Recording the Effects of Climate Change

We just had a pretty good storm blow through northern California. The winds were clocked in Sacramento at 63 mph (101 km/hr) and it destroyed a Red-tailed Hawk nest which I had been monitoring for a few years now. The nest had been destroyed by another wind event last winter, too, and had just been rebuilt. Although this wind event has not been directly attributed to climate change, it has been suggested that it was made worse by climate change and El Nino. (My observation trail is here:

This made me wonder if it would be helpful for iNat to collect data on observations which were influenced by climate change, similar to the California State Roadkill project. However, given my health of late, I do not have the capability or energy to administer such a project. Just a thought, realizing that not all ideas are good ideas.


Small soapbox moment: It’s a mischaracterization to talk about Climate Change’s impact on any one single weather event. This is a common oversimplification perpetuated by the media. Climate Change can be attributed to changes in frequency and average severity of weather events, but we need to stop referring to Climate Change in reference to the occurrence/severity of a single weather event.

I don’t think observations can really be attributed to “influenced by Climate Change”. Climate Change can influence populations, phenology, and range shifts, but again, it’s a mischaracterization to attribute Climate Change as a causal factor to something that happened in any single point in time. Whereas roadkill can be definitively determined.


I’m not a scientist but I suspect it’s probably difficult to directly attribute a single observation to climate change. What you want to look for are trends, so I’d say the best thing to do is keep observing (including common species) and getting the data onto iNat. Also annotating observations will help generate phenological data. iNat observations were used in an attempt to model off-season Yucca blooms [PDF], for example.

Another approach might be a project like this one that @leptonia made last year, which collects fungus observations from areas affected by Tropical Storm Hilary (a storm which was possibly made more likely due to climate change).


An additional issue is that all weather is influenced by anthropogenic climate change - the entire atmospheric system has more energy in it and you can’t easily say this event was influenced but that one not - they both were, though we tend to only attribute extreme events to climate change and normal ones we don’t think about.

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As someone whose other area of interest outside ecology is meteorology, I was going to say something similar about how a single event cannot easily be attributed to climate change, There are cases where an extreme record weather event can be determined to have only been as extreme as it was due to climate change, but we are talking something like 220 mph vs 190 mph winds in a hurricane, we cannot really say the existence of the hurricane was caused by climate change

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I appreciate you comment. It is true, it is currently claimed that any single event cannot be attributed to climate change, however, when one looks at a large number, then one can start making conclusions about the effects of climate change. It is known that hurricanes rains and atmospheric rivers are getting stronger concurrently with climate warming. The weather service issued their first ever hurricane wind warning for the San Francisco Bay area and they are also contemplating creating a Category 6 Hurricane level. What I am suggesting is to start collecting the data and one can then see trends. Yes, it is a hunch, but given enough data, and the use of some good algorithms, one may be able to develop some conclusions.

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I think that by recording where things are and when things happen (like flowers blooming) and odd events (like heat damage to conifers a couple years ago) we automatically record effects of climate change. We don’t need to tag individual observations as due to global warming (and as pointed out above, we can’t, really). To turn iNaturalist raw data into reports on climate change, we need people looking through the data and reporting on trends. I imagine we’ll see more of that (an indirect effect of warming).


It’s a misconception that category 6 storm can only happen due to climate change, storms of the intensity that has been proposed to be called cat 6 have occurred in the past, likely even hundreds of years ago, they are not a new thing, it’s just new that we don’t lump them in with cat 5s

Cat 6 hurricanes are expected to increase in frequency due to climate change, but a cat 6 rating does not automatically mean climate change is responsible for the specific event, or even for the cat 6 intensity


It’s not really a “burden of proof” issue; it’s more about the definition of climate change itself. The climate is not defined by a single weather event. So it’s nonsensical to attribute the occurrence or severity of any single event to climate change.

This may seem like petty semantics, but it is important because otherwise it falls along the same line of reasoning used by many climate change deniers (e.g., “if global warming is real, why did we have 6 inches of snow last Saturday”).


Back in the late 1990’s I started studying the biological impacts of climate change and have followed the climate models and the associated literature quite closely. There is a relatively young branch of statistical analyses that attempts to attribute single events to ongoing climate alterations. Below are a few references that come up on a google scholar ( search of keywords “climate change event attribution”. While not necessarily written for the lay public, the point is, unequivocally yes, we are able to apply statistical methods to assess likelihood that any one event is caused by climate change.

But that really wasn’t your question. Your question was can/should it be documented. While scientists, such as myself, are in the early stages of figuring out how to harness the power of community science, such as iNaturalist, the power to do so resides entirely on the data available. So, absolutely you should document ANYTHING that seems like a climate-related phenomenon. Some examples of seemingly innocuous things that may be relevant: first frog call, first plant flowering, first tree turning autumn colors, new bird where it’s never been observed, snakes encountered in the middle of winter, a dead bird on the seashore. Seasons are changing and we cannot easily predict which observations will be the ones that illustrate the change we’re trying to document. We cannot predict which will be the last observation of a species. For example, golden toad populations simultaneously crashed in 1987 when El Nino caused precipitation changes and likely caused failure of reproduction and subsequent recruitment. The science took over a decade to figure this out (see Pounds et al. 1999 below) as well as any number of conference presentations, symposia, etc. Early on the literature was, who knows? For example, see Barinaga, 1990. But as data accumulated the pattern became obvious and unequivocal. Data comes in different forms and iNat is a great source of the undirected, unfocused, unexpected, but represents the power of community to generate data in a new and novel way.

Who knows what we would have known if we weren’t limited to the scientific community relying on their own data to determine these events. We cannot know which will be the last observation of an endangered species. Nor can we know which observations will be of value to the scientific community. However, we do know that lack of information will prevent any such understanding and that’s the gap this site fills. It is an undirected source of information that with time and effort will enable scientists to harness information to gain knowledge and understanding. ANY observation you add might do that, and you may never know which observations have value and which are just more of the same.

Barinaga, Marcia. “Where Have All the Froggies Gone? It has taken a decade, but herpetologists are hopping up and down about declining amphibian populations.” Science 247.4946 (1990): 1033-1034.

Hulme, Mike. “Attributing weather extremes to ‘climate change’ A review.” Progress in Physical Geography 38.4 (2014): 499-511.

Otto, Friederike EL. “Attribution of Extreme Events to Climate Change.” Annual Review of Environment and Resources 48 (2023): 813-828.

Pounds, J. Alan, Michael PL Fogