I’m Michael Dietze, ecologist researching how to make near-term nature forecasts similar to weather forecasts. Ask me anything about how short-term environment forecasts will help us understand, manage ＆ conserve ecosystems.

You use the terms "short-term" and "near-term" - what are the scales of these terms both temporally and spatially? Since a weather forecast is pretty unreliable more than 5 days out for a given region, it seems that an ecological forecast would have an even larger problem with reliability at a scale smaller than months for 100s of square miles.

Great question. First, from the perspective of using forecasts to advance our scientific understanding, we use "near-term" to refer to timescales where we can regularly test our predictions against new data. In some cases these are forecasts that are produced every day like a weather forecast. Others might be weekly, monthly, seasonal, or even annual. But the term is meant to distinguish these forecast from long-term projections (e.g. climate responses in 2100).
Second, your point about the reliability of the weather forecasts is a great one, as those forecasts are indeed a key input into most ecological forecasts. In some cases the weather forecast uncertainties do dominate the uncertainties in our ecological forecasts, which puts a limit on the time which the forecast remains useful. Still, in many cases such short-term forecasts can still be useful to managers, decision makers, and the public (just how weather forecasts are also useful on short scales). This class of forecasts will improve as weather forecasts improve -- indeed, subseasonal to seasonal (S2S) forecasting is a major priority in Earth System predictability research. The other interesting thing is that because many ecological processes integrate over weather variability, ecological forecasts can sometimes be more accurate than the weather forecasts that go into them. As an example, it might not matter to a plant whether it rains today, tomorrow, or this weekend so long as there's enough rain over recent days to weeks that they don't become stressed.

In your experience, how do ecological forecasts affect the food supply? Right now, we're seeing an increase in prices at the grocery store. How can accurate forecasting help curb these increases?

So ecological forecasts can be used to predict crop growth out into the future, which helps farmers anticipate yields, and how they vary from year to year. Importantly, they allow us to anticipant plant stressors (e.g. drought, pests) and implement management options, aiming to do so in ways that are more precise, better timed, and ultimately more cost effectively. At larger scales, forecasts create opportunities for the larger food system to respond to these anticipated changes in yield in different sectors and geographic areas

Hey Michael!

I think this is the first time I've ever caught an AMA in time for questions let alone one I share common interests/goals with.

I pay a good bit of my attention to phenology in my local area, when certain species of wildflowers bloom, what mushrooms are fruiting, what birds are migrating, and more recently to local fishes behavior from weather and seasonal changes.

This year we had what locals call a "Dogwood Winter" where a hard snow occurred in late April after a warm few weeks. Seeing the flowering trees under a layer of snow was beautiful and unforgettable. Such a shame I didn't get the opportunity to document it more thoroughly! Mammals not in hibernation, resident birds, and early migrators flocked to our feeders. I lost one of my bee colonies and the two others had difficult starts. I also noticed that flowers and mushrooms in our area have been thrown off by ~2-3 weeks this year depending on the local environment and fewer insects overall.

While events like these do happen historically, could they become more frequent as climate change continues to affect us? If there are more unusual and chaotic weather events could it render these predictive capabilities less effective or ineffective entirely? We would find these tools to be most useful here in Appalachia, so we really appreciate your work.

Thanks,

An Ohio River Boy

So you raise a good point that extreme weather events that outside the historical norm (and thus outside the data we use to train our forecasting models) are always going to be a challenge. This is compounded in situations where the weather forecasts for these events are also uncertain. So in that case HOW a forecast model is constructed (which may not be easy to figure out from a website) has a lot to do with how much trust I'd put into it's predictions in these cases. In general, the more a forecast represents our underlying mechanistic understanding of processes, the more likely it is going to be able to extrapolate successfully into new conditions. So for your example of phenology, there's a long tradition of using simple models that just accumulated "warming" or "cooling" which are regrettably not great at distinguishing gradual warming/cooling from extreme events. But I have a graduate student, Kathryn Wheeler, who's been working on forecasts of leaf out and leaf fall whose taken a deeper dive into the physiological mechanisms (e.g. chlorophyll synthesis [creation] and degradation [break down]) and has some new models in the works that we're really excited about. She's currently in the field making measurements as fall hits us here in New England, so fingers crossed 🤞 this Fall's forecast will show improvements.

I'm someone who is currently getting a phd in ecology and thinking about forecasting as part of my future career. Do you expect the field to move more towards having specialized jobs that are purely forecasting and interpretation (like a meteorologist compared to an academic climate researcher)? Would this be beneficial, and if so, how would you facilitate those sorts of jobs?

