The assessment of wildlife population sizes and their trends is one of
the most important research fields in conservation biology, as it is
used to identify vulnerability soon enough to implement measures in
threatened species, or to set up sustainable harvesting rates in
exploited populations. Yet, because field work is expensive, may be
difficult in terms of logistics and because some populations of the same
species may be monitored by different stakeholders, population status
often rely on fragmented and heterogenous information on sub-populations
collected through various monitoring programs. In this context, data
integration, i.e. the simultaneous analysis of different datasets in a
single modelling framework allows to get unbiased and more precise trend
estimates than separated analysis that in turn may lead to more adequate
management policies. In this study we developed an integrated
state-space model to jointly model populations growth rates from
individual counts and hunting bags data for three hunted species of
mountain Galliformes in Italy. We examined population trends at various
spatial scales and disentangled the potential effect of game management
plans from biological factors. The integration of counts and bags
succeeded in improving growth rate parameter precision and in reducing
proxy-specific bias by increasing the sample size and extending data
series length. On a 19-year basis, all three species exhibited negative
mean growth rates. We did not find strong regional patterns for Rock
ptarmigan and Rock partridge, as a likely consequence of prevailing
effects of local environmental conditions on population growth rate.
Black grouse eastern populations exhibited lower growth rate than
western populations. Our paper demonstrates that an integrated model of
different index of population size of game species can provide more
accurate values than separate analysis, we advocate to consider such an
approach for other wildlife monitoring cases for which data is scarce.