History and Productivity Determine the Spatial Distribution of Key Habitats for Biodiversity in Norwegian Forest Landscapes

Abstract

Retention forestry, including the retention of woodland key habitats (WKH) at the forest stand scale, has become an essential management practice in boreal forests. Here, we investigate the spatial distribution of 9470 habitat patches, mapped according to the Complementary Habitat Inventory method (CHI habitats), as potential WKHs in 10 sample areas in Norway. We ask whether there are parts of the forest landscapes that have consistently low or high density of CHI habitats compared to the surveyed landscape as a whole, and therefore have a low or high degree of conflict with harvesting, respectively. We found that there was a general pattern of clumped distribution of CHI habitats at distances up to a few kilometres. Furthermore, results showed that most types of CHI habitats were approximately two to three times as common in the 25% steepest slopes, lowest altitudes and highest site indices. CHI habitats that are most common in old-growth forests were found at longer distances from roads, whereas habitats rich in deciduous trees were found at shorter distances from roads than expected. Both environmental factors and the history of human impact are needed to explain the spatial distribution of CHI habitats. The overrepresentation of WKHs in parts of the forest landscapes represents a good starting point to develop more efficient inventory methods.

Prediction of biodiversity hotspots in the Anthropocene: The case of veteran oaks

“…However, in forests, harvesting is difficult in steep areas, where veteran oaks consequently have a strongly elevated probability of occurrence compared to similar slopes in the open landscape (Figure b). This aligns well with the general overrepresentation of key habitats for biodiversity in steep terrain in forests (Sætersdal, Gjerde, Heegard, Schei, & Nilsen, ) and with our previous work suggesting that the diversity and species composition in oak hot spot habitats differ in open landscapes and forests, and respond to different factors in these two systems (Sverdrup‐Thygeson, Skarpaas, & Ødegaard, ). When responses differ strongly between landscape types, as in the veteran oak examples, simply adding landscape type as another covariate in a (generalized) linear regression model is not enough to resolve the problem.…”

“…However, in forests, harvesting is difficult in steep areas, where veteran oaks consequently have a strongly elevated probability of occurrence compared to similar slopes in the open landscape ( Figure 3b). This aligns well with the general overrepresentation of key habitats for biodiversity in steep terrain in forests (Saetersdal, Gjerde, Heegard, Schei, & Nilsen, 2016) and with our previous work suggesting that the diversity and species composition in oak hot spot habitats differ in open landscapes and forests, and respond to different factors in these two systems (Sverdrup-Thygeson, Skarpaas, & Ødegaard, 2010 This brings us to the second general challenge for spatial prediction modeling: different predictors may be relevant only in specific areas (prediction 2). Humans frequently modify ecosystems to the extent that original natural processes are no longer the most relevant and important processes structuring ecosystems (Ellis et al, 2010).…”

“…Humans frequently modify ecosystems to the extent that original natural processes are no longer the most relevant and important processes structuring ecosystems (Ellis et al., ). For instance, for veteran oaks (and other old trees; see Lindenmayer & Laurance, ; Sætersdal et al., ), dominant tree species, and forest productivity are important variables in forests, but not relevant in the open landscape where the forest has been cleared and kept away, often for centuries. This implies that the sets of relevant and important predictor variables are different in the two landscape types.…”