Selective extinctions resulting from random habitat destruction lead to under‐estimates of local and regional biodiversity loss in a manipulative field experiment

Land-use change is a significant cause of anthropogenic extinctions, which
are likely to continue and accelerate as habitat conversion proceeds in
most biomes. One way to understand the effects of habitat loss on
biodiversity is through improved tools for predicting the number and
identity of species losses in response to habitat loss. There are
relatively few methods for predicting extinctions and even fewer
opportunities for rigorously assessing the quality of these predictions.
In this paper we address these issues by applying a new method based on
rarefaction to predict species losses after random, but aggregated,
habitat loss. We compare predictions from three rarefaction models,
individual-based, sample-based, and spatially-clustered, to those derived
from a commonly-used extinction estimation method, the Species-Area
Relationship (SAR). We apply each method to a mesocosm experiment, in
which we aim to predict species richness and extinctions of arthropods
immediately following 50% habitat loss. While each model produced
strikingly accurate predictions of species richness immediately after the
habitat loss disturbance, each model significantly underestimated the
number of extinctions occurring at both the local (within-mesocosm) and
regional (treatment-wide) scales. Despite the stochastic nature of our
small-scale, short-term, and randomly applied habitat loss experiment, we
found surprisingly clear evidence for extinction selectivity, for example
when abundant species with low extinction probabilities were extirpated
following habitat loss. The important role played by selective extinction
even in this contrived experimental system suggests that ecologically
driven, trait-based extinctions play an equally important role to
stochastic extinction, even when the disturbance itself has no clear
selectivity. As a result, neutrally stochastic null models such as the SAR
and rarefaction are likely to underestimate extinctions caused by habitat
loss. Nevertheless, given the difficulty of predicting extinctions, null
models provide useful benchmarks for conservation planning by providing
minimum estimates and probabilities of species extinctions.