Data from: Increasing synergistic effects of habitat destruction and hunting on mammals over three decades in the Gran Chaco

Habitat destruction and overexploitation are the main threats to
biodiversity and where they co-occur, their combined impact is often
larger than their individual one. Yet, detailed knowledge of the spatial
footprints of these threats is lacking, including where they overlap and
how they change over time. These knowledge gaps are real barriers for
effective conservation planning. Here, we develop a novel approach to
reconstruct the individual and combined footprints of both threats over
time. We combine satellite-based land-cover change maps, habitat
suitability models, and hunting pressure models to demonstrate our
approach for the community of larger mammals (48 species >1 kg)
across the 1.1 million km² Gran Chaco region, a global deforestation
hotspot covering parts of Argentina, Bolivia, and Paraguay. This provides
three key insights. First, we find that the footprints of habitat
destruction and hunting pressure expanded considerably between 1985 and
2015, across ~40% of the entire Chaco – twice the area affected by
deforestation. Second, both threats increasingly acted together within the
ranges of larger mammals in the Chaco (17% increase on average, ±20% SD,
cumulative increase of co-occurring threats across 465,000 km2),
suggesting large synergistic effects. Conversely, core areas of
high-quality habitats declined on average by 38%. Third, we identified
remaining priority areas for conservation in the northern and central
Chaco, many of which are outside the protected area network. We also
identify hotspots of high threat impacts in central Paraguay and northern
Argentina, providing a spatial template for threat-specific conservation
action. Overall, our findings suggest increasing synergistic effects
between habitat destruction and hunting pressure in the Chaco, a situation
likely common in many tropical deforestation frontiers. Our work
highlights how threats can be traced in space and time to understand their
individual and combined impact, even in situations where data are sparse.