A novel and low cost method to estimate population survival using inverse modelling
Estimating survival rates of organisms is important for use in population models and population viability analyses. However state-of-the-art methods to estimate survival, such as capture-recapture analysis, are costly since they are based on long-term monitoring and require a large number of individuals captured, and subsequently recaptured. In this sense, developing new approaches that provide accurate, but affordable, estimates of survival are a priority in population ecology. Here, we present a novel method to estimate survival rates from short-term census data. Our method combines two well-known approaches in the ecological and modeling literature (matrix population models and inverse modelling), but which joint potential has not been fully exploited. Specifically, we searched through a sample space of possible survival rates to identify which combination of parameter values best reproduces the observed age structure of the population according to a likelihood function. This method also allows us to test for changes in the survival through time (e.g. due a disturbance). We evaluated our method against a long-term monitored population (15 years) of the tortoise Testudo graeca that experienced a fire during the monitoring period.
Our model estimates of survival were very similar both in their mean values and their error measures when compared to estimates obtained using capture-recapture methods. In addition, in agreement with capture-recapture results, our approach indicated that fire strongly impacted survival the years after the fire and that this parameter recovered pre-perturbation values few years after the event. In testing the sensitivity of our method, survival estimates were accurate even with a sample size as low as 50 individuals sampled in one single year. Our results encourage the use of alternative methods, such as those based on inverse modelling, as one more possible tool for estimating demographic parameters in ecology and conservation.