Migration routes, population status and important sites used by the globally threatened Black-faced Spoonbill (Platalea minor): a synthesis of surveys and tracking studies

Background The Black-faced Spoonbill (Platalea minor) is a globally threatened species, nesting mainly in western Korea with smaller numbers breeding in Liaoning Province, China, and Far East Russia. Recent winter field surveys to estimate the species’ population size were almost totally conducted in coastal areas, but tracking studies showed that some individuals now winter inland. To ensure its long-term survival, we need a more comprehensive assessment of the current distribution and abundance of the species. Methods We combined the most recent count data and satellite tracking information to update existing information about the population abundance and distribution of the Black-faced Spoonbill at all stages of its annual life cycle, and how these have changed during 2004–2020. Results Black-faced Spoonbills mainly breed on the west coast of the Korean peninsula, while immature birds show a wider summer distribution throughout Yellow Sea coastal areas, when a few remain on wintering sites in the south. Combined tracking results and mid-winter counts confirmed known wintering sites on the east and south coasts of China, but showed that the species also winters on wetlands in the Yangtze River floodplain and in Southeast Asia. During 2004–2020, counts of wintering birds in coastal habitats increased from 1198 to 4864, with numbers wintering on the island of Taiwan contributing most to the overall increase. Latest counts found 5222 in 2021. We also identify key wintering and stopover sites as well as their current conservation status. Conclusions This study revised the known summering and wintering ranges of the Black-faced Spoonbill and assessed the conservation status of key sites based on a combination of field survey and satellite tracking data. We recommend prioritisation of further field research to identify and survey inland wintering areas in the Yangtze River floodplain and summering areas of immature birds. More tracking of adult individuals and birds during spring migration is necessary to fill these information gaps. We also suggest establishing a Black-faced Spoonbill monitoring platform to store, share and show real-time distribution range and population abundance data.

Ontogenetic shifts from social to experiential learning drive avian migration timing

Migrating animals may benefit from social or experiential learning, yet whether and how these learning processes interact or change over time to produce observed migration patterns remains unexplored. Using 16 years of satellite-tracking data from 105 reintroduced whooping cranes, we reveal an interplay between social and experiential learning in migration timing. Both processes dramatically improved individuals’ abilities to dynamically adjust their timing to track environmental conditions along the migration path. However, results revealed an ontogenetic shift in the dominant learning process, whereby subadult birds relied on social information, while mature birds primarily relied on experiential information. These results indicate that the adjustment of migration phenology in response to the environment is a learned skill that depends on both social context and individual age. Assessing how animals successfully learn to time migrations as environmental conditions change is critical for understanding intraspecific differences in migration patterns and for anticipating responses to global change.

Modeling the Wintering Habitat Distribution of Oriental Honey Buzzards in West Java Indonesia with Satellite Tracking Data Using Logistic Regression

Oriental honey buzzards (OHBs, Pernis ptilorhynchus) are one of migratory raptor from Japan to Indonesia which is widely recognized as indicator species reflecting the conditions of their habitat. Since 2003, OHBs have been satellite-tracked in their wintering grounds in Indonesia. Less information available on wintering areas in the west Java, which hampers the OHB conservation efforts. This paper proposes a new approach for predicting the probability models of the wintering habitat distribution of OHBs with the presence data derived from satellite tracking using logistic regression analysis coupled with RAMAS GIS. This spatial model was locally constructed from the data concerning Talaga Bodas and its surrounding areas and extrapolated for the entire West Java region. The best predicted probability model successfully characterized the distribution of the OHB wintering habitat using slope (25–40%), elevation (0–300 m and >1,000 m), and land cover (forest, paddy field, and water body). The extrapolation model generated potential areas of the wintering habitat distribution covering an area of 3013.13 km2 (8.11% of West Java). These areas were predominantly located outside the protected areas (94.04%). The modeling approach proposed herein may be used to study other migratory species that are tracked using satellite or other navigation technologies.

