Capturing the Contribution of Rare and Common Species to Turnover: A Multi-Site Version of Generalised Dissimilarity Modelling

Post provided by Guillaume Latombe and Melodie A. McGeoch

Understanding how biodiversity is distributed and its relationship with the environment is crucial for conservation assessment. It also helps us to predict impacts of environmental changes and design appropriate management plans. Biodiversity across a network of local sites is typically described using three components:

  1. alpha (α) diversity, the average number of species in each specific site of the study area
  2. beta (β) diversity, the difference in species composition between sites
  3. gamma (γ) diversity, the total number of species in the study area.
Two tawny frogmouths, a species native to Australia. ©Marie Henriksen.

Two tawny frogmouths, a species native to Australia. ©Marie Henriksen.

Despite the many insights provided by the combination of alpha, beta and gamma diversity, the ability to describe species turnover has been limited by the fact that they do not consider more than two sites at a time. For more than two sites, the average beta diversity is typically used (multi-site measures have also been developed, but suffer shortcomings, including difficulties of interpretation). This makes it difficult for researchers to determine the likely environmental drivers of species turnover.

We have developed a new method that combines two pre-existing advances, zeta diversity and generalised dissimilarity modelling (both explained below). Our method allows the differences in the contributions of rare versus common species to be modelled to better understand what drives biodiversity responses to environmental gradients. Continue reading

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A New Modelling Strategy for Conservation Practice? Ensembles of Small Models (ESMS) for Modelling Rare Species

Post provided by FRANK BREINER, ARIEL BERGAMINI, MICHAEL NOBIS and ANTOINE GUISAN

Rare Species and their Protection

Erythronium dens-canis L. – a rare and threatened species used for modelling in Switzerland. ©Michael Nobis

Erythronium dens-canis L. – a rare and threatened species used for modelling in Switzerland. ©Michael Nobis

Rare species can be important for ecosystem functioning and there is also a high intrinsic interest to protect them as they are often the most original and unique components of local biodiversity. However, rare species are usually those most threatened with extinction.

In order to help prioritizing conservation efforts, the International Union for Conservation of Nature (IUCN) has published criteria to categorize the status of threatened species, which are then published in Red Lists. Changes in a species’ geographical distribution is one of the several criteria used to assign a threat status. For rare species, however, the exact distribution is often inadequately known. In conservation science, Species Distribution Models (SDMs) have recurrently been used to estimate the potential distribution of rare or insufficiently sampled species. Continue reading

What is Dark Diversity?

Post provided by ROB LEWIS & MEELIS PÄRTEL

Our understanding of how biological diversity works has been advanced by a long history of observing species and linking patterns to ecological processes. However, we generally don’t focus as much on those species that aren’t observed, or in other words ‘absent species’. But, can absent species provide valuable information?

Dark diversity – a set of species absent from a particular site but which belong to its species pool – has the potential to be as ecologically meaningful as observed diversity. Part of the species pool concept, understanding dark diversity is relatively straightforward.

The Basic Theory of Dark Diversity

To begin learning about dark diversity, there are two important terms that we need to define: ‘species pool’ and ‘focal community’. A ‘species pool’ is a set of species present in a particular region or landscape that can potentially inhabit a particular observed community because of suitable local ecological conditions.

A ‘focal community’ is the set of species that have been observed in a particular region or landscape (this is the ‘observed community’ and can also be referred to as alpha diversity). For a given focal community to become established, the species within it must have overcome dispersal pressures as well as environmental and biotic filters.

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Continue reading

Spatially-explicit Power Analysis: A First Step for Occupancy-Based Monitoring

Post provided by Martha Ellis and Jody Tucker

Where’s Waldo? Trying to find this fisher somewhere in a giant landscape is going to be tricky! ©Mike Schwartz

Where’s Waldo? Trying to find this little guy somewhere in a giant landscape is going to be tricky! © Mike Schwartz

The seemingly basic question of whether a population is increasing, decreasing, or stable can be one of the most difficult to answer. Collecting data on rare and elusive species is hard. Imagine trying to detect a handful of fisher or wolverine across hundreds of thousands of acres – it is physically demanding, time consuming and logistically complicated. And that’s just to do it once! To monitor a population for changes, you have to repeat these surveys regularly over many years. The long-term monitoring that is necessary for conservation requires careful planning and a substantial commitment of resources and funding. So before we spend these valuable resources, it’s critical to know whether the data we are collecting can help us to answer our questions. Continue reading

A Model Approach to Weed Management

Post provided by VANESSA ADAMS

Vanessa Adams in the field with gamba grass in the Batchelor region, NT. ©Amy Kimber (NERP Northern Australia Hub)

Vanessa Adams in the field with gamba grass in the Batchelor region, NT.
©Amy Kimber (NERP Northern Australia Hub)

Invasive weeds cause environmental and economic harm around the world. Land managers bear a heavy responsibility for the control of infestations in what is often a time-consuming and costly battle.

Fortunately, an increasing number of research-based solutions are giving land managers an advantage. This includes tools to determine the distribution of weeds and also the development of modelling approaches to predict their spread.

Understanding the current and future distribution of an invasive species allows managers to better direct their limited resources. However, the direct and strategic management of weeds is tricky and that’s why population models (in particular spatial dispersal models that can be applied without much data) are needed to inform and facilitate action on the ground. Continue reading

Being Certain about Uncertainty: Can We Trust Data from Citizen Science Programs?

Post provided by VIVIANA RUIZ GUTIERREZ

Citizen Science: A Growing Field

Thousands of volunteers around the world work on Citizen Science projects. ©GlacierNPS

Thousands of volunteers around the world work on Citizen Science projects. ©GlacierNPS

As you read this, thousands of volunteers of all ages and backgrounds are collecting information for over 1,100 citizen science projects worldwide. These projects cover a broad range of topics: from volunteers collecting samples of the microbes in their digestive tracts, to tourists providing images of endangered species (such as tigers) that are often costly to survey.

