Assessing the utility of statistical adjustments for imperfect detection in tropical conservation science

Abstract

1. In recent years, there has been a fast development of models that adjust for imperfect detection. These models have revolutionized the analysis of field data, and their use has repeatedly demonstrated the importance of sampling design and data quality. There are, however, several practical limitations associated with the use of detectability models which restrict their relevance to tropical conservation science. 2. We outline the main advantages of detectability models, before examining their limitations associated with their applicability to the analysis of tropical communities, rare species and large-scale data sets. Finally, we discuss whether detection probability needs to be controlled before and/or after data collection. 3. Models that adjust for imperfect detection allow ecologists to assess data quality by estimating uncertainty and to obtain adjusted ecological estimates of populations and communities. Importantly, these models have allowed informed decisions to be made about the conservation and management of target species. 4. Data requirements for obtaining unadjusted estimates are substantially lower than for detectability-adjusted estimates, which require relatively high detection/recapture probabilities and a number of repeated surveys at each location. These requirements can be difficult to meet in large-scale environmental studies where high levels of spatial replication are needed, or in the tropics where communities are composed of many naturally rare species. However, while imperfect detection can only be adjusted statistically, covariates of detection probability can also be controlled through study design. Using three study cases where we controlled for covariates of detection probability through sampling design, we show that the variation in unadjusted ecological estimates from nearly 100 species was qualitatively the same as that obtained from adjusted estimates. Finally, we discuss that the decision as to whether one should control for covariates of detection probability through study design or statistical analyses should be dependent on study objectives. 5.Synthesis and applications. Models that adjust for imperfect detection are an important part of an ecologist's toolkit, but they should not be uniformly adopted in all studies. Ecologists should never let the constraints of models dictate which questions should be pursued or how the data should be analysed, and detectability models are no exception. We argue for pluralism in scientific methods, particularly where cost-effective applied ecological science is needed to inform conservation policy at a range of different scales and in many different systems.

Keywords: biodiversity conservation; capture–recapture models; detectability; detection probability; imperfect detection; monitoring; occupancy models; species richness.

Figure 1

Predicted values (±CI) of the unadjusted estimates of occurrence and occupancy of 30 agroforests for nine large mammal species as a function of domestic dog capture rate. Values calculated using the best fit occupancy model for each species (see Appendix S2 and Tables S1 and S2 in Supporting Information).

Figure 2

Monthly population sizes of three Atlantic Forest small mammals estimated by closed population estimates (circles, solid line, ±95% confidence intervals) and unadjusted estimate (MNKA; triangles, spotted line). Pearson's correlation coefficient (r) and probability of significance for each species are given in the upper right corner. For Marmosops incanus, abundance could not be estimated in November and December 2008 as well as in October 2009 due to low capture probabilities (see Appendix S2 and Tables S3, S4 and S5 in Supporting Information).

Figure 3

Bias of unadjusted estimate, defined as the absolute difference from P‐adjusted estimates (i.e. occupancy), increases at lower levels of detection probability (P < ~0·15). Data points represent species‐specific bias estimates calculated in each of the four forest cover treatments (10, 30, 50 and 90%), and thus, there are in total 336 data points (84 species × 4 forest cover treatments). Previous inspection of the data showed that the trend of increase in bias did not vary among landscapes (see Appendix S2 and Table S6 in Supporting Information).