Santika, T. | bioRxiv Science

bioRxiv2019

The power of mixed survey methodologies for detecting decline of the Bornean orangutan

For many threatened species, it is difficult to assess precisely for large areas the change in their abundances over time and the relative impacts of climate and anthropogenic land use. This is because surveys of such species are typically restricted to small geographic areas, are conducted during short time periods, and use different survey protocols. We assessed the change in the abundance of Bornean orangutan Pongo pygmaeus morio in Sabah, Malaysia, and to identify environmental drivers affecting the change by integrating different types of survey data. We used nest count data obtained from aerial and ground transect surveys and occurrence data obtained from reconnaissance walks and interview survey over the past decade. We built a spatially-explicit dynamic population model within the Bayesian framework allowing these varying survey data to be analyzed jointly by explicitly accounting for each surveys sampling rate. We found that sampling rates vary across survey types, reflecting each surveys associated effort. Orangutan survival rates were strongly determined by natural forest extent and moderately by temperature. Orangutan migration rates across more than 1 km distance between forest patches were low, which underlines the importance of maintaining ecological connectivity. The paucity of species abundance data collected in a consistent manner over many years across broad extents often hinders the assessment of species population trend and their persistence across regional scales. We demonstrate that this can be addressed by integrating multiple survey data across different localities, provided that sampling rate inherent to each survey is accounted for.

ecology↗

bioRxiv2019

The effects of data adequacy and calibration size on the accuracy of presence-only species distribution models

O_LIPresence-only data used to develop species distribution models are often biased towards areas that are frequently surveyed. Furthermore, the size of calibration area with respect to the area covered by the species occurrences has been shown to affect model accuracy. However, existing assessments of the effect of data inadequacy and calibration size on model accuracy have predominately been conducted using empirical studies. These studies can give ambiguous results, since the data used to train and test the model can both be biased.\nC_LIO_LIThese limitations were addressed by applying simulated data to assess how inadequate data coverage and the size of calibration area affect the accuracy of species distribution models generated by MaxEnt and BIOCLIM. The validity of four presence-only performance measures, Contrast Validation Index (CVI), Boyce index, AUC and AUCratio, was also assessed.\nC_LIO_LICVI, AUC and AUCratio ranked the accuracy of univariate models correctly according to the true importance of their defining environmental variable, a desirable property of an accuracy measure. Contrastingly, Boyce index failed to rank the accuracy of univariate models correctly and a high percentage of irrelevant variables produced models with a high Boyce index.\nC_LIO_LIInadequate data coverage and increased calibration area reduced model accuracy by reducing the correct identification of the dominant environmental determinant. BIOCLIM outperformed MaxEnt models in predicting the true distribution of simulated species with a symmetric dominant response. However, MaxEnt outperformed BIOCLIM in predicting the true distribution of simulated species with skew and linear dominant responses. Despite this, the standard performance measures consistently overestimated the performance of MaxEnt models and showed them as always having higher model accuracy than the BIOCLIM models.\nC_LIO_LIIt has been acknowledged that research should be directed towards testing and improving species distribution modelling tools, particularly how to handle the inevitable bias and scarcity of species occurrence data. Simulated data, as demonstrated here, provides a powerful approach to comprehensively test the performance of modelling tools and to disentangle the effects of data properties and modelling options on model accuracy. This may be impossible to achieve using real-world data.\nC_LI

ecology↗