function was selected, which enabled the algorithm to select between
linear, quadratic, product, threshold, and hinge features in fitting the models. The jackknife
Figure 1. Map of the study area in South Africa indicating biome types (Mucina and Rutherford
2006). Locations of Hottentot Buttonquail T. hottentottus encounters are indicated.
A. T. K. Lee et al. 4
routine was used to measure variable importance and response curves were generated to
show how each environmental variable influenced the prediction, with 100 replicates and the
number of iterations for model convergence set to 500. The minimum, median and maximum
distributions of the 100 replicate models were used for range calculations. We selected 5% as
the value for parameter E (sensu Peterson et al. 2008), which is a threshold based on the
amount omission error permissible. This parameter is determined based on the error charac-
teristics of the occurrence data. We selected a relatively low value because our data consisted
of GPS located field observations and we were confident of species identifications. We used
the threshold calculator tool in the NicheA software package (Qiao et al. 2016) to determine
the logistic threshold value at E = 5 to make binary predictions on habitat suitability. We also
used this software package to construct partial Receiver Operating Characteristic curves
(P-ROC) and to analyse mean AUC values for training and test data. In order to assess the
one-tailed significance difference in the AUC from null expectations, we fitted a standard
normal variate (z-statistic) and calculated the probability that the mean AUC ratio (model to
null expectation) is 1.
Estimates of population size were calculated as the product of the densities obtained from the
surveys and the remaining habitat. We also determined extent of occurrence (EOO) for the spe-
cies by creating a minimum convex polygon (MCP) around the buttonquail occurrence data using
the “genmcp” command in Geospatial Modelling Environment (Beyer, 2012). Oceanic areas were
clipped from this polygon.
Results
Abundance from flush surveys
During surveys across the fynbos we obtained 37 encounters with Hottentot Buttonquail,
consisting of 31 individuals and six cases of two birds. The resulting density estimate was
0.032 ± 0.13 individuals per ha. Most birds were flushed from close proximity to observers
(3.7 ± 4.4 m; n =31), and although the birds are sexually dimorphic, we were mostly unable
to determine the sex of birds in flight.
Range estimation through species distribution modelling using MaxEnt
The range predicted by the models was 10,37741,303 km2 (median 27,855 km2). The MaxEnt
models produced a good fit to the training data as evidenced by the Receiver Operating
Characteristic (ROC) with a mean fixed P-ROC AUC of 0.99 ± 0.004 (SD). The recorded mean
AUC ratio was 1.49 ± 0.05 (range 1.381.71), and the probability of the mean AUC ratio being
1 was very small (P = 3.2 × 10-14). This was validated by test data, which had a mean AUC
ratio of 1.47 ± 0.19 (range 1.151.95) and a probability of being better than a random model of
P = 0.005. The logistic output maps are provided as Figures S1a–c in the supplementary infor-
mation. Using the density estimate from flush surveys and our estimates of remaining suitable
habitat, we obtained a median estimate of the population size of 89,136 individuals (range
33,206132,169). The MCP representing EOO generated from buttonquail localities had a ter-
restrial extent of 79,886 km2 (see Figure 2).
The variables that contributed the most to the distribution model were habitat transforma-
tion (27.4% permutation importance), followed by slope (16.1%), vegetation age (14.8%) and
Bio 2 (Mean Diurnal Temperature Range; 17.1%). In the jackknife analysis Bio 2 and vegeta-
tion age produced both the highest training and test gain when used in isolation (Figure S2).
The model indicated buttonquail were associated with untransformed vegetation, 25 years
after it was burnt, and where mean daily temperature was cooler. Suitability also appears to decrease
with slope, being highest for relatively flat areas. Response curves for each of the environmental
variables are presented in Figure S3.
Hottentot Buttonquail status 5
Discussion
Our study, which is the first biome-wide survey for the Hottentot Buttonquail, derived a popu-
lation estimate of 89,136 individuals and a median range of 27,855 km2. It was noted that
Hottentot Buttonquails flushed only within very close proximity to observers, which may
mean individuals were missed during the survey and the population estimate may be low.
Despite these limitations, we are confident that the population is > 2,500 mature individuals
and that the range is > 5,000 km2, being current IUCN conservation classification thresholds
for ‘Endangered’ status (IUCN 2012).
Conventional survey techniques are unsuited to finding Hottentot Buttonquail. For example,
no buttonquails were recorded during a comprehensive point count survey of the fynbos biome
conducted in 2012 (Lee et al. 2015 supplementary information). Likewise, little reliable inference
on populations can be made from citizen science atlas data (Lee et al. 2017): our survey revealed
their presence at locations with very high atlas coverage. In order to adequately survey for the
presence of Hottentot Buttonquail, and likely other open landscape Turnix species, flush survey
lines of a minimum of five people are required to walk substantial distances to cover large enough
areas (Lee et al. 2018).
