Spatial Cross-Validation of Species Distribution Models in R: Introducing the blockCV Package

Post provided by Roozbeh Valavi

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Modelling species distributions involves relating a set of species occurrences to relevant environmental variables. An important step in this process is assessing how good your model is at figuring out where your target species is. We generally do this by evaluating the predictions made for a set of locations that aren’t included in the model fitting process (the ‘testing points’).

Random splitting of the species occurrence data into training and testing points

Random splitting of the species occurrence data into training and testing points

The normal, practical advice people give about this suggests that, for reliable validation, the testing points should be independent of the points used to train the model. But, truly independent data are often not available. Instead, modellers usually split their data into a training set (for model fitting) and a testing set (for model validation), and this can be done to produce multiple splits (e.g. for cross-validation). The splitting is typically done randomly. So testing points sometimes end up located close to training points. You can see this in the figure to the right: the testing points are in red and training points are in blue. But, could this cause any problem? Continue reading

اعتبارسنجی متقاطع مکانی در مدلسازی توزیع گونه‌‌ها

نویسنده: روزبه وَلَوی

This post is available in English

مدلسازی توزیع گونه‌ها به تخمین و برآورد ارتباط بین مجموعه‌ای از نقاط حضور گونه با متغیرهای زیست‌محیطی مرتبط می پردازد. یکی از مراحل اساسی این فرایند، ارزیابی قدرت مدل برای پیش­بینی مکان‌هایی است که احتمال حضورگونه در آنجا وجود دارد. این کار اغلب با ارزیابی پیش­بینی انجام شده در مجموعه‌ای ازنقاط که در فرآیند مدلسازی مورد استفاده قرار نگرفته اند (نقاط آزمایشی) صورت می‌گیرد.

تقسیم تصادفی داده‌های حضور گونه به نقاط آزمایشی و آموزشی

تقسیم تصادفی داده‌های حضور گونه به نقاط آزمایشی و آموزشی

مطالعات پیشین بر این نکته تاکید دارند که به منظور ارزیابی معتبر، نقاط آزمایشی باید مستقل از نقاط آموزشی باشند، این درحالیست که داده مستقل واقعی به ندرت در دسترس می باشد. به همین دلیل، در فرایند مدلسازی معمولا داده‌های موجود را به دو قسمت داده‌های آموزشی (برای کالیبره کردن مدل) و داده های آزمایشی (برای ارزیابی دقت مدل) تقسیم می‌کنند، این استراتژی می‌تواند چند قسمتی هم باشد (برای مثال اعتبارسنجی متقاطع یا cross-validation). از آنجاییکه این تقسیم بندی معمولا بصورت تصادفی انجام می‌شود، بنابراین گاهی اوقات نقاط آزمایشی در فواصل نزدیک به نقاط آموزشی قرار می‌گیرند. شکل زیر این مساله را به خوبی نشان می دهد که در آن نقاط آزمایشی به رنگ قرمز و نقاط آموزشی آبی هستند. اما آیا این مساله می‌تواند مشکلی ایجاد کند؟ Continue reading

How do You Know that the Top Dog is Really the Top Dog? Using Elo-Ratings and Bayesian Inference to Determine Rankings in Animal Groups

Post provided by Julia Fischer

A female chacma baboon (rear) signals her submission to another female by raising her tail. ©Julia Fischer.

A female chacma baboon (rear) signals her submission to another female by raising her tail. ©Julia Fischer.

Anyone who studies social animals in the wild (or human groups, for that matter), will soon find that some individuals threaten or attack others frequently, while others try to get out of the way or signal their submission in response to aggression. Observers tally the outcome of such aggressive interactions between any given two individuals (or ‘dyads’) and try to deduce the rank hierarchy from such winner-loser matrices. One drawback of this approach is that all temporal information is lost.

Imagine Royal, a baboon, dominating over Power, another baboon, 20 times, and Power dominating over Royal 20 times as well. If we just look at these data, we might think that they have the same fighting ability and similar ranks. But, if we know that Royal beat Power the first 20 of the interactions, then Power beat Royal in all further interactions, we’d come to a totally different conclusion. We’d infer that Power had toppled Royal and a rank change had taken place.

How do Rank Hierarchies Change Over Time?

One prominent method that takes the temporal dynamics of winner-loser interactions into account was originally developed to calculate the relative skill level of chess players. This method was introduced by Arpad Elo and is hence known as Elo-Rating. Elo-Rating has also been applied to rate the relative skills in a variety of competitive fields, including Major League Baseball, video games, and Scrabble. Continue reading

The Manager’s Dilemma: Which Species to Monitor?

Post provided by Payal Bal and Jonathan Rhodes

The greater bilby (M.Lagotis). ©Save the Bilby Fund

The greater bilby (M.Lagotis). ©Save the Bilby Fund

Imagine you’re the manager of a national park. One that’s rich in endemic biodiversity found nowhere else on the planet. It’s under the influence of multiple human pressures causing irreversible declines in the biodiversity, possibly even leading to the extinction of some of the species. You’re working with a complex system of multiple species and threats, limited knowledge of which threats are causing the biggest declines and limited resources. How do you decide what course of action to take to conserve the biodiversity of the park? This is the dilemma faced by biodiversity managers across the globe.

In our recent paper, ‘Quantifying the value of monitoring species in multi‐species, multi‐threat systems’, we address this problem and propose a method using value of information (VOI) analysis. VOI estimates the benefit of monitoring for management decision-making. Specifically, it’s a valuation tool that can be used to disentangle the trade-offs in competing monitoring actions. It helps managers decide how to invest (or whether to invest) their money in monitoring actions when faced with imminent biodiversity declines and the urgency of efficient conservation action. Continue reading

Statistical Ecology Virtual Issue

To celebrate the International Statistical Ecology Conference and British Ecological Society Quantitative Ecology Annual Meeting, Laura Graham and Susan Jarvis have compiled a virtual issue celebrating all things statistical and quantitative in ecology.

