Climate change and zoonotic and vector-borne disease in a net increase in the risk of some zoonotic infections in both wildlife and humans. Other interactions and confounders. In addition to barriers caused by anthropogenic changes, natural barriers to dispersal such as mountain ranges or bodies of water can preclude species from reaching suitable habi- tats. Stronger competitors or predators or the absence of prey or other food sources could prevent species from occupying otherwise suitable habitats. Behavioral adaptation such as changing activity patterns or nesting sites can alter the responses of individuals and populations to climate change. One factor facilitating the northward range expansion of hispid cotton rats has likely been a switch to occupancy of deeper burrows that extend below the frost line (Dunnum 2002). Species with the genetic capacity to undergo evolutionary adaptation to a changing environ- ment can withstand or even benefit from cli- mate change. The photoperiodic response of the pitcher plant mosquito (Wyeomyia smithii) has shifted toward shorter, more southern day lengths in response to an extended growing season, demonstrating an adaptive evolutionary response to global warming in eastern North America (Bradshaw and Holzapfel 2001). The capacity for such adaptation will vary among species, and those that lack adaptive capacity may be forced to migrate in order to survive. Some investigators believe recent increases in malaria incidence in the highlands of East Africa were associated with a warming trend that began in the 1970s (Pascual et al. 2006; Patz 2002); others have linked the increases to other factors, including drug resistance (Hay et al. 2002a, 2002b; Shanks et al. 2002). However, the reality may involve the inter- action of multiple factors. Climate changes do not satisfactorily explain the temporal and spatial patterns of increases in incidence of tick-borne disease in Europe. Other factors likely include demo- graphic changes, increases in deer abundance, and changes in land use after the end of com- munist rule in Eastern Europe (reviewed by Gage et al. 2008). Finally, in this review, we have often used observations of short-term responses of hosts, vectors, and pathogens to climate variability to conjecture about possible responses to long- term climate change. Responses of host, vector, and pathogen populations to temporary— natural or experimental—changes in environ- mental conditions (wet or dry periods, warm or cold periods) are clues to how populations will respond to long-term climate change, but these inferences must be made cautiously. North American deer mice, an opportunis- tic species, quickly respond to increasing moisture in the southwestern United States. Their rapid population growth allows them to escape predators and competitors in the short term. However, if such changes are longer term, competitors will respond to the same changing climatic conditions and predators will adapt to the increased density of prey. The new long-term equilibrium is likely to be much different from what was observed during the initial short-term response. Longer term behavioral, social, and genetic adaptation to the new biotic and abiotic environment will ultimately modify outcomes in ways that may not have been predicted from short-term observations. This important issue, and other caveats for predicting climate change effects on disease, are treated in detail in an earlier review (National Research Council 2001). In summary, host and vector “responses” that are correlated with (or “associated with,” to use our terminology throughout this review) changing climate variables do not prove cause and effect. Potential confound- ing variables including behavioral changes, interspecific interactions, intrinsic population phenomena, anthropogenic factors, and evo- lutionary changes should be considered when appropriate. What Kinds of Studies Will Be Useful? The CDC policy on climate change and pub- lic health (CDC 2010) sets forth 11 priority public health actions that address research, prediction, training, communication, pre- paredness, and prevention to prepare the nation and the world for confronting poten- tial health problems associated with climate change. Using these general priorities, we pro- pose six specific goals that we believe would lead to a better understanding of the inter- action between climate and VBZDs and an improved capability for predicting how cli- mate change will affect the incidence and dis- tribution of VBZDs. Pathogen identification and characteriza- tion. Our knowledge of tropical biota and the pathogens associated with them is inadequate [see Supplemental Material (doi:10.1289/ ehp.0901389)]. Calls for the study of world- wide (and especially tropical) biodiversity have come from a variety of sources [e.g., the United Nations, the U.S. National Science Foundation (NSF), and the Ecological Society of America]. Understanding biodiversity of plants and ani- mals has scientific, economic, and esthetic appeal and has attracted much support (e.g., NSF’s Planetary Biodiversity Inventories pro- gram; NSF 2003). Understanding the diver- sity of tropical pathogens has obvious practical importance yet has received little attention. Although a complete survey of potential patho- gens in nature is an overwhelming task, a useful beginning would be to select the most likely potential hosts and vectors (e.g., bats and rodents, ticks and mosquitoes) and catalog Environmental Health Perspectives • volume 118 | number 11 | November 2010 those groups of pathogens with known pro- pensities for causing disease in humans (e.g., hemorrhagic fever viruses, arboviruses, rickett- siae, and some bacteria). Establish baseline data on the geographic and habitat distribution of recognized zoonotic and vector-borne pathogens and their hosts and vectors. Accurate information on the current distribution of these agents and their hosts and vectors defines current potential disease- endemic areas and helps to quantify relative risk among habitat types. These data also are prerequisite to documenting spatial changes in the distribution of pathogens associated with climate change. Because pathogen dis- tributions do not always coincide with host and vector distributions (Mills and Childs 1998), studies cannot be limited to document- ing host and vector presence or absence but must include sampling hosts and vectors for pathogen presence. Finally, because distinct pathogens may be associated with genetically distinct populations of hosts or vectors (e.g., at the subspecies level) (Mills and Childs 1998), accompanying taxonomic studies will be required. Collection of accurate baseline data is the first step in the establishment of long- term monitoring programs. Establish longitudinal monitoring pro- grams. A major impediment to accurate prediction has been the lack of long-term monitoring programs (Harvell et al. 2002; Jannett et al. 2007; Pascual and Bouma 2009; Sagarin 2002). These studies would track changes in zoonotic disease risk to humans by following changes in population dynam- ics and infection prevalence of a few zoonotic pathogens in their host and vector popula- tions. These studies, whose length should be measured in decades, would monitor changes in environmental variables using field-based and remote-sensing platforms and identify factors associated with changes in host, vector, and pathogen dynamics at selected sites. Long- term sampling should also monitor changes in the distribution of hosts and vector popula- tions and their distribution across latitudi- nal, elevational, and environmental gradients. Although only a few agents, hosts, and vectors can be addressed this way, these studies might be applied to other pathogens, even those yet to be discovered, that have the same or similar host or vector associations. A few long-term databases describing temporal dynamics of animal populations or disease incidence already exist (e.g., data from long-term ecological research sites and records of reportable diseases), and a few more are proposed (e.g., NSF’s National Ecological Observatory Network), but such ecological data were rarely collected for the purposes of infectious disease studies. Conversely, surveil- lance records for human diseases rarely con- tain significant amounts of useful ecological 1511