DOI: 10.5061/dryad.280gb5mrq
title: The effect of past defaunation on ranges, niches, and future biodiversity forecasts

description: Humans have reshaped the distribution of biodiversity across the globe,
 extirpating species from regions otherwise suitable and restricting
 populations to a subset of their original ranges. Here, we ask if
 anthropogenic range contractions since the Late Pleistocene led to an
 under‐representation of the realized niches for megafauna, an emblematic
 group of taxa often targeted for restoration actions. Using
 reconstructions of past geographic distributions (i.e., natural
 ranges) for 146 extant terrestrial large‐bodied (&gt;44 kg)
 mammals, we estimate their climatic niches as if they had retained their
 original distributions and evaluate their observed niche dynamics. We
 found that range contractions led to a sizeable under‐representation of
 their realized niches (i.e., niche unfilling). For
 29 species, more than 10% of the environmental space once seen in
 their natural ranges has been lost due to anthropogenic activity, with at
 least 12 species undergoing reductions of more than 50% of their
 realized niches. Eighteen species mainly now be confined to
 low‐suitability locations, where fitness and abundance are likely
 diminished; we consider these taxa climatic refugees. For those
 species, conservation strategies supported by current ranges risk being
 misguided if current, suboptimal habitats are considered baseline for
 future restoration actions. Because most climate‐based biodiversity
 forecasts rely exclusively on current occurrence records, we went on to
 test the effect of neglecting historical information on estimates of
 species’ potential distribution – as a proxy of sensitivity to climate
 change. We found that niche unfilling driven by past range contraction
 leads to an overestimation of sensitivity to future climatic change,
 resulting in 50% higher rates of global extinction, and underestimating
 the potential for megafauna conservation and restoration under future
 climate change. In conclusion, range contractions since the Late
 Pleistocene have also left imprints on megafauna realized climatic niches.
 Therefore, niche truncation driven by defaunation can directly affect
 climate and habitat‐based conservation strategies.
description: To evaluate the effect of
 shifted baselines on estimates of sensitivity to future climate
 change—measured in terms of potential distribution variation—we used
 ecological niche models based on different assumptions. The first scenario
 assumes that species’ distributions are in equilibrium with the
 environmental conditions across their ranges today and uses climatic
 information solely from species’ current ranges to calibrate ecological
 niche models, which we call <em style="padding-left:1px !important;padding-right:1px !important;">current</em>‐<em style="padding-left:1px !important;padding-right:1px !important;">based</em> models. The second scenario relies
 on climatic information from species’ natural ranges, that is, simulating
 a scenario where species have never experienced heavy anthropogenic
 stressors (Faurby & Svenning, 2015). The latter considers that fundamental niches tend to be conserved over time (Peterson, 2011) so that occurrence records from different periods should provide additional information on species’ climatic tolerances from environments without contemporary counterparts (Faurby & Araújo, 2018; Lima‐Ribeiro et al., 2017; Martínez‐Freiría et al., 2016). We name this approach a <em style="padding-left:1px !important;padding-right:1px !important;">natural</em>‐<em style="padding-left:1px !important;padding-right:1px !important;">based</em> model, to adhere to the terminology originally proposed by the authors of the PHYLACINE 1.2.1 dataset (Faurby et al., 2018). For each species, we sampled random points within the species’ current and natural ranges, proportionally to its range size (Table S2), and used ecological niche models to generate potential distribution maps. To do so, we used MaxEnt, a presence‐background method in which the species’ distribution is an unknown probability along with the full background points, that is, non‐negative values that add up to one (Elith et al., 2011). MaxEnt is robust to the presence of a moderate level of locational error and still provides useful predictions of species’ environmental preferences (Graham et al., 2008). The values of predictor variables at localities within natural and current ranges restrict the unknown distributions so that the average and variance values of environmental predictor should be, therefore, close to empirical values (Graham et al., 2008; Merow & Silander, 2014). However, the complexity of the fit to the observed values can be adjusted by transformations on the original predictor values (“feature classes”) (Muscarella et al., 2014). In this work, we compared two combinations of feature classes: (1) MXS: Maxent Simple (only linear and quadratic features); (2) MXD: Maxent Default (linear, quadratic hinge, product, and threshold features, based on MaxNet package) (S. Phillips, 2017; S. J. Phillips et al., 2017). The combination of feature classes exhibiting the highest accuracy metric (<em style="padding-left:1px !important;padding-right:1px !important;">F<sub>pb</sub></em>, calculation explained in Supporting Information section <em style="padding-left:1px !important;padding-right:1px !important;">“Model accuracy assessment”</em>) was then selected for final projections. Because