An overview DR MONIQUE MACKENZIE & PROF NORMAN

An overview DR MONIQUE MACKENZIE & PROF NORMAN

An overview DR MONIQUE MACKENZIE & PROF NORMAN OWEN-SMITH CENTRE FOR RESEARCH INTO ECOLOGICAL AND ENVIRONMENTAL MODELLING (CREEM) SENIOR LECTURER IN MATHEMATICS AND STATISTICS UNIVERSITY OF ST ANDREWS, UK The next few minutes Objectives of the meeting Ideal outcomes (hopes and dreams)

Some ideas about how to achieve these outcomes Possible next actions Main objective: Using tracking data to inform conservation and management: What tag data is being collected? Spatial/temporal coverage How

can we use this tagging data to assist conservation and/or wildlife management? Ideal outcomes: Better collaboration Understanding the current situation Quantifying changes looking forward (either climate

based or as a result of conservation-based activities) Identify possible funding avenues Better collaboration Creatures cross boundaries The effects of climate change require us to look far and wide

It can be more efficient e.g. combining predator and prey abundance and distribution maps Greater insights based on shared information & better decisions Understanding the current situation How many creatures do we have in an area of interest?

What are the key characteristics of the population? Age-structure, health etc Where are the animals found? Are there any hotspots? Why are they found, where they are found? Key drivers for distribution Understanding the current situation

Where are the animals not found? Are there any exclusion areas? What are the effects of any fences/boundaries? What would we expect to see if there were no boundaries present?

Compare the current reality with what wed expect to see without boundaries. How can we use what we have to conserve a species? E.g. an early warning system for poaching using vultures. Quantifying the effects of any changes going forward Quantifying changes in: Distribution

Abundance Home range Overlap with other creatures (including humans) Fitted surface for divers in Phase 2. The plot on the left indicates predicted numbers for a 1km x 1km grid while the plot on the right represents the same values with the observed data for this phase overlaid. The sizes of the circles are in proportion to the observed counts in each location and the solid black circles represent knot locations. Fitted surface for the Greater Wash for divers in Phase 4. The plot on the left indicates predicted numbers for a 1km x 1km grid while the plot on the right represents the same values with the observed data for this phase overlaid. The sizes of the circles are in proportion to the observed counts in each

location and the solid black circles represent knot locations. Quantifying the effects of any changes going forward Assessing Site the power to detect change: wide changes (e.g. overall decline) Localised changes/redistribution (e.g. lower numbers at a disturbance site Use

what we have as reality Impose change scenarios Whats the chance we detect change when its there? Some ideas about how to achieve these outcomes: Collaboration Strategic planning via a group representing all stakeholders:

Policy makers, industry, scientists, community representatives What do we know? Where are the gaps? Prioritise Sharing filling these gaps & identifying funders data: commonly agreed format

Some ideas about how to achieve these outcomes: Understanding the current situation Data on the right spatial and temporal scales Collecting covariate information (or getting it somehow) Using methods which are fit-for-purpose Telemetry data is repeated measures data (many observations on relatively few individuals) and the analysis methods must account for the correlated

nature of the data. Some ideas about how to achieve these outcomes: Understanding the current situation Using methods which are fit-for-purpose: Pretending your data are sets of independent points can lead you to make dodgy conclusions. You are likely to conclude that covariate relationships/effects are genuine when they are not. You

are likely to conclude that change has occurred when it has not. Boundaries should be respected and explicitly included in the analysis approach to avoid `leakage across boundaries. This would also enable fence effects to be quantified. Some ideas about how to achieve these outcomes: Quantifying change Quantifying changes using trusted/standardised methods:

Distribution, Abundance, Home range etc Translating results to stakeholders Linking shifts in distributional patterns with behaviour to make meaningful conclusions: What were they doing when they were there?

What are they doing now? What are the population consequences of this change? Some ideas about how to achieve these outcomes: Quantifying change Given the data/model available, what is the power to detect change? Site

wide changes in abundance(e.g. overall decline) Localised changes/redistribution. Shifts in species distribution and abundance. Possible next actions Form/co-opt a well composed group and make a plan

Think critically about any model-based results: Does your analysis pass the sniff test? Help the decision makers by listing the important things to check Training and/or collaboration Can the results form case studies for use in universities/schools?

Masters student projects, PhD projects, postdoctoral researchers Possible next actions Quantify the power to detect change based on what we know -- do we have sufficient data? Convert the satellite tracking data into conservation activities

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