DISCUSSION
Our results show that sample counts can be adequate to monitor
population trends of mountain ungulates in a large area. Sampling half
of the total area allowed us to correctly identify medium to strong
population trends. In addition, in most of the cases in which sample
counts were not able to estimate the population trend, also complete
counts failed in detecting the direction or magnitude of the trend. The
retrospective power analysis confirmed our results, as the trends
historically estimated with complete counts would have been inferred
equally well, with a sufficient statistical power, using sample counts
with only half of the sectors monitored. Given that the results obtained
with sample counts are very similar to those obtained with a complete
count, we suggest that sample counts (in particular monitoring half of
the target area) can be used as a viable alternative when monitoring
trends, hence allowing for important, cost-effective improvements in the
monitoring of wild animals of conservation interest. Time and budget
constraints can indeed disincentivize wildlife managers to implement
monitoring programs (McDonald-Madden et al. 2010), while potentially
reducing the survey effort could lead to undertake monitoring projects
that are otherwise impossible to perform.
In the case of Alpine ibex, the species is currently monitored
throughout its entire area of extent in the Alps without a standardized
protocol and, while in GPNP a long time series is available, the lack of
data in many other areas makes impossible to draw reliable conclusions
about the population trends (Brambilla et al. 2020a). Reducing the costs
of monitoring by using sample counts, could therefore be of critical
importance to allow more conservation and management authorities to
perform periodic counts. Assessing population trends in Alpine ibex is
essential as the species may be sensitive to future declines (Toïgo et
al. 2020) because of low recolonization rates, low genetic diversity and
high inbreeding levels (Biebach and Keller 2009, Brambilla et al. 2014,
2020b) and heat-sensitivity (Aublet et al. 2008, Mason et al. 2017,
Semenzato et al. 2021) in an environment that suffers strong global
warming effects (Giorgi 2006, Gobiet et al. 2014, Rogora et al. 2018).
However, as the reliability of sample counts depends on the variability
between years and sectors, wildlife managers must carefully
evaluate the expected reliability of censuses in their specific case.
Sample counts can also be useful to perform a quick and highly
cost-effective preliminary monitoring in new areas to collect
information on the status of a population in the short term, as we
showed that the direction of a strong trend over 10 years can be
detected even with very few sectors sampled (less than 5 out of 38). For
instance, the method can be useful to detect recovery after epidemic
outbreaks with high mortality, common in mountain ungulates as the
Alpine ibex (Giacometti et al. 2002, Garnier et al. 2016, Pérez et al.
2021), or to identify a strong response after management actions such as
reintroductions (Giacometti 1991, Stüwe and Nievergelt 1991, Brambilla
et al. 2020a).
However, if there are sufficient resources, sampling the entire area may
still be preferable than using sample counts. Sampling all sectors,
indeed, reduced the occurrence of errors in trend detection that, even
if at a low frequency, were present with sample counts. Furthermore,
sampling the entire area would allow for less biased abundance
estimates, as shown in our results and as pointed out by Sutherland
(2006). An accurate estimate of population size is important for
instance to measure carrying capacity (Holzgang 1997, Terry Bowyer et
al. 2014) or for management purposes such as reintroductions (Peracino
and Bassano 1990) and hunting (e.g., Carvalho et al. 2018). Therefore,
conservation authorities must weigh up the costs and benefits of using
sample counts, using them to reliably detect population trends at a
lower cost or performing complete counts to measure abundance with
higher financial effort but also reduced errors in the estimation of
trends and abundance.
We also showed that neither sample counts nor complete counts could
reliably monitor the magnitude of short-term trends and that for such
analysis at least 15 or 20 years of censuses data are needed. This
result is consistent with several other studies pointing out that more
than 10 years are required to draw reliable conclusions on population
trends (e.g., Gerber et al. 1999, Hatch 2003, White 2019). Long-term
wildlife monitoring projects are indeed of critical importance for
conservation (Nichols and Williams 2006, Magurran et al. 2010,
Giron-Nava et al. 2017), but series of yearly counts are lacking for
most mountain ungulates (Singh and Milner-Gulland 2011, Brambilla et al.
2020a, Nuttall et al. 2022). Therefore, we further recommend that
stakeholders plan long-term monitoring projects to correctly evaluate
population dynamics at a local and global scale. Sample counts, that
proved in this study to be as reliable as complete counts in detecting
the long-term trends if half of the sectors are sampled, can constitute
a viable method to reduce the required census effort for such projects,
but further studies are needed before they can also be applied in
abundance estimations.