I do think that the field is heading in that direction -- I've already seen a few academic and non-academic jobs ads that explicitly mention forecasting -- but it could still be a while before forecasting specifically becomes mainstream. That said, I do think that the larger field of environmental data science is growing rapidly, with many universities beginning to offer programs in this area and an uptick in jobs looking for these more broad quantitative skills. Anyone trained in ecological forecasting would be well prepared for these jobs and I suspect anyone hired to do environmental data science will find that forecasting will become a larger and larger part of their job going into the future.

In terms of facilitating job, I'll note that the Ecological Forecasting Initiative ecoforcast.org has a #jobs board in its community Slack. We're not posting those to our website yet (maybe we should!) but right now I'm seeing a lot of jobs being posted that want to hire people from our community relative to the number of folks that are available to fill them.

Is there any new technique or research method that can be used to help predict natural disasters, like wildfires and earthquakes? And what's the biggest challenge you're facing in this process of developing near-term environmental forecasts?

So earthquakes are outside of what I think about, but I've definitely thought a lot about wildfire and other similar ecosystem disturbances (pests and pathogens, windthrow, ice damage, invasive species). Ecological forecasts can help with these in multiple ways. First we can forecast the disturbance itself -- for example the Randerson Lab at UC Irvine has a long-lead fire severity outlook https://www.ess.uci.edu/~amazonfirerisk/ForecastWeb/SAMFSS2021.html which can be used to help with advanced planning (e.g. getting resources and crews in the right regions ahead of time). There's also a whole bunch of folks out there forecasting individual fire events once they've started, which at that point is more physics than ecology, but a critical input into those fire forecasts are inputs on fuel loads and fuel moisture, which are things that ecological forecasts do work on. And then on the other side, after a disturbance event, there are a number of us working on forecasts of ecosystem recovery, which aim to make restoration efforts more successful and cost effective.

But to follow up about the biggest challenges, I tend to think about these at a large scale (across different forecasts projects rather than the challenges to individual predictions) and I think three biggest challenges are data available, human resources (increasing the number and diversity of people with sufficient training in this area) and computational resources (in particular, building shared community cyberinfrastructure that will make building and operationalizing forecasts easier and less expensive for individual projects)

Short term eco-forecasting sounds like a win win to me. Are any town, state or federal government personnel currently following and or requesting your forecasts?

So both NOAA and NASA have ecological forecasting programs in place, the former producing predictions of things like algal blooms, coral bleaching, and fisheries by-catch, and the latter largely working with academics and other stakeholders (including states and NGOs) to incorporate remote sensing into ecological forecasts. The National Phenology Network, which has been supported by the USGS, also produces a range of forecasts and we've had discussions with them about adopting some of the models we've developed and iterative learning approaches we use. There are also lots of other similar examples from other groups -- for example Virginia Tech's Smart Reservoir project https://smartreservoir.org/ is working with local reservoir managers on a range of lake-level forecasts.

Right from the start I feel your research, if shown to be accurate and successful, could help the masses feel more connected to nature due to the "instant feedback" it could provide.

We all know biodiversity loss is bad, but people have a hard time connecting with animals going extinct in exotic lands. I could see your predictions having a much more empathizing effect due to their near-term and potentially local predictions. Conservation potential of this is huge, in theory.

You don't have to even forecast species loss in order to help foster a connection between people and nature. Even predictions about infectious disease and natural disasters will help people understand the undeniable tie between ecosystem health and human health.

What are your thoughts on this?

I think this is a great point. I had the fortunate opportunity to speak to the Massachusetts Environmental Education Society a couple years ago and there was a lot of excitement and ideas among environmental educators both in the classroom and in non-traditional settings (nature centers, museums, etc) about how forecasts could be used to teach basic ecological concepts. There was also a lot of excitement about connecting forecasts to citizen science projects like iNaturalist.org, because you could see the data you collected being put to use that same day. Indeed, we've looked into pulling iNaturalist data into our tick and small mammal forecasts to be able to scale them out in space and make them more locally accurate.

You say you are researching how to make near-term nature forecasts, have anyone tried it before or are you the first one to attempt it?

I'm definitely not the only person working in this area. But one of the things I've been working hard on is helping to bring this small community together into an emerging discipline. Working with others in this area we launched the Ecological Forecasting Initiative ecoforcast.org in 2018 to help build a community of practice that spans the many subdisciplines where folks are working on forecasts (e.g. land, freshwater, marine; biologists, earth scientists, social scientists, computational scientists). That webpage includes a directory of some example forecast research projects throughout the community. We've also been working hard to create conferences, workshops, educational opportunities & materials, and shared tools

There are a variety of weather derived indices for things such as allergies, golf conditions, heating, cooling, etc You can also get forecasts of these indices, whats the difference in what you're proposing?

