Ebola, the Bubonic plague, the Spanish flu. These are some of the most devastating contagious diseases life on Earth. Are all plagues made equal? Do all harmful pathogens result in the decimation of the host population? Laura Grogan and her team set out to examine just that in their Biological Conservation article: “Is disease a major causal factor in declines? An Evidence Framework and Case Study on koala chlamydiosis”.
Preece’s Framework is an older, alternative model that uses temporal limitations, spatial limitations, and risk factors to classify pathogens as a driving force for biotic deaths.2 Grogan creates the Evidence Framework (Fig. 1), an expansion of Preece’s model, by studying the effects of chlamydiosis, a koala-infecting strain of the sexually transmitted disease chlamydia, on koala populations in southeastern Australia.
“Evidence Framework for Identifying disease-associated wildlife declines” by Laura F. Grogan is licensed under CC 2.0
Grogan claims that “determining the role of an infectious agent in contributing to wildlife population declines is a pervasive problem in conservation biology.” Before delving into the koala chlamydiosis investigation, Grogan debunks common misconceptions on pathogen biology: (1) the detection of an infectious agent is not necessarily correlated with population impact, (2) the prevalence of an infection is not correlated with population impact, and (3) diseases do not impact populations only through mortality.1 Grogan also addresses some of the challenges conservation biology encounters with pathogens: gaps of knowledge, lack of protocols, limited resources, and time-delayed response.1 Taking all of this into consideration, Grogan proposed her Evidence Framework (Fig. 1). To be sure, this model does not effectively address all of the challenges mentioned in her article; however, Grogan’s Evidence Framework is the most detailed and comprehensive model available.
The Evidence Framework separates diseases into two categories: “Individual Host” and “Host Population”. For a pathogen to achieve “driving force” status by significantly contributing to population decline, the pathogen must have at least one of the effects on Fig. 1 for both scales with sufficient supporting data.
As empirical support for the Evidence Framework, Grogan and her team researched the impact of chlamydiosis on Phascolarctos cinereus in southeast Queensland, Australia. They found that fertility rates among diseased populations significantly decreased compared with healthy populations (~65% to ~30%). Additionally, Grogan’s team found that the disease was associated with higher mortality rates, though they recognized that it was hard to track this given the fast decomposition of animal carcasses. Given this evidence, Grogan and her team concluded that chlamydiosis does not significantly alter koala populations, acknowledging that this characterization was mostly due to a lack of data; in other words, there was not a sufficient amount of data to support the hypothesis of chlamydiosis infections impacting koala populations.
Grogan’s Evidence Framework is a guide in navigating a challenge she pointed out intrinsic to the field of conservation biology: gaps in knowledge. Her Evidence Framework directs future avenues of research by highlighting what data must be gathered to properly characterize a pathogen as a significant contributor to host population decline. With more evidence, Grogan states, chlamydiosis may be proven to impact the koala population. Grogan suggests directing future research on koala recruitment rates, the rate at which organisms join a population.
Grogan and her team’s work has major implications for the future of conservation biology by revolutionizing the manner in which characterizations of pathogens are made. In contrast to earlier, more flexible models, Grogan’s Evidence Framework lays out detailed, explicit guidelines for the characterization of pathogens. This framework elucidates the misconception that all harmful pathogens significantly contribute to host population decline. If the structure for this model is translated to other ongoing conservation biology research and education, definitions will be clearer, misconceptions will be debunked, and knowledge gaps will close.