Our results show that the strongest constraint for accurate trend
estimations is the variability between years, for which Alpine ibex in
GPNP showed a coefficient of variation around 0.05 in the last 65 years.
Keith et al. (2015) found that, among nine species of terrestrial
mammals for which population trend was estimated counting the total
number of individuals in a specified area (mostly Cricetidae and
Muridae), the mean variability between years was 0.052, and Saiga
tatarica , the only ungulate species in the study, showed a yearly
variation of 0.058. Therefore, sample counts would likely have been
efficient for estimating the trend also in those species and other with
similar trend variation over time. Under a greater variability,
estimating the population dynamics could be subject to severe errors
even with complete counts and especially for weak trends, as also
pointed out by other studies (Wilson et al. 2011, Rhodes and Jonzén
2011, Rueda-Cediel et al. 2015).
In general, in Keith et al. (2015) species with lower yearly variation
of abundance were mostly those with a higher generation time
(coefficient of variation was 0.088 for species that give birth to a new
generation within 2 years and 0.026 for species with longer generation
time, with a mean variability of 0.014 if the generation length was
higher than 5 years). Species with a long life-history can indeed show
less extreme yearly abundance fluctuations, as for example they are
slower to react to fast environmental changes (Berteaux et al. 2004).
However, White (2019) showed that life history traits (such as
generation length) have only a very weak influence on the minimum time
required to accurately monitor the species, while time series
characteristics (e.g., trend strength) seems to be a much more important
driver. Our results could therefore potentially be relevant to any
animal species with a sufficient generation length (possibly more than 2
years), but the applicability to other species with a different
life-history must be tested.
In our simulations, sampling half of the sectors allowed to achieve a
sufficient statistical power even with high values for
cvs and cvd. A large number of species
is likely to exhibit a trend variability between sectors in the range we
used in our simulations (i.e., 0.05-0.20): Weiser et al. (2019) reported
a sector-specific variation between 0.05 and 0.1 in many species of
different taxa (from 0.02 to 0.024 in small mammals). Additionally, the
wide range of cvd we used in the simulations (0.1-0.5)
suggests that our results can be expanded to many study areas in which
spatial variation of parameters that affect detectability is high.
We also show that the overall detectability in the target area did not
have any effect on the statistical power, thus trend estimation could be
performed with cost-effective methods such as block counts, that are
believed to underestimate population size (Loison et al. 2006, Morellet
et al. 2007, Corlatti et al. 2015). In such case, the costs of trend
estimations could be further reduced by the use of block counts instead
of other techniques, that account for imperfect detection but require a
higher field or data analysis effort, such as Capture-Mark-Resight
(Schwarz and Seber 1999) or Distance Sampling (Buckland et al. 2001).
Besides, also several sources of errors in abundance estimation, such as
underestimations of group size (Vallecillo et al. 2021) or missed
detection of small groups (Samuel and Pollock 1981), could possibly be
ignored while performing sample counts to estimate direction and
magnitude of population trends. Our results about detectability are in
contrast to other studies, for which a low detectability critically
influenced the ability of detecting a trend (e.g. Newson et al. 2013,
Ficetola et al. 2018, Sanz-Pérez et al. 2020). However, some of these
studies included the possibility of sites with very few animals and a
detectability (p) close to zero (e.g. abundance of 7.5 per site and
p=0.05 in Ficetola et al. 2018), thus analyzed the situation in which no
animals were detected in most of the sites. This phenomenon, not present
in our Alpine ibex population where the population per sector was
higher, could have been therefore the major cause of a difficult trend
estimation in such studies. Besides, in other of the above mentioned
researches, the detectability was also variable across the sites (e.g.
Sanz-Pérez et al. 2020), and such effect could have been the actual
driver of a difficult trend estimation, rather than the value of
detectability, as we show here.
When using sample counts, the monitoring costs can be further reduced by
performing censuses only in the sectors with the highest abundance (that
can be easier to sample). However, as the method of sector selection had
a weak effect on statistical power of trend estimations, also selecting
sectors based on other needs could lead to a reliable analysis. This
result is in contrast with Fournier et al. (2019) who claimed that
counting in the best sites (i.e., those with the highest abundance)
leads to detect false trends (Fournier et al. 2019). However, as also
pointed out by Fournier and colleagues, not every real population
exhibits this bias. The advantage of selecting the most abundant sectors
however disappears if abundance is estimated instead of the population
trend, with a considerable overestimation compared to a random sector
selection.