ARMI Philosophy on the Role of Monitoring: It’s not a “Stand-alone” Activity. Our goal is to develop a monitoring program that considers how the data can be used to understand how amphibians respond to changes occurring on the landscape or in response to management even over long periods of time. These data can be combined with detailed experiments and habitat or species management, which allows ARMI scientists to learn how amphibians are doing in a more comprehensive manner.
Before starting a monitoring project, we answer the 3 fundamental questions of an informative monitoring program: Why are we monitoring; What are we monitoring; and How will we design the project?
Designing a monitoring program about amphibians had a unique set of challenges. ARMI wanted a monitoring program that would provide information about the status of amphibians while simultaneously providing data on their ecology. There are approximately 287 species of amphibians in the United States whose ecology is as diverse as the habitat types they occupy. Further, amphibians have some characteristics that make them difficult to survey. For example: 1) some species are cryptic, even fossorial, and available for detection during short periods of time or in brief periods of the year; 2) several species are difficult to distinguish even when in hand, and 3) no salamanders call, and not all frogs and toads call.
The sampling technique, the design of the monitoring plan, and data interpretation should be congruent with the life history of the species being surveyed and the questions being asked. Examples of characteristics that make designing and interpreting survey data of amphibians difficult include: 1) many species are explosive breeders; 2) many species are “unavailable for sampling” during times when it is too cold, hot or dry for them to be active; 3) some species suffer local short-term extinctions and large fluctuations in population size as a normal part of their life history; 4) some sites (e.g., desert pools) do not exist every year; and 5) some amphibian habitat patches are obviously discrete (e.g., desert pools), while some are not (e.g., Atchafalaya Basin?).
Each of the ARMI Regional Principal Investigators (PI) is responsible for estimating the status of amphibians on a set of lands within a multi-state area. Each PI develops monitoring projects with its own goals and sampling methods. All of the PIs conduct monitoring on the state of a set of amphibian species on an ongoing basis to estimate the proportion of area that is occupied by the species of interest.
Even though we are a national program, we don’t collect the same data across all of the regions for several reasons. Because we are using a model-based monitoring approach, each scientist can identify the factors believed to be primarily responsible for driving the populations in the areas to which the sampling is making inference. These factors (i.e.,covariates), are specific to the region, ecology, and history of the species being surveyed. For example, the impact of repeated fires may be an important covariate affecting populations of toads in the Intermountain West, but proximity to urbanization may be more influential to amphibians in the Northeastern U.S. However, there are 2 factors which are common to all of ARMI monitoring: 1) we use a probability based sampling design, and 2) we estimate detection probability.
It is common for investigators to fail to locate every individual animal or every species during a survey. Unless the raw data, such as counts of individuals or species, and occupancy are adjusted for missed detections, the inferences drawn from the data can be misleading. The results will be biased towards those individuals or species which are easier to detect. This can result in biased conclusions and inappropriate management decisions.
However, it is also problematic to develop a single “correction factor” for missed detections and apply it to the species each time it is surveyed. Applying a fixed “correction factor” assumes that time, place, observer; or conditions which influence the probability of detecting individuals never changes, which is extremely unlikely. It is better to estimate detection directly. ARMI has shown how naïve data (i.e., detection not estimated) can result in misleading conclusions in several studies (Grant et al. 2005, Mattfeld and Grant 2007).
The development of new quantitative tools has enabled ARMI scientists to increase the types of questions they can ask. Recent developments have allowed ARMI to handle data with missing observations, spatial correlations, and interactions among multiple species. By developing some of these advances and applying others as they become available, ARMI scientists are in the forefront of quantitative research on amphibians.