Combining capture-recapture data and known ages allows estimation of age-dependent survival rates

Preserving identity in capture–mark–recapture studies: increasing the accuracy of minimum number alive (MNA) estimates by incorporating inter-census trapping efficiency variation

Quantifying abundance is often key to understanding ecological and evolutionary processes in wild populations. Despite shortcomings in producing accurate abundance estimates, minimum number alive (MNA) remains a widely used tool, due to its intuitive computation, reliable performance as an abundance indicator, and linkage to individual life-histories. Here, we propose a novel “efficiency-modified” MNA (eMNA) metric, which aims to preserve MNA’s favourable aspects while remedying its flaws, by incorporating (a) growth correlates to back-age individuals first captured as adults, and (b) estimates of undetected persistence beyond last capture based on time-varying capture efficiency. We evaluate eMNA through samplings of a simulated baseline population parameterised using data from a long-term demographic study of European badgers (Meles meles), under three different levels of capture efficiency (low; intermediate/“real” based on badger field data; high). We differentiate between eMNA’s performance as an abundance estimator—how well it approximates true abundance (accuracy)—and as an abundance indicator—how tightly it correlates with population abundance and changes thereof (precision). eMNA abundance estimates were negatively biased at all capture efficiencies. However, this bias was negligible at intermediate-to-high capture efficiency, particularly once low-information terminal sampling years (the first year and final three years of simulated studies) were removed. Excluding these years, eMNA under-estimated abundance by only 3.5 badgers (1.5% of population) at intermediate (real) capture efficiencies, and performed as a precise abundance indicator, with half the standard deviation of Cormack–Jolly–Seber probabilistic estimates and proving robust to inter-sampling variation in capture efficiency. Using undetected persistence probabilities to parameterise survival regression, we recreated baseline age-based survival relationships, albeit with some negative bias for under-represented ages. We offer considerations on the continued limitations of using eMNA for abundance estimates, minimum study duration for reliability, the metric’s benefits when individual identity is required, and potential for further improvement.

Joint estimation of growth and survival from mark-recapture data to improve estimates of senescence in wild populations

Understanding age-dependent patterns of survival is fundamental to predicting population dynamics, understanding selective pressures, and estimating rates of senescence. However, quantifying age-specific survival in wild populations poses significant logistical and statistical challenges. Recent work has helped to alleviate these constraints by demonstrating that age-specific survival can be estimated using mark-recapture data even when age is unknown for all or some individuals. However, previous approaches do not incorporate auxiliary information that can improve age estimates of individuals. We introduce a survival estimator that combines a von Bertalanffy growth model, age-specific hazard functions, and a Cormack-Jolly-Seber mark-recapture model into a single hierarchical framework. This approach allows us to obtain information about age and its uncertainty based on size and growth for individuals of unknown age when estimating age-specific survival. Using both simulated and real-world data for two painted turtle (Chrysemys picta) populations, we demonstrate that this additional information substantially reduces the bias of age-specific hazard rates, which allows for the testing of hypotheses related to aging. Estimating patterns of senescence is just one practical application of jointly estimating survival and growth; other applications include obtaining better estimates of the timing of recruitment and improved understanding of life-history trade-offs between growth and survival.

Increased population size of fish in a lowland river following restoration of structural habitat

Most assessments of the effectiveness of river restoration are done at small spatial scales (<10 km) over short time frames (less than three years), potentially failing to capture large-scale mechanisms such as completion of life-history processes, changes to system productivity, or time lags of ecosystem responses. To test the hypothesis that populations of two species of large-bodied, piscivorous, native fishes would increase in response to large-scale structural habitat restoration (reintroduction of 4,450 pieces of coarse woody habitat into a 110-km reach of the Murray River, southeastern Australia), we collected annual catch, effort, length, and tagging data over seven years for Murray cod (Maccullochella peelii) and golden perch (Macquaria ambigua) in a restored "intervention" reach and three neighboring "control" reaches. We supplemented mark-recapture data with telemetry and angler phone-in data to assess the potentially confounding influences of movement among sampled populations, heterogeneous detection rates, and population vital rates. We applied a Bayesian hierarchical model to estimate changes in population parameters including immigration, emigration, and mortality rates. For Murray cod, we observed a threefold increase in abundance in the population within the intervention reach, while populations declined or fluctuated within the control reaches. Golden perch densities also increased twofold in the intervention reach. Our results indicate that restoring habitat heterogeneity by adding coarse woody habitats can increase the abundance of fish at a population scale in a large, lowland river. Successful restoration of poor-quality "sink" habitats for target species relies on connectivity with high-quality "source" habitats. We recommend that the analysis of restoration success across appropriately large spatial and temporal scales can help identify mechanisms and success rates of other restoration strategies such as restoring fish passage or delivering water for environmental outcomes.