Micro Level Design Tools
Why use the Micro Design? To achieve sufficient detections of individual animals and recaptures; to reduce individual ID errors; to effectively analyze your data and reliably assess animal density.
Camera Trap Sampling
How to choose where to setup camera traps (survey design)?
- This is the Micro-level survey design It provides advice on selecting locations at a micro level to guide efficient camera trap design
- By Dr. Ian Durbach, University of St Andrews, Scotland
How to set up camera traps?
- Here we share best practices for camera setup in terms of alignments, heights, monitoring, installation and recovery to optimize resources at the time of data analysis.
- By Dr. Koustubh Sharma, Snow Leopard Trust/GSLEP
Good practices for estimating snow leopard population abundance
- The goal of this document is to provide guidelines for designing camera trap surveys whose overall goal is estimating the total number and density of snow leopard populations. The key principle behind planning your camera trap survey is that we will be using spatial capture-recapture (SCR) methods to estimate snow leopard numbers and densities.
- Dr. Ian Durbach, University of St Andrews; Dr. Koustubh Sharma, Snow Leopard Trust/GSLEP; Dr. Justine Shanti Alexander Snow Leopard Trust/Snow Leopard Network.
Genetic Sampling
Fecal swab sampling protocol
- This illustrative manual shares how to collect genetic data from fecal swabs.
- By the Ramakrishanan Lab, National Centre for Biological Sciences (ncbs)
Scat sampling protocol
- This illustrative manual provides useful approaches to identify locations to cover during transects, best practices for DNA collection kits, protocols for storage and precautions for handling samples.
- By Charlotte Hacker, Duquesne University; Imogene Cancellare, University of Delaware, Panthera; Dr. Byron Weckworth, Panthera; Dr. Jan E.Janecka, Duquesne University
DNA extraction and processing for Species ID
- This manual provides detailed advice on the lab procedures for DNA extraction and processing for Species ID.
- By Charlotte Hacker, Duquesne University; Imogene Cancellare, University of Delaware, Panthera; Dr. Byron Weckworth, Panthera; Dr. Jan E.Janecka, Duquesne University
Data Organization and Analysis
How to extract snow leopard images using Microsoft AI tool
- A machine learning model developed by Microsoft engineers can process snow leopard images and automatically classify hundreds of thousands of photos as snow leopards in a matter of minutes. Microsoft engineers built the model with deep neural networks,
- By Microsoft (Please contact paws@globalsnowleopard.org for more information)
Best practices for Individual ID
- Snow leopards can be mis-identified as their spot patterns may not be easily recognized when their thick fur gets ruffled or when their body is photographed at different angles. This document provides recommendations to improve individual identification of snow leopards from camera trap images.
- By Dr. Justine Shanti Alexander, Snow Leopard Trust/Snow Leopard Network; Dr. Orjan Johansson, Snow Leopard Trust, Dr. Koustubh Sharma, Global Snow Leopard & Ecosystem Protection Program; Dr. Ian Durbach, University of St Andrews; Dr. David Borchers, University of St Andrews.
Data analysis with SECR
- SECR surveys use detectors at fixed locations to record the presence of individually identifiable animals at those locations. Detectors can be camera-traps, hair snares and scat The R package secr provides methods for estimating animal abundance from SECR data under many different conditions. This sheet summarizes getting your data into the format secr wants
- By Dr. Ian Durbach, University of St Andrews, Scotland