Land‐use and climatic causes of environmental novelty in Wisconsin since 1890

Abstract

Multiple global change drivers are increasing the present and future novelty of environments and ecological communities. However, most assessments of environmental novelty have focused only on future climate and were conducted at scales too broad to be useful for land management or conservation. Here, using historical county‐level data sets of agricultural land use, forest composition, and climate, we conduct a regional‐scale assessment of environmental novelty for Wisconsin landscapes from ca. 1890 to 2012. Agricultural land‐use data include six cropland types, livestock densities for four livestock species, and human populations. Forestry data comprise biomass‐weighted relative abundances for 15 tree genera. Climate data comprise seasonal means for temperature and precipitation. We found that forestry and land use are the strongest cause of environmental novelty (NoveltyForest = 3.66, NoveltyAg = 2.83, NoveltyClimate = 1.60, with Wisconsin's forests transformed by early 20th‐century logging and its legacies and multiple waves of agricultural innovation and obsolescence. Climate change is the smallest contributor to contemporary novelty, with precipitation signals stronger than temperature. Magnitudes and causes of environmental novelty are strongly spatially patterned, with novelty in southern Wisconsin roughly twice that in northern Wisconsin. Forestry is the most important cause of novelty in the north, land use and climate change are jointly important in the southwestern Wisconsin, and land use and forest composition are most important in central and eastern Wisconsin. Areas of high regional novelty tend also to be areas of high local change, but local change has not pushed all counties beyond regional baselines. Seven counties serve as the best historical analogues for over one‐half of contemporary Wisconsin counties (40/72), and so can offer useful historical counterparts for contemporary systems and help managers coordinate to tackle similar environmental challenges. Multi‐dimensional environmental novelty analyses, like those presented here, can help identify the best historical analogues for contemporary ecosystems, places where new management rules and practices may be needed because novelty is already high, and the main causes of novelty. Separating regional novelty clearly from local change and measuring both across many dimensions and at multiple scales thus helps advance ecology and sustainability science alike.

Caught in a bottleneck: Habitat loss for woolly mammoths in central North America and the ice‐free corridor during the last deglaciation

“…Such species usually are highly mobile and require large habitat areas, so high habitat connectivity helps these species to track and adjust to rapid climate change (McGuire et al., 2016). The projected rates and magnitudes of future global temperature rise are similar to or greater than those of the last deglaciation (Burke et al., 2018; Williams et al., 2019). The brief opening and limited suitability of the ice‐free corridor during the extinction window of 14–11 ka offers an object lesson in how low connectivity can enhance extinction risk during periods of changing environments and intensified human pressures.…”

“…However, movement corridors must be suited to particular species, possessing a suitable habitat for dispersal and transient survival (Baguette et al., 2013; Bowler & Benton, 2005). Given that nearly one‐quarter of species have a high risk of extinction (IPBES, 2019) and that the Earth system is approaching rates and magnitudes of climate change unseen since the end of the last glacial period, if ever (Burke et al., 2018; Williams et al., 2019), a key research need is to understand whether and how corridors can mitigate local habitat losses during rapid climate change, thereby facilitating species adaptation to climate change and maintaining biodiversity and ecosystem services.…”

The rise of novelty in marine ecosystems: The Baltic Sea case

“…Fitzpatrick et al, 2018;Radeloff et al, 2015;. This dissimilarity preserves the signal of individual variables (Williams et al, 2019) while standardizing variables that have different units to the same unit. SED was calculated as follows:…”

“…The baseline also depends on the context of the study, the time scale and the characteristics of throughout the past (e.g. 15,000 years in Burke et al, 2019;and in Finsinger et al, 2017; more than 100 years in Radeloff et al, 2015; and in Williams et al, 2019). The analysis of such long-term datasets increases the likelihood of identifying truly novel conditions (i.e.…”

Quantifying socio‐economic novelty in fisheries social‐ecological systems

“…Yet, the quantification of ecological novelty have been studied in terrestrial ecosystems (e.g. Finsinger et al, 2017;Radeloff et al, 2015;Williams et al, 2019), and more recently in some marine ecosystems (e.g. Ammar et al, 2021).…”