We were able to compile for the first time find the best-available long-term data for the two species and their two metrics – brood locations and non-breeders – for the study area of Eastern Yakutia and Chukotka. Also, we were able to obtain the best publically-available assessment data in a digital format explicit in space and time.
Further, our findings show the first achieved predictions and their assessments for post-breeding moult of Tundra Bean Goose and Greater White-fronted Goose for non-breeders and parents with brood (Table 2; Figs. 5 and 6).
Species: Tundra Bean Goose
The moulting non-breeders are primarily distributed in coastal areas of Yakutia and Western Chukotka, thus inhabiting coastal plains. A low occurrence is predicted in the eastern study area, and the birds are more or less absent in the mountains of the interior Chukotka, wider inland and along the coast of northern Bering Sea (Fig. 5a). This shows a more nuanced and complex distributional picture than what was previously known; arguably, the distribution of this species is not as crisp as presented and assumed elsewhere.
broods, and b) post-breeding nonbreeders for the study area). Best-available GBIF
presence location for this species are overimposed for assessment.
For the parents with broods the above pattern shows even stronger, with the parents and broods primarily occurring in the western section of the study area. It is noteworthy that the parents with broods are absent along the coastline and are found more inland, primarily Yakutia Arctic and around the wider Chaun Bay region, while Chukotka Peninsula is widely free of this cohort (Fig. 6b).
It is noteworthy that the non-breeders are not really overlapping with the parents with broods; the latter concentrate in the western section of the study area and more inland.
Species: Greater White-fronted Goose
The moulting non-breeders are widely dispersed in the study area but seem to avoid the mountain habitats, e.g. inner parts of the Chukotka Peninsula and parts of Yakutia.
location for this species are overimposed for assessment.
For the actual parents with broods it shows an almost opposite pattern, where the species is found in the interior, specifically in Chukotka and in Yakutia.
The patterns are hardly overlapping and are somewhat complementary to each other. There are two distinct patches, leaving a coastal area free of this species.
Model performance details and assessment
Model performance details
Our models achieved good to very good accuracy. Predictors most strongly center around an interaction between climatic metrics like summer precipitation, temperature, as well as elevation and landcover categories, added by NDVI (detail shown in Appendix 4). While the human footprint showed a smaller role, those trends were upwards indicating that those geese are somewhat affiliated with the human footprint.
For Tundra Bean Goose broods we identified NDVI as a powerful predictor with a positive relationship (Appendix 4). Together with lower elevations below 150 m it indicates where brood-rearing habitats can be found in the study area. For non-breeders we found precipitation in July as a powerful predictor with a positive relationship (Appendix 4). Together with specific with landcover classes it indicates where moulting areas can be found in the study area.
For Greater White-fronted Goose broods we found precipitation in July as a powerful predictor, but with a negative relationship (Appendix 4). Together with somewhat higher elevations around 300 m it indicates where brood-rearing geese occur in the study area. For non-breeders we identified elevation as a powerful14 predictor with a negative relationship (Appendix 4). Together with specific landcover classes it indicates where moulting flocks can be found in the study area.
For robust inference and evidence we actually used four pathways to assess the performance of our data-based model predictions for Tundra Bean Goose and Greater White-fronted Goose and their post-breeding non-breeders and parents with brood. The first is the internal aspatial ROC metric that comes with the exploratory model data itself. It shows a ROC of 82 % (Tundra Bean Goose non-breeders), 85 % (Tundra Bean Goose broods), 91% (Greater White-fronted Goose non breeders) and 94% (Greater White-fronted Goose broods) for both species and their metrics. The ROC is based on the confusion matrix from the binary presence and pseudo-absence of the two survey data used for each of the two species (see Figs. 3, 4, 5 and 6). Those assessments indicate already a rather good model on the training data.
The second – more thorough - assessment is based on a visual match of the predictions with their training data, allowing us to provide evidence of a good general match of the pattern predicted (see Figs. 5 and 6) for the two species and their metrics.
The third assessment, more independent but less specific for parents with broods and non-breeders, is based on the GBIF.org data and the compiled literature references for the species and its ecological niche overall in summer, less though for the brood and the non-breeders (see Table 1b; Fig. 7). But at least on a generic level it shows a very high match for the models (compare with Figs. 5, 6).
Taken the evidences together, overall, we therefore think that the methodology shown (Fig. 2 for workflow) and results presented are a good start and offer us presentable validity, allowing to move next into thorough abundances and population trend models. Arguably, better data, e.g. more explicit, more extensive, and ideally corrected for detectability coming from a proper research design (see 55. for an example) will allow for fine-tuning our findings further while urgency in the study area (1., 2., 39.,40.) warrants good and immediate action though.