Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.

To address the ongoing debate over the impact of invasive species on native terrestrial wildlife, we conducted a large-scale experiment to test the hypothesis that invasive Burmese pythons (Python molurus bivittatus) were a cause of the precipitous decline of mammals in Everglades National Park (ENP). Evidence linking pythons to mammal declines has been indirect and there are reasons to question whether pythons, or any predator, could have caused the precipitous declines seen across a range of mammalian functional groups. Experimentally manipulating marsh rabbits, we found that pythons accounted for 77% of rabbit mortalities within 11 months of their translocation to ENP and that python predation appeared to preclude the persistence of rabbit populations in ENP. On control sites, outside of the park, no rabbits were killed by pythons and 71% of attributable marsh rabbit mortalities were classified as mammal predations. Burmese pythons pose a serious threat to the faunal communities and ecological functioning of the Greater Everglades Ecosystem, which will probably spread as python populations expand their range.

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Camera traps have increased our knowledge of animal distribution, activity, and behavior, but they are rarely used for small mammal research. This is likely because there are few techniques to that allow for species identification, reduce disturbance of bait from non‐target animals (e.g., raccoon [Procyon lotor]), and that can be used in all environments. In this paper we present a small mammal camera‐trapping methodology, the Hunt trap, which was designed to 1) work in tidal environments, 2) eliminate capture myopathy, 3) allow for successful identification of small mammal species, and 4) allow for continued trapping after disturbance by non‐target species. We tested the Hunt trap in the Lower Suwannee National Wildlife Refuge, Florida, USA, during February 2012 to February 2013. Live traps are still the best option when individuals must be physically captured for marking, radiotagging, demographic studies, or physiological assessments. However, if such data are not required, the Hunt trap design is an excellent technique to monitor species diversity, community composition, habitat selection, and distribution with efficiency and minimal effort. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

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