Variables associated with the presence of Hottentot Buttonquail
Modelling suggested Hottentot Buttonquail presence was associated with time-since-fire
veld of 2-5 years; and negatively associated with steep slopes, which is in agreement
with Lee et al. (2018), as well as areas experiencing large mean diurnal or annual temperature
fluctuations. The presence of the species was negatively associated with transformed
landscapes.
Our knowledge of vital aspects of the life history and biology of the Hottentot Buttonquail,
as well as many other Turnix species, remains to be clarified. It is possible for instance that
dietary habits may be specialised, further restricting range and site occupancy. Movement and
Figure 2. Species distribution modelling map for Hottentot Buttonquail indicating suitable habitat
from MaxEnt modelling using 5% training presence as a threshold (grey).
A. T. K. Lee et al. 6
migrations of this species, while suspected (Blackshaw and Blackshaw 1998), remain to be
confirmed and explained. Our model outputs should be seen as an initial understanding of the
likely distribution of suitable habitat for this species.
Implications for the conservation, and conservation status of Hottentot Buttonquail
The results of this survey and species population and distribution modelling lead us to suggest an
IUCN Red List categorisation of ‘Vulnerable’ for the Hottentot Buttonquail, a downlisting from
the current IUCN category of ‘Endangered’ (Taylor et al. 2015). The estimated extent of occur-
rence and population size both exceed the criteria thresholds for ‘Endangered’ (IUCN Standards
and Petitions Subcommittee 2017). Our classification is primarily due to the lower estimates of
range being less than 20 000 km2, which is a used for ‘Vulnerable’ categorisation – criterion B1.
In addition, the remaining subpopulations of the species are both severely fragmented and likely
to be experiencing ongoing decline in extent and quality of habitat – criteria B1a and B1biii of the
Vulnerable categorisation. Loss of habitat in the species’ population strongholds in the fynbos
biome is occurring due to habitat transformation for agriculture and encroachment of alien inva-
sive vegetation.
Supplementary Material
To view supplementary material for this article, please visit https://doi.org/10.1017/
S0959270918000059
Acknowledgements
This study would not have been possible without the support of CapeNature and the Eastern
Cape Parks and Tourism Agency. Thanks to all 80 survey participants, especially Wim de
Klerk, Andrew de Blocq, Brian Haslett, Selengemurun Dembereldagva, Krista Oswald, Tom
Barry, Johan Huisamen, and Marius Brand. Special thanks to Anton Odendaal and Brian van
der Walt for fundraising initiatives. We would like to thank Graeme Buchanan, A. Townsend
Peterson and two anonymous reviewers for input into early versions of this manuscript. This
project was made possible by the generous contributions from a range of donors and organi-
sations which included Club 300 Bird Protection of Sweden, Biosphere Expeditions, Avian
Leisure Group, local bird club organisations in South Africa - BirdLife Overberg, Tygerberg
bird club, Lakes bird club and additional funds from BirdLife South Africa, Bruce Ward-Smith
and Geoff Crane.
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A. T. K. Lee et al. 8
ALAN T. K. LEE*
FitzPatrick Institute of African Ornithology, DST-NRF Centre of Excellence, University of Cape
Town, Private Bag X3, Rondebosch 7701, South Africa.
BRIAN REEVES
Eastern Cape Parks and Tourism Agency, PO Box 11235, Southernwood 5213, East London,
South Africa.
DALE. R. WRIGHT
BirdLife South Africa, Room A08, Centre for Biodiversity Conservation, Kirstenbosch Botanical
Gardens, Rhodes Ave, Newlands, Cape Town 7700, South Africa.
*Author for correspondence; email: alan.tk.lee@googlemail.com
Received 30 May 2017; revision accepted 17 January 2018
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... Although more data are needed, the species should likely be better listed as Endangered or Critically Endangered. The South African fynbos endemic Fynbos Buttonquail, until recently, was listed as Endangered, However, Lee et al. (2018) found it to be more common and widespread than previously thought, thus proposing to down list the species to Vulnerable. This species ranks in our index with lower or similar values than other Least Concern species. ...
... Single predictions derived from these measures of fire may only identify areas that have fire regimes broadly suitable for a species persistence, but may fail to identify suitable habitat at any given point in time. Alternatively, a time since fire layer can account for a species preference for particular post-fire successional habitat in SDM (Connell et al., 2017;Lee et al., 2018). However, a single SDM prediction using time since fire depicts habitat at an exact point in time and does not reflect the variation of this habitat through time. ...
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