Females of all blood-feeding arthropod vectors must find and feed on a host in order to produce offspring. For tsetse—vectors of the trypanosomes that cause human and animal African trypanosomiasis—the problem is more extreme, since both sexes feed solely on blood. Host location is thus essential both for survival and reproduction. Host population density should therefore be an important driver of population dynamics for haematophagous insects, and particularly for tsetse, but the role of host density is poorly understood. We investigate the issue using data on changes in numbers of tsetse (Glossina morsitans morsitans Westwood) caught during a host elimination experiment in Zimbabwe in the 1960s. During the experiment, numbers of flies caught declined by 95%. We aimed to assess whether models including starvation-dependent mortality could explain observed changes in tsetse numbers as host density declined. An ordinary differential equation model, including starvation-dependent mortality, captured the initial dynamics of the observed tsetse population. However, whereas small numbers of tsetse were caught throughout the host elimination exercise, the modelled population went extinct. Results of a spatially explicit agent-based model suggest that this discrepancy could be explained by immigration of tsetse into the experimental plot. Variation in host density, as a result of natural and anthropogenic factors, may influence tsetse population dynamics in space and time. This has implications for Trypanosoma brucei rhodesiense transmission. Increased tsetse mortality as a consequence of low host density may decrease trypanosome transmission, but hungrier flies may be more inclined to bite humans, thereby increasing the risk of transmission to humans. Our model provides a way of exploring the role of host density on tsetse population dynamics and could be incorporated into models of trypanosome transmission dynamics to better understand how spatio-temporal variation in host density impacts trypanosome prevalence in mammalian hosts.

Community structure is determined by the interplay among different processes, including biotic interactions, abiotic filtering and dispersal. Their effects can be detected by comparing observed patterns of co-occurrence between different species (e.g. C-score and the natural metric) to patterns generated by null models based on permutations of species-by-site matrices under constraints on row or column sums. These comparisons enable us to detect significant signals of species association or dissociation, from which the type of biotic interactions between species (e.g. facilitative or antagonistic) can be inferred. Commonly used patterns are based on the levels of co-occurrence between randomly paired species. The level of co-occurrence for three or more species is rarely considered, ignoring the potential existence of functional guilds or motifs composed of multiple species within the community. Null model tests that do not consider multi-species co-occurrence could therefore generate false negatives (Type II error) in detecting non-random forces at play that would only be apparent for such guilds. Here, we propose a multi-species co-occurrence index (hereafter, joint occupancy) that measures the number of sites jointly occupied by multiple species simultaneously, of which the pairwise metric of co-occurrence is a special case. Using this joint occupancy index along with standard permutation algorithms for null model testing, we illustrate nine archetypes of multi-species co-occurrence and explore how frequent they are in the seminal database of 289 species-by-site community matrices published by Atmar and Patterson in 1995. We show that null model testing using pairwise co-occurrence metrics could indeed lead to severe Type II errors in one specific archetype, accounting for 2.4% of the tested community matrices.