Random population fluctuations bias the Living Planet Index

The Living Planet Index (LPI) is a standardized indicator for tracking population trends through time. Due to its ability to aggregate many time series in a single metric, the LPI has been proposed as an indicator for the Convention on Biological Diversity’s post-2020 Global Biodiversity Strategy. However, here we show that random population fluctuations introduce biases when calculating the LPI. By combining simulated and empirical data, we show how random fluctuations lead to a declining LPI even when overall population trends are stable and imprecise estimates of the LPI when populations increase or decrease nonlinearly. We applied randomization null models that demonstrate how random fluctuations exaggerate declines in the global LPI by 9.6%. Our results confirm substantial declines in the LPI but highlight sources of uncertainty in quantitative estimates. Randomization null models are useful for presenting uncertainty around indicators of progress towards international biodiversity targets.DATA AVAILABILITY: Empirical data of population time series in the Living Planet database are available from the dedicated website maintained by the Zoological Society of London (ZSL) (http://stats.livingplanetindex.org/) and are subject to the Data Use Policy by the Indicators & Assessments Unit at the ZSL and WWF International. Simulated data to replicate the results are available from https://doi.org/10.5281/zenodo.4744533.CODE AVAILABILITY : All simulation outputs and code (R scripts) to reproduce the results in this manuscript are available from https://doi.org/10.5281/zenodo.4744533.EXTENDED DATA FIG. 1: The nine steps to calculating the Living Planet Index (LPI).EXTENDED DATA FIG. 2: The Living Planet Index (LPI) for randomly fluctuating populations that are stable on average.EXTENDED DATA FIG. 3: Starting population sizes of time series added to the Living Planet Index have declined between 1950 and 2015.EXTENDED DATA FIG. 4: Larger population fluctuations cause less precise estimates of the Living Planet Index (LPI) in nonlinear population trajectories.EXTENDED DATA FIG 5: Population fluctuations cause generalised additive models (GAM) to misestimate starting and ending populations when populations decrease from 100 to 40 individuals.EXTENDED DATA FIG 6: Population fluctuations cause generalised additive models (GAM) to misestimate starting and ending populations when populations increase from 100 to 160 individuals.EXTENDED DATA FIG 7: The reshuffling null model used to account for random population fluctuations.EXTENDED DATA FIG. 8: Cumulative population declines can occur in the Living Planet Index even when average population declines are zero.EXTENDED DATA FIG. 9: Cumulative population changes represent empirical trajectories more accurately than average changes as time series lengths increase.The National Research Foundation of South Africa and the Jennifer Ward Oppenheimer Research Grant.https://www.nature.com/natecolevolhj2022Zoology and Entomolog

Reply to: Capturing stochasticity properly is key to understanding the nuances of the Living Planet Index

DATA AVAILABILITY : Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.REPLYING TO E. J. Talis & H. J. Lynch Nature Ecology & Evolution https://doi.org/ 10.1038/s41559-023-0286-w (2023)http://www.nature.com/natecolevolhj2024Zoology and EntomologyNon

The processes underlying continental-scale biodiversity patterns - Mechanisms at the interface of ecology and biogeography

