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T Scott Sillett

Publications and source records attributed to T Scott Sillett.

2 recordsLinked to original sources

Modeling two decades of elevational change in endemic bird densities in Hawaiʻi’s Kaʻū Rainforest

Protected areas are vital to the recovery of endangered species. Of the 24 remaining endemic passerines in Hawaiʻi, 16 species are endangered or critically endangered. Yet protected areas in the archipelago are changing as a result of climate change and biological invasions. For example, the non-native southern house mosquito ( Culex quinquefasciatus ), the primary vector of avian malaria ( Plasmodium relictum ), has been encroaching upward as higher elevations warm. How the ranges of endemic birds have also shifted their range upward in the Kaʻū Rainforest, the largest native forest on the Island of Hawaiʻi, is not well understood. We used hierarchical distance sampling to characterize how population density has changed from 2002 to 2024 for eight endemic bird species in the Kaʻū Rainforest. For five species, the most parsimonious model included an interaction between year and a quadratic elevation effect. Species that were most common at lower elevations in 2002, e.g., ʻApapane ( Himatione sanguinea ), tended to be most common at mid- elevations by 2024. Species that were already most common at higher elevations, such as ʻIʻiwi ( Drepanis coccinea ) and Hawaiʻi ʻĀkepa ( Loxops coccineus ), tended to decline in density. For example, ʻIʻiwi density at 1,530 m declined from 3.31 birds per ha (95% confidence interval [CI]: 2.67-4.11) to 1.34 birds per ha (95% CI: 1.05-1.71), and Hawaiʻi ʻĀkepa were completely extirpated from elevations below 1,530 m by 2024. Our results demonstrate how the elevational ranges of endemic species have shifted in response to climate change, biological invasions, and habitat degradation. Translating these results into conservation measures may require a more thorough investigation of the causal mechanisms of these range shifts with finer scale habitat data, under the same hierarchical modeling framework.

BioRxiv↗

An open-population hierarchical distance sampling model

Modeling population dynamics while accounting for imperfect detection is essential to monitoring programs. Distance sampling allows estimating population size while accounting for imperfect detection, but existing methods do not allow for direct estimation of demographic parameters. We develop a model that uses temporal correlation in abundance arising from underlying population dynamics to estimate demographic parameters from repeated distance sampling surveys. Using a simulation study motivated by designing a monitoring program for island scrub-jays ( Aphelocoma insularis ), we investigated the power of this model to detect population trends. We generated temporally autocorrelated abundance and distance sampling data over six surveys, using population rates of change of 0.95 and 0.90. We fit the data generating Markovian model and a mis-specified model with a log-linear time effect on abundance, and derived post hoc trend estimates from a model estimating abundance for each survey separately. We performed these analyses for varying number of survey points. Power to detect population changes was consistently greater under the Markov model than under the alternatives, particularly for reduced numbers of survey points. The model can readily be extended to more complex demographic processes than considered in our simulations. This novel framework can be widely adopted for wildlife population monitoring.

Ecology↗