Statistical and quantitative methods within ecology have increased substantially in recent years. This rise can be attributed both to the growing need to address global environmental change issues, as well as the increase in data sources to address these challenges. Continue reading

Fourier Methods Gain Wide Appeal for Tropical Phenology Analysis

Post provided by Emma Bush

Lopé National Park. ©Jeremy Cusack

Lopé National Park. ©Jeremy Cusack

Like all living things, plant species must reproduce to persist. Key stages in successful plant reproduction must be carefully timed to make sure resources are available and conditions are optimal. There will be little success if flowers mature in bad weather conditions for their insect pollinators or if fruits ripen but the seed dispersers have migrated elsewhere.

Because plants rely on the abiotic environment for sunlight, nutrients and water, and in some cases for the dispersal of pollen and seeds, it is not surprising that their life stages are closely linked to environmental cycles. Continue reading

Can Opportunistically Collected Citizen Science Data Create Reliable Habitat Suitability Models for Less Common Species?

Post provided by Ute Bradter, Mari Jönsson and Tord Snäll

Detta blogginlägget är tillgängligt på svenska

Opportunistically collected species observation data, or citizen science data, are increasingly available. Importantly, they’re also becoming available for regions of the world and species for which few other data are available, and they may be able to fill a data gap.

Siberian jay ©Ute Bradter

Siberian jay ©Ute Bradter

In Sweden, over 60 million citizen science observations have been collected – an impressive number given that Sweden has a population of about 10 million people and that the Swedish Species Observation System, Artportalen, was created in 2000. For bird-watchers (or plant, fungi, or other animal enthusiasts), this is a good website to bookmark. It will give you a bit of help in finding species and as a bonus, has a lot of pretty pictures of interesting species. Given the amount of data citizen science can provide in areas with few other data, it’s important to evaluate whether they can be used reliably to answer questions in applied ecology or conservation. Continue reading

Kan medborgarnas opportunistiskt insamlade data användas för artutbredningsmodeller av mindre vanliga arter?

Bloginlägg av Ute Bradter, Mari Jönsson och Tord Snäll

This blog post is available in English

Opportunistiskt insamlade artobservationer av frivilliga, så kallade medborgarforskningsdata, blir alltmer tillgängliga. Dessa data har potentialen att fylla ett databehov för olika regioner i världen och arter för vilka få andra data är tillgängliga.

Siberian jay ©Ute Bradter

Lavskrika ©Ute Bradter

I Sverige har över 60 miljoner artobservationer samlats in av frivilliga i Artportalen – ett imponerande antal med tanke på att Sverige har en befolkning på cirka 10 miljoner människor och att webbplatsen endast har funnits sedan år 2000. För fågelskådare (eller växt-, svamp-, andra djurentusiaster), är Artportalen en bra hemsida att bokmärka om man vill ha lite hjälp med att hitta arter eller tycker om att titta på vackra bilder på arter. Globalt samlas ett stort antal sådana uppgifter för artförekomst i Global Biodiversity Information Facility. Med tanke på den mängd data som medborgarforskare kan tillhandahålla för områden med få andra data är det viktigt att utvärdera om de kan användas för att tillförlitligt besvara frågor inom grundläggande ekologi eller naturvård. Continue reading

Monitoring the Distribution and Abundance of Sea Otters

Post provided by Perry Williams

Sea otters (Enhydra lutris) are an apex predator of the nearshore marine ecosystem – the narrow band between terrestrial and oceanic habitat. During the commercial maritime fur trade in the 18th and 19th centuries, sea otters were nearly hunted to extinction across their range in the North Pacific Ocean. By 1911, only a handful of small isolated populations remained.

Sea otters resting in Glacier Bay National Park. © Jamie Womble, NPS. USFWS Permit #14762C-0, NPS Permit #GLBA-2016- SCI-0022.

Sea otters resting in Glacier Bay National Park. © Jamie Womble, NPS. USFWS Permit #14762C-0, NPS Permit #GLBA-2016- SCI-0022.

But sea otter populations have recovered in many areas due to a few changes. The International Fur Seal Treaty in 1911 and the Marine Mammal Protection Act (1972) protected sea otters from most human harvest. Wildlife agencies helped sea otter colonisation by transferring them to unoccupied areas. Eventually, sea otters began to increase in abundance and distribution, and they made their way to Glacier Bay, a tidewater glacier fjord and National Park in southeastern Alaska. Continue reading

Learn to be a Reviewer: Peer Reviewer Mentoring Scheme

Today is the first day of peer review week. One of the issues that many people bring up about the current system of peer review is that there is very little formal training. There are guidance documents available (including the BES Guide to Peer Review), workshops on peer review can be found at some conferences and some senior academics teach their PhD students or post-docs about the process. In general though, peer review training is fairly hard to come by.

This is something that people have told us (the BES publications team) at conferences and through surveys, so we’re doing something about it. From October 2017 until April 2018 Methods in Ecology and Evolution is going to be partnering with the BES Quantitative Ecology Special Interest Group to run a trial Peer Review Mentoring Scheme.

The trial scheme is going to focus on statistical ecology (as we receive a lot of statistical papers at Methods in Ecology and Evolution), but if it goes well, we’ll be looking at other areas of expertise too.

Applications for Mentor and Mentee positions are now open. If you’re an experienced statistical ecologist or evolutionary biologist or an Early Career Researcher in those fields, we’d love to receive an application from you. Continue reading