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Data from: Habitat loss in the restricted range of the endemic Ghanaian cichlid Limbochromis robertsi

Citation

Kalacska, Margaret et al. (2020), Data from: Habitat loss in the restricted range of the endemic Ghanaian cichlid Limbochromis robertsi, Dryad, Dataset, https://doi.org/10.5061/dryad.ht76hdrc7

Abstract

Remote sensing has become an integral and invaluable tool to inform biodiversity conservation and monitoring of habitat degradation and restoration over time. Despite the disproportionately high levels of biodiversity loss in freshwater ecosystems worldwide, ichthyofauna are commonly overlooked in favor of other keystone species. Freshwater fish, as indicators of overall aquatic ecosystem health can also be indicators of larger scale problems within an ecosystem. As a case study with multi-temporal, multi-resolution satellite imagery, we examined deforestation and forest fragmentation around the Atewa Forest Reserve, Ghana. Within small creeks, Limbochromis robertsi, a unique freshwater cichlid with an extremely limited distribution range can be found. Historically, the land cover in the area has undergone substantial deforestation for agriculture and artisanal small-scale mining. In the 1389 km2 study area we found deforestation accelerated along with increased forest fragmentation in the 2014 – 2017 period (167.4 km2 of deforestation) with the majority of the forest loss along the river and creek banks due to small-scale mining operations and increased agriculture. Field visits indicated a decrease in the total L. robertsi population by approximately 90% from the early 1990s to 2018. Its distribution has been reduced to higher elevations by anthropogenic habitat barriers at low elevations and the presence of predatory species. Loss of Riparian forest through land use and cover change to mining and agriculture contributes to the habitat degradation for these endemic species. The fine spatial and temporal scale studies required to assess habitat characteristics is not captured by global or continental scale datasets.

Funding

Natural Sciences and Engineering Research Council of Canada