the performance of ecological niche models is affected by the spatial distribution of background points (Barbet‐Massin et al., 2012), we used a stepwise approach to select and partition our background data. First, we defined species‐specific background extents and built a preliminary BIOCLIM (Busby, 1991) habitat suitability model to constrain our background data to regions considered less suitable, that is, suitability &lt;0.3 (Engler et al., 2004). Then, we partitioned our presence‐background data by the latitude and longitude lines that divide occurrence localities into blocks in a checkerboard‐like fashion. Optimal band sizes were considered those that (i) exhibited smaller spatial autocorrelation, based on Moran's I, and (ii) minimized the difference in the number of records between blocks (Roberts et al., 2017; Velazco et al., 2019). Blocks were then alternatively used for fitting and evaluating the model, and evaluation metrics were summarized across iterations (Andrade et al., 2020; Muscarella et al., 2014). Continuous suitability surfaces (values ranging from 0 to 1) were then created to reflect the relationship between species’ occurrences and their environment. Values closer to zero indicate lower predicted suitability, whereas values closer to 1 suggest high environmental suitability. Because we used range maps as a source of environmental information, the outcome of our ecological niche models represents the environmental conditions most frequently observed across species’ known distributional limits. Therefore, our results should not be interpreted in terms of probability of occurrence <em style="padding-left:1px !important;padding-right:1px !important;">per se</em>, nor can be directly translated into any abundance‐related metric. Such broadly defined climate envelopes, however, are meant to provide an initial assessment of species climatic suitability at the continental scale and are useful to investigate macroecological relationships between biotas and environments (Sales et al., 2019, 2020a, 2020b). We stress that our results should not be explored at face value in conservation assessments at local spatial scales. Climate information from current and natural ranges was, therefore, used as input in our ecological niche models and was compared in terms of future potential distribution areas, for each species (model details and parameterization are presented in the Supporting Information section <em style="padding-left:1px !important;padding-right:1px !important;">“Modeling framework”)</em>. To do so, the ecological niches inferred from current‐based and natural‐based models were projected onto climate forecasts, dated to the year 2090 and based on the 6th Assessment Report of the International Panel of Climate Change (IPCC, 2021). Two representative concentration pathways were considered: an optimistic level of emission of greenhouse gases (<em style="padding-left:1px !important;padding-right:1px !important;">ssp126</em>), and a more extreme scenario (<em style="padding-left:1px !important;padding-right:1px !important;">ssp585</em>). While the first scenario represents the “best case” future from a sustainability perspective, with temperature increases of less than 2°C, the latter assumes “no climate policy” and predicts potential increases of almost 5°C in global mean temperature. Both forecasts, therefore, anticipate increases in temperature that are above the 1.5°C thresholds suggested to avoid the negative effects of climate change on global ecosystems and human well‐being (IPCC, 2018). Within each climate change scenario, we chose three distinct climate models to encompass uncertainty on future projections—namely BCC‐CSM2‐MR, CanESM5, and MIROC6—using a stepwise procedure aimed at minimizing similarity (Sanderson et al., 2015). The resulting projections of those three climate forecasts were combined to create a single consensus map of the potential future distribution for each species at each climate change scenario. Only cells that were predicted as suitable by all climate models were included in the final consensus map. Models were evaluated using methods that do not rely on true absence, namely the Jaccard’s (1908) and the Sørensen's (1948) similarity indexes, in addition to the <em style="padding-left:1px !important;padding-right:1px !important;">F<sub>pb</sub></em>, a proxy of the F‐measure based on presence‐background data (Li & Guo, 2013), according to the equations in Table S3. To restrict our analysis to regions likely accessible to species, we further limited access to future suitable areas according to species‐specific dispersal constraints.
description: File List:  1) Current_based_future_binary.rar
 maps of
 species' future potential distribution, calibrated with environmental
 information from species' current ranges (available as
 Current_PHYLACINE.rar) 2) Current_PHYLACINE.rar
 maps of
 species' current realized distribution, obtained from PHYLACINE
 dataset 3) Present_natural_future_binary.rar maps of species'
 future potential distribution, calibrated with environmental information
 from species' present natural ranges (available as
 Present_natural_PHYLACINE.rar)
 4)Present_natural_PHYLACINE.rar maps of species'
 present natural distribution, obtained from PHYLACINE dataset
 Relationship between files:  Present natural ranges depict the
 locations species might have occupied in the present, had they not been
 affected by human presence. Current ranges depict the locations species
 are known to occupy in the present.