Stochastic satellite tracking with constrained budget via structured-chromosome genetic algorithms

This paper focuses on the scheduling under uncertainty of satellite tracking from a heterogeneous network of ground stations taking into account allocated resources. An optimisation-based approach is employed to efficiently select the optimal tracking schedule that minimises the final estimation uncertainty. Specifically, the scheduling is formulated as a variable-size problem, and a Structured-Chromosome Genetic Algorithm is developed to tackle the mixed-discrete global optimisation. The search algorithm employs genetic operators specifically revised to handle hierarchical search spaces. An orbit determination routine is run within each call to the fitness function to quantify the estimation uncertainty resulting from each candidate tracking schedule. The developed scheduler is tested on the tracking optimisation of a satellite in low Earth orbit, a highly perturbed dynamical regime. The obtained results show that the variable-size variants of Genetic Algorithms always outperform the fixed-size counterparts employed for comparison. In particular, Structured-Chromosome Genetic Algorithm is shown to find significantly better schedules under severely limited budgets.

Adaptive Predictive Functional Control of X-Y Pedestal for LEO Satellite Tracking Using Laguerre Functions

In this paper, Predictive Functional Control (PFC) is used for X-Y pedestal control for LEO satellite tracking. According to the nonlinear characteristics of the X-Y pedestal and pedestal model variation caused by its operating point change, the use of system identification algorithm, which is based on special types of orthonormal functions known as Laguerre functions, is presented. This algorithm is combined with PFC to obtain a novel adaptive control algorithm entitled Adaptive Predictive Functional Control (APFC). In this combination, Laguerre functions are utilized for system identification, while the PFC is the control law. An interesting feature of the proposed algorithm is its desirable performance against the interference effect of channel X and channel Y. The proposed APFC algorithm is compared with Proportional Integral Derivative (PID) controller using simulation results. The results confirm that the proposed controller improves the performance in terms of the pedestal model variations; that is, the controller is capable of adapting to the model changes desirably.

Time-Variable Gravity Field from the Combination of HLSST and SLR

The Earth’s time-variable gravity field is of great significance to study mass change within the Earth’s system. Since 2002, the NASA-DLR Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE follow-on mission provide observations of monthly changes in the Earth gravity field with unprecedented accuracy and resolution by employing low-low satellite-to-satellite tracking (LLSST) measurements. In addition to LLSST, monthly gravity field models can be acquired from satellite laser ranging (SLR) and high-low satellite-to-satellite tracking (HLSST). The monthly gravity field solutions HLSST+SLR were derived by combining HLSST observations of low earth orbiting (LEO) satellites with SLR observations of geodetic satellites. Bandpass filtering was applied to the harmonic coefficients of HLSST+SLR solutions to reduce noise. In this study, we analyzed the performance of the monthly HLSST+SLR solutions in the spectral and spatial domains. The results show that: (1) the accuracies of HLSST+SLR solutions are comparable to those from GRACE for coefficients below degree 10, and significantly improved compared to those of SLR-only and HLSST-only solutions; (2) the effective spatial resolution could reach 1000 km, corresponding to the spherical harmonic coefficient degree 20, which is higher than that of the HLSST-only solutions. Compared with the GRACE solutions, the global mass redistribution features and magnitudes can be well identified from HLSST+SLR solutions at the spatial resolution of 1000 km, although with much noise. In the applications of regional mass recovery, the seasonal variations over the Amazon Basin and the long-term trend over Greenland derived from HLSST+SLR solutions truncated to degree 20 agree well with those from GRACE solutions without truncation, and the RMS of mass variations is 282 Gt over the Amazon Basin and 192 Gt in Greenland. We conclude that HLSST+SLR can be an alternative option to estimate temporal changes in the Earth gravity field, although with far less spatial resolution and lower accuracy than that offered by GRACE. This approach can monitor the large-scale mass transport during the data gaps between the GRACE and the GRACE follow-on missions.