Habitat loss and fragmentation are major threats to primate populations globally. The Endangered golden monkey Cercopithecus mitis kandti is only found in two small forest fragments: the Virunga massif in Rwanda, Uganda and the Democratic Republic of the Congo, and the Gishwati Forest in Rwanda. Little is known about the abundance and distribution of this subspecies, or threats to its survival. During 2007–2018, we collected data along 893.7 km of line transects and 354.2 km of recce trails in Volcanoes National Park and in Gishwati–Mukura National Park to estimate golden monkey density and examine any threats. In Volcanoes National Park, golden monkeys were found almost exclusively in the bamboo zone, and in Gishwati–Mukura National Park they occurred only in the remnant tropical montane Gishwati Forest. In Volcanoes National Park, density was estimated to be 7.89 (95% CI: 3.85–16.19), 5.41 (2.64–11.08), and 5.47 (3.68–8.14) groups per km2 in 2007, 2011 and 2017–2018, respectively. This corresponds to a total of 4,331 individuals (95% CI: 2,723–5,938) in 2007, 4,487 (2,903–6,071) in 2011 and 4,626 (4,165–5,088) in 2017–2018. In Gishwati Forest, group density averaged 1.98 (95% CI: 1.27–3.16) per km2, corresponding to 172 (95% CI: 154–190) individuals in 2017–2018. Survey results from Volcanoes National Park suggest that the golden monkey population has been stable during 2007–2018. Limited habitat, illegal activities such as harvesting of bamboo and firewood, and the presence of feral dogs, threaten the golden monkey in Rwanda and require continued monitoring. The development of a conservation action plan is a priority to protect this species.

Species distribution models (SDMs) are statistical tools used to develop continuous predictions of species occurrence. ‘Integrated SDMs’ (ISDMs) are an elaboration of this approach with potential advantages that allow for the dual use of opportunistically collected presence-only data and site-occupancy data from planned surveys. These models also account for survey bias and imperfect detection through the use of a hierarchical modelling framework that separately estimates the species–environment response and detection process. This is particularly helpful for conservation applications and predictions for rare species, where data are often limited and prediction errors may have significant management consequences. Despite this potential importance, ISDMs remain largely untested under a variety of scenarios. We performed an exploration of key modelling decisions and assumptions on an ISDM using the endangered Baird’s tapir (Tapirus bairdii) as a test species. We found that site area had the strongest effect on the magnitude of population estimates and underlying intensity surface and was driven by estimates of model intercepts. Selecting a site area that accounted for the individual movements of the species within an average home range led to population estimates that coincided with expert estimates. ISDMs that do not account for the individual movements of species will likely lead to less accurate estimates of species intensity (number of individuals per unit area) and thus overall population estimates. This bias could be severe and highly detrimental to conservation actions if uninformed ISDMs are used to estimate global populations of threatened and data-deficient species, particularly those that lack natural history and movement information. However, the ISDM was consistently the most accurate model compared to other approaches, which demonstrates the importance of this new modelling framework and the ability to combine opportunistic data with systematic survey data. Thus, we recommend researchers use ISDMs with conservative movement information when estimating population sizes of rare and data-deficient species. ISDMs could be improved by using a similar parameterization to spatial capture–recapture models that explicitly incorporate animal movement as a model parameter, which would further remove the need for spatial subsampling prior to implementation.

We study a point process model with stochastic intensities for a particular branching population of individuals of two types. Type-I individuals immigrate into the population at the times of a Poisson process. During their lives they generate type-II individuals according to a random age dependent birth rate, which themselves may multiply and die. Living type-II descendants increase the death intensity of their type-I ancestor, and conversely, the multiplication and dying intensities of type-II individuals may depend on the life situation of their type-I ancestor. We show that the probability generating function of the marginal distribution of a type-I individual's life process, conditioned on its individual infection and death risk, satisfies an initial value problem of a partial differential equation, and derive its solution. This allows for the determination of additional distributions of observable random variables as well as for describing the complete population process.