Census data aggregation decisions can affect population-level inference in heterogeneous populations

Spatial autocorrelation may bias the risk estimation: An application of eigenvector spatial filtering on the risk of air pollutant on asthma

Air pollutants are major risk factors for respiratory diseases, particularly asthma, socially and spatially correlated. Many existing environment-asthma-related studies, however, have evaluated the impact of crude trends at the largest district level, which accounts only for temporal effects and may produce biased results with spatial autocorrelation. This study aimed to investigate how the spatial autocorrelation affects the air pollution effect estimations (sulfur dioxide [SO₂], nitrogen dioxide [NO₂], carbon monoxide [CO], and particulate matter [PM₁₀]) on daily asthma emergency department (ED) visits in two metropolitan areas in Korea (Seoul Metropolitan Area [SMA] and Busan Metropolitan City, Ulsan Metropolitan City, Gyeongsangnamdo [BUG]). We applied eigenvector spatial filter (ESF) to the spatio-temporal model to remove spatial autocorrelation and distributed lag nonlinear model (DLNM) to explore nonlinear patterns between air pollutant concentration and lagged days on the three models including aggregated model (a temporal model), spatial model without ESF, and spatial model with ESF (both are spatio-temporal models). The effect of SO₂ was not statistically significant for asthma ED visits in the aggregated model for SMA (cumulative relative risks [CRR] = 0.99, confidence intervals [CI]: 0.93-1.05), while the effect was statistically significant in the spatial model with ESF (CRR = 1.10, CI: 1.08-1.12). NO₂ and CO were positively correlated to asthma ED visits in the spatial model without ESF (CRR = 0.84, CI: 0.81-0.86; 0.91, 0.89-0.94, respectively), but the spatial model with ESF showed significant risks (CRR = 1.21, CI: 1.18-1.24; 1.13, 1.11-1.16). Moreover, the spatial model with ESF successfully removed spatial autocorrelation (P-values for Moran's I 0.83-0.98) and demonstrated the highest model fit (McFadden's pseudo R² 0.42-0.43 for SMA and 0.26-0.27 for BUG) among the three models. Our findings demonstrate how ESF can be introduced into spatial correlation to remove bias and construct more reliable models.

Life-history predicts global population responses to the weather in terrestrial mammals

With the looming threat of abrupt ecological disruption due to a changing climate, predicting which species are most vulnerable to environmental change is critical. The life-history of a species is an evolved response to its environmental context, and therefore a promising candidate for explaining differences in climate-change responses. However, we need broad empirical assessments from across the world's ecosystems to explore the link between life history and climate-change responses. Here, we use long-term abundance records from 157 species of terrestrial mammals and a two-step Bayesian meta-regression framework to investigate the link between annual weather anomalies, population growth rates, and species-level life history. Overall, we found no directional effect of temperature or precipitation anomalies or variance on annual population growth rates. Furthermore, population responses to weather anomalies were not predicted by phylogenetic covariance, and instead there was more variability in weather responses for populations within a species. Crucially, however, long-lived mammals with smaller litter sizes had smaller absolute population responses to weather anomalies compared with their shorter living counterparts with larger litters. These results highlight the role of species-level life history in driving responses to the environment.

Sustained reduction in numbers of Australian fur seal pups: Implications for future population monitoring

Fur seal populations in the Southern Hemisphere were plundered in the late 1700s and early 1800s to provide fur for a clothing industry. Millions of seals were killed resulting in potentially major ecosystem changes across the Southern Hemisphere, the consequences of which are unknown today. Following more than a century of population suppression, partly through on-going harvesting, many of the fur seal populations started to recover in the late 1900s. Australian fur seals (Arctocephalus pusillus doriferus), one of the most geographically constrained fur seal species, followed this trend. From the 1940s to 1986, pup production remained at approximately 10,000 per year, then significant growth commenced. By 2007, live pup abundance had recovered to approximately 21,400 per year and recovery was expected to continue However, a species-wide survey in 2013 recorded a 20% decline, to approximately 16,500 live pups. It was not known if this decline was due to 2013 being a poor breeding year or a true population reduction. Here we report the results of a population-wide survey conducted in 2017 and annual monitoring at the most productive colony, Seal Rocks, Victoria that recorded a large decline in live pup abundance (-28%). Sustained lower pup numbers at Seal Rocks from annual counts between 2012–2017 (mean = 2908 ± 372 SD), as well as the population-wide estimate of 16,903 live pups in 2017, suggest that the pup numbers for the total population have remained at the lower level observed in 2013 and that the 5-yearly census results are not anomalies or representative of poor breeding seasons. Potential reasons for the decline, which did not occur range-wide but predominantly in the most populated and long-standing breeding sites, are discussed. To enhance adaptive management of this species, methods for future monitoring of the population are also presented. Australian fur seals occupy several distinct regions influenced by different currents and upwellings: range-wide pup abundance monitoring enables comparisons of ecosystem status across these regions. Forces driving change in Australian fur seal pup numbers are likely to play across other marine ecosystems, particularly in the Southern Hemisphere where most fur seals live.