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The effectiveness and cost of camera traps for surveying small reptiles and critical weight range mammals

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Context Biodiversity studies often require wildlife researchers to survey multiple species across taxonomic classes. To detect terrestrial squamate and mammal species, often multiple labour-intensive survey techniques are required. Camera traps appear to be more effective and cost-efficient than labour-intensive methods for detecting some mammal species. Recent developments have seen camera traps used for detecting terrestrial squamates. However, the performance of camera traps to survey terrestrial squamate and mammal species simultaneously has not been evaluated. Aim We compared the effectiveness and financial cost of a camera trapping method capable of detecting small squamates and mammals with a set of labour-intensive complementary methods, which have been used in a long-term monitoring program. Methods We compared two survey protocols: one employed labour-intensive complementary methods consisting of cage traps, Elliott traps and artificial refuges; the second utilised camera traps. Comparisons were made of the total number of species detected, species detectability, and cost of executing each type of survey. Key results Camera traps detected significantly more target species per transect than the complementary methods used. Although camera traps detected more species of reptile per transect, the difference was not significant. For the initial survey, camera traps were more expensive than the complementary methods employed, but for realistic cost scenarios camera traps were less expensive in the long term. Conclusions Camera traps are more effective and less expensive than the complementary methods used for acquiring incidence data on terrestrial squamate and mammal species. Implications The camera trapping method presented does not require customised equipment; thus, wildlife managers can use existing camera trapping equipment to detect cryptic mammal and squamate species simultaneously.

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Wildlife Research

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