So I think there's a lot of similarities to what we're proposing and some of those indices, but in most cases rather than simple indices we're looking to make quantitative predictions of specific natural resources, species, etc that are based on our understanding of the ecology of the systems in question and calibrated extensively against field data. So, for example, my team produces forecasts of things like tick populations, lake algal blooms, and fall colors. We also do a lot of work specifically around ecosystem carbon sequestration and the requirements for monitoring, reporting, and verification

What specific graduate programs should I be looking into if this is a field I would like to pursue?

I've worked for the private energy sector for nearly 10 years, and my background is a BS in Physics from a top ranked state school. I'm eager to transition my skills into the environmental/ecological space, and hope that I can play some part in mitigating the effects we're already beginning to see.

Depends on whether you're looking at the MS or PhD level. For most PhD programs in ecology or other environmental sciences I generally recommend figuring out who is doing the cool research in the area you want to work, and then figure out where they're located and whether that program would be a good fit for you. I'll note that the Ecological Forecasting Initiative ecoforcast.org maintains a searchable members directory and a listing of some of the forecasting projects in the community (but there's always more out there than we've had a chance to pull in), as well as a community Slack that's helpful for connecting with people. For MS you're more applying to a program than a lab. There aren't ecological forecasting MS programs yet, but there are a number of schools that are well known for their environmental management MS programs (e.g. Duke, Yale), and environmental data science programs emerging all the time (Virginia Tech, Northern Arizona University, etc).

Excellent - really appreciate you taking the time out to give a detailed response.

You may hear again from me soon, Dr. Dietze!

I was also reminded of this resource EFI just produced about ecological forecasting careers and courses https://qubeshub.org/publications/2756/1

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https://twitter.com/mcdietze/status/1443604264354525195

Do you think human made climate change can be stopped by economic investments into climate friendly companys?

By that alone? No, I think it's going to require substantial national and international legislation and investment of resources into decarbonizing the economy. That said, I definitely prefer to give my money to responsible companies and do think that can demonstrate that demand is there and the technology is ready (e.g. I remember when politicians used to say electric cars were impossible and consumers would never want them, but Tesla changed that)

Not sure if I'm still in time here (I'll preface by saying I'm in the UK).

I have a masters degree in History but since leaving university and starting work I've realised I have a real passion and desire to work in ecology, in particular anything involving entomology. What paths, if any, would I have to retrain towards this? Without the option to go back to university for an extended period, is this even possible at this point?

Thanks!

Well, if you did want to go back to school at the Masters-level, a switch from History to Entomology wouldn't be unheard of. And even just taking an odd class or two to prepare might help you understand if that's what you really want to do or not. Beyond that, I'll admit that it's hard for me to address what type of entry-level jobs are available in the UK in the environmental sciences and how competitive they are i.e. you'll have a harder time finding one if the market is already flooded with folks who have degrees in that area and you don't, but on the flip side if there's unmet demand you might find someone willing to take a chance on you and train you on the job. I'd say it's definitely worth taking a deeper look into what sorts of jobs are available in your area. Here a lot of our students end up in government agencies, NGOs, environmental consulting firms, in environmental industries (e.g. solar) or in the environmental impact/sustainability initiatives in larger companies.

Any tips to figure out what the odds of large snowfall are ahead of a given winter season? It’s been minimal for a while but always fun when you get a big dump of snow.

Outside my core area (this is more traditional weather forecasting), but I'd look at NOAA's seasonal outlook https://www.cpc.ncep.noaa.gov/products/predictions/90day/

Thanks for the AMA!

I am in the drought ridden American West where wildfires are accelerating desertification.

How would a short term ecology forecast be managed after a long term devastation like a wildfire? Is there a protocol for blending forecasts to empower local communities?

So I think there are a lot of important opportunities to develop more ecological forecasts around predicting restoration success, for example, when is the best time to do restoration operations (e.g. planting) and where are they most needed vs where will systems recover on their own. The USGS has started doing this in the southwest and we've got a bit of this included in some new projects we're working on (e.g. forest pest recovery)

Did you grow up in Germany (what with your totally German name)?

New Jersey, but my family lineage is German

Is machine learning plays a great part in your research?

So ecological forecasts approach prediction using a range of different approaches. Sometimes this is machine learning, sometimes its simpler statistical models, sometimes its more complex demographic models (e.g. keeping track of different size and age classes of organisms), and sometimes its through highly sophisticated and mechanistic Earth System models. Interestingly, because of the computational demands of the Earth System models we're increasingly using approaches that fuse these models with machine learning to speed up computations and our ability to incorporate new data into forecasts.