For more than a century, naturalists have wondered why species occur where they do. Yet despite this persistent attention, identifying the mechanisms underlying contemporary biodiversity patterns remains a challenge and, as a consequence, a general theory of biodiversity is not forthcoming. The research presented in this thesis aimed at examining the importance of deterministic environment-driven processes in shaping the geographical distributions of individual vertebrate species and the aggregated properties of multi-species assemblages. To do this my co-authors and I used a macroecological approach to scrutinise diversity patterns in sub-Saharan Africa and applied a suite of mechanistic and statistical modelling techniques to distil evidence for deterministic causal processes. Furthermore, we also tested the ability of alternative mechanisms in explaining diversity patterns that have traditionally been attributed to climate mechanisms. Overall, we failed to find convincing evidence for environmental determinism at the continent-scale. We scrutinised an array of biological patterns and tested several alternative ecological and evolutionary mechanisms and all of our findings were within one of four categories. First, patterns consistent with environmental determinism - such as correlations between species' geographical ranges and climate gradients - arose even in the absence of environmental mechanisms. This weakens evidence for environmental determinism because we were unable to falsify alternative non-climate mechanisms such as continuous range expansion from a single point of origin. Second, evidence for environmental determinism could account for one biological pattern, but not for another equally relevant one. For example, we found that range expansion along continuous climate gradients could explain local, but not range-wide, patterns of species co-occurrence. This raises doubts around the ubiquity of climate-driven ecological mechanisms. Third, analytical tools used to identify environmental determinism at one spatial scale were inappropriate for use at a different scale. This is particularly relevant for macroecology, which regularly borrows analytical tools from smaller-scale ecological studies. As such, patterns could be misattributed to climate-driven ecological mechanisms in instances where analyses were carried out at unsuitable spatial scales. Lastly, in cases where non-climate mechanisms (specifically, the mid-domain effects hypothesis) have been tested and rejected, it is inappropriate to attribute these patterns to environmental determinism by default. Instead, it is equally likely that the original non-climate mechanism was poorly formulated with unrealistic assumptions.It was not our intention to discredit existing evidence for environmental determinism nor do we believe that the environment plays no role in shaping the distribution of life on earth. Instead, this study demonstrates the fragility of current macroecological knowledge and suggests reasons why strong evidence for environmental determinism is so hardto obtain. Ultimately, our findings allowed us to formulate several strategies for strengthening our understanding of ecological and evolutional processes at large spatial scales to, hopefully, build towards a general global theory of biodiversity.status: publishe

A camera trap survey of nocturnal mammals on former farmland in the eastern Free State Province, South Africa, 10 years after removing livestock

<p>This archive contains the data and R scripts used for the following study:</p> <p>Buschke, F.T.(unpublished). A camera trap survey of nocturnal mammals on former farmland in the eastern Free State Province, South Africa, 10 years after removing livestock</p> <p>A written description of the research methodology can be obtained from the manuscript. Please consult the README.txt file for a detailed outline of all the files in this archive.</p> <p>Any comments or inquiries can be directed to the author, Falko Buschke ([email protected])</p> <p> </p

Analysing the assemblage dispersion field

<p>This archive contains the data and R scripts used for the following study:</p> <p>Buschke, F.T., Brendonck, L. & Vanschoenwinkel (2015). Simple mechanistic models can partially explain local but not range-wide co-occurrence of African mammals. Global Ecology and Biogeography doi: 10.1111/geb.12316</p> <p>Please be sure to read the README.txt file first, before attempting to use these data.</p> <p>A written description of the research methodology can be obtained from the manuscript.</p> <p>Any comments or inquiries can be directed to the lead author, Falko Buschke ([email protected])</p> <p> </p

African Water Symposium 2015

<p>This is the conference booklet for the 2nd African Water Syposium held in Bloemfontein, South Africa, on 7-8 October 2015.</p> <p>It includes the program and abstracts for all the contributed sessions</p

R-scripts for all Neutral simulations

The following repository contains R code for a ecological simulation of biodiversity offsets using neutral theory. Please note that this code only contains the backbone algorithms for the neutral simulation, and has been shortened for illustrative purposes (i.e. the time series is shorter, there are fewer iterations, and the code only simulates the outputs for one combination of parameters). Running this code will take approximately 3-5 minutes of computation time depending on your machine. There is a reporter that counts the number of iterations (Note: this counter does not report the duration of the time-series). The complete simulation as presented in the manuscript took several hours and was run on several cloud-based RStudio servers using Amazon Web Services

Differences between regional and biogeographic species pools highlight the need for multi-scale theories in macroecology

Ecologists are intrigued by the manner in which colonists from a regional pool of species establish and structure local ecological communities. This has initiated several approaches to identifying the relative roles of regional and local processes. Recently, large-scale data sets and novel statistical tools have sparked renewed interest in objectively defined homogeneous species pools. At continental and global scales, these homogenous units are known as biogeographic species pools. Here we argue that the biogeographic species pool is not just a scaled-up version of the regional species pool featured in many foundational ecological theories. Instead, the processes linking local communities and regional species pools differ from those in the biogeographic species pool. To illustrate this, we distinguish between regional and biogeographic species pools by overlaying species distribution data and differentiat- ing between the intersection and union of these distributions. Although patterns in the regional and biogeographic species pools may appear self-similar across scales, the underlying mechanisms differ from those between local communities and the regional species pool. As a consequence, conventional approaches of quantifying the relative role of local and regional process are inappropriate for studying the biogeographic species pool, thus highlighting the need for new multi-scale theories in macroecology.</p