Using Species Distribution Models for Spatial Conservation Planning of African Penguins

Abstract

The African Penguin (Spheniscus demersus) is found on the south-
western coast of Africa, living between Namibia and Algoa Bay, near Port
Elizabeth, South Africa, with the largest colony found on St. Croix Island.
The population is currently at about 14 percent of its 1950s level, when the
first official census was conducted, and is still continuing its strong down-
ward population trajectory. African penguins are an early warning system
for environmental threats, thus studying the factors that affect them is im-
portant. Due to their downward population trajectory, immediate conser-
vation action is required to prevent this species’ extinction from occurring.
The African Penguin is declared as Endangered on the IUCN Red List of
Threatened Species. An understanding of the dynamics and causes for this
decrease is thus of integral importance. Studies indicate drivers of change
include climate change, parasites, pollution (oiling), disease, lack of food
resources, predation risk and habitat interference. A large component of
this is the anthropogenic impact, especially with human population expan-
sion. A cause of this is ecological traps: these are scenarios in which rapid
environmental change leads organisms to prefer to settle in poor quality
habitats. The rate of heterogeneous landscape change may be central to the creation of these traps. Of particular concern are the shifting distributions of forage fish which indicate / result in a spatial mismatch between the
main penguin breeding colonies and their preferred prey. It is important
for conservation purposes to be able to identify these traps and differentiate
them from sinks (a very low quality habitat that, on its own, would not
be able to support a population). A species distribution model (SDM) will
be established in response to these challenges. It is a predictive, concep-
tual model of the abiotic (e.g. physical barriers, climate, lack of resources)
and biotic (e.g. competition, predators, parasites) factors influencing the
role of habitat suitability in controlling species distributions in space, time
and scale. R statistical programming language will be used by implement-
ing the locations of the colonies and incorporating the environmental data.
Other names, such as habitat suitability, niche modelling, bioclimatic mod-
els, resource selection functions and spatial correlation models, are used to
describe such species distribution models. Greater consideration needs to
be given to the information required to assess the consequences of traps at
landscape scales, which are most relevant for conservation management.