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We build on previous work which explained the origin of myriad gullies and incised channels on the dry, sandy uplands of northern Lower Michigan by invoking widespread permafrost. Indicators of permafrost (ice-wedge casts and patterned ground) are known from many sites across the region. Our study area, within an extensive reentrant of the retreating Laurentide Ice Sheet, had been particularly well positioned, geographically, for permafrost. Our goal was to characterize the geomorphic characteristics of the gullies on 72 large ridges, to address the hypothesis that they had formed in association with permafrost. Across the study area, thousands of dry, narrow channels and gullies occur in dense networks, typically with channels aligned directly downslope, in parallel drainage patterns. Most of the gullies exhibit only a minimal amount of incision (ca. 2–3 m), a nearly straight longitudinal profile, and lack a clear depositional fan at their mouth. Even where small fans are present, they are subtle and exhibit little down-fan textural sorting, as would be present in larger, more mature fluvial systems. Gully morphologies did not exhibit strong morphological differences as a function of aspect, as we would have expected for an erosional, periglacial system forming on fairly steep slopes. Nonetheless, in these sandy/gravelly sediments, we could find no other scenario that would have allowed for runoff and gully formation, except ice-rich permafrost that limited infiltration and promoted saturation of the active layer, and eventually, runoff. We conclude that the gullies formed via thermo-erosion into ice-rich permafrost, involving mostly fluvial processes but also some slope failure. Even though thermo-erosion can rapidly form deep gullies, our study area has mainly weak gully forms, perhaps because: (1) permafrost existed here only briefly, (2) the landscape was so cold and the permafrost so ice-rich that runoff was rare, (3) the permafrost on the sandy slopes remained somewhat permeable, limiting runoff, and/or (4) the paleoclimate was so dry that little water was available for sediment transport. We could find no evidence that the gullies developed within preexisting polygonal networks, as is happening today in polar regions under a warming climate. Thus, our study has implications for areas of the Arctic and Antarctic that are, today, experiencing rapid hydrological changes. 

Abstract Determining the spatial distributions of species and communities is a key task in ecology and conservation efforts. Joint species distribution models are a fundamental tool in community ecology that use multi‐species detection–nondetection data to estimate species distributions and biodiversity metrics. The analysis of such data is complicated by residual correlations between species, imperfect detection, and spatial autocorrelation. While many methods exist to accommodate each of these complexities, there are few examples in the literature that address and explore all three complexities simultaneously. Here we developed a spatial factor multi‐species occupancy model to explicitly account for species correlations, imperfect detection, and spatial autocorrelation. The proposed model uses a spatial factor dimension reduction approach and Nearest Neighbor Gaussian Processes to ensure computational efficiency for data sets with both a large number of species (e.g., >100) and spatial locations (e.g., 100,000). We compared the proposed model performance to five alternative models, each addressing a subset of the three complexities. We implemented the proposed and alternative models in thespOccupancysoftware, designed to facilitate application via an accessible, well documented, and open‐source R package. Using simulations, we found that ignoring the three complexities when present leads to inferior model predictive performance, and the impacts of failing to account for one or more complexities will depend on the objectives of a given study. Using a case study on 98 bird species across the continental US, the spatial factor multi‐species occupancy model had the highest predictive performance among the alternative models. Our proposed framework, together with its implementation inspOccupancy, serves as a user‐friendly tool to understand spatial variation in species distributions and biodiversity while addressing common complexities in multi‐species detection–nondetection data.