The popularity of citizen science initiatives has been increasing exponentially in the past decade, and the wealth of knowledge being contributed is overwhelming. For example, almost 300,000 participants have submitted around 300 million bird observations from 252 countries worldwide to the eBird program since 2002. Amazingly, rates of submissions have exceeded 9.5 million observations in a single month! Continue reading

Demography and Big Data

Post provided by BRITTANY TELLER, KRISTIN HULVEY and ELISE GORNISH

Follow Brittany (@BRITTZINATOR) and Elise (@RESTORECAL) on Twitter

To understand how species survive in nature, demographers pair field-collected life history data on survival, growth and reproduction with statistical inference. Demographic approaches have significantly contributed to our understanding of population biology, invasive species dynamics, community ecology, evolutionary biology and much more.

As ecologists begin to ask questions about demography at broader spatial and temporal scales and collect data at higher resolutions, demographic analyses and new statistical methods are likely to shed even more light on important ecological mechanisms.

Population Processes

Midsummer Opuntia cactus in eastern Idaho, USA. © B. Teller.

Midsummer Opuntia cactus in eastern Idaho, USA. © B. Teller.

Traditionally, demographers collect life history data on species in the field under one or more environmental conditions. This approach has significantly improved our understanding of basic biological processes. For example, rosette size is a significant predictor of survival for plants like wild teasel (Werner 1975 – links to all articles are at the end of the post), and desert annual plants hedge their bets against poor years by optimizing germination strategies (Gremer & Venable 2014).

Demographers also include temporal and spatial variability in their models to help make realistic predictions of population dynamics. We now know that temporal variability in carrying capacity dramatically improves population growth rates for perennial grasses and provides a better fit to data than models with varying growth rates because of this (Fowler & Pease 2010). Moreover, spatial heterogeneity and environmental stochasticity have similar consequences for plant populations (Crone 2016). Continue reading

On the Tail of Reintroduced Canada Lynx: Leveraging Archival Telemetry Data to Model Animal Movement

Post provided by FRANCES E. BUDERMAN

Animal Movement

218 Canada lynx were reintroduced to the San Juan Mountains between 1999 and 2006 with VHF/Argos collars. © Colorado Parks and Wildlife

218 Canada lynx were reintroduced to the San Juan Mountains between 1999 and 2006 with VHF/Argos collars. © Colorado Parks and Wildlife

Animal movement is a driving factor underlying many ecological processes including disease transmission, extinction risk and range shifts. Understanding why, when and how animals traverse a landscape can provide much needed information for landscape-level conservation and management practices.

The theoretical underpinnings for modelling animal movement were developed about seventy years ago. Technological developments followed, with radio-collars initially deployed on large mammals such as grizzly bears and elk. We can now monitor animal movement of a wide variety of species, including those as small as a honeybee, at an unprecedented temporal and spatial scale.

However, location-based data sets are often time consuming and costly to collect. For many species, especially those that are rare and elusive, pre-existing data sets may be the only viable data source to inform management decisions. Continue reading

How Did We Get Here From There? A Brief History of Evolving Integral Projection Models

Post provided by MARK REES and Steve Ellner

The Early Days: Illyrian Thistle and IBMs

Illyrian Thistle

Illyrian Thistle

Back in 1997 MR was awarded a travel grant from CSIRO to visit Andy Sheppard in Canberra. CSIRO had been collecting detailed long-term demographic data on several plant species and Andy was keen to develop data-driven models for management.

Andy decided Illyrian thistle (Onopordum Illyricum) would be a good place to start, as this was the most complicated in terms of its demography. The field study provided information on size, age and seed production. The initial goal was to quantify the impact of seed feeders on plant abundance, but after a few weeks of data analysis it became apparent that the annual seed production per quadrat was huge (in the 1000s) but there were always ~20 or so recruits. This meant that effects of seed feeders (if any) occurred outside the range of the data, which wasn’t ideal for quantitative prediction.

So the project developed in a different direction. Onopordum is a monocarpic perennial (it lives for several years then flowers and dies) and Tom de Jong and Peter Klinkhamer had recently developed models to predict at what size or age monocarps should flower, so it seemed reasonable to see if this would work. Continue reading

Models, Practical Management and Invasive Critters

POST PROVIDED BY NICK BEETON

How Simple Should a Model Be?

Should scientists make simplifying assumptions in complex models? This is a debate as old as the hills, and one that everyone seems to have strong opinions about. Some argue that because even the most simplistic model based on the best available estimates is objective, it is better than relying solely on “gut feelings”. In such a model, estimates based on expert opinion or simplifying assumptions can at least be included in a transparent fashion. Others argue that such an approach can miss important emergent properties as a result of missed complexity, making any results misleading and potentially even worse than not using a model at all.

Models to Support Management: Invasive Horses, Cats and Deer

Wild horses in the Australian alps. © Regina Magierowski

Wild horses in the Australian Alps. © Regina Magierowski

Both sides are right in their own way, of course, but perhaps unusually (as an applied mathematics graduate working in ecology), I’ve found myself leaning towards the former view as my career progresses. During my last postdoc, I was confronted with a large, vexing problem: the incursion of wild horses in the Australian Alps. The species was already impacting bogs and wetlands, overpopulated in some places to the point of starvation, and spreading to previously pristine areas of National Park. The issue was (and still is) highly contentious, with activists applying considerable political pressure against lethal forms of control. Knowledge of population densities across the horses’ range was patchy and ability to predict their likely movements equally unreliable. Even predicting their demographics was difficult, with most values for population growth rates conflicting and spatially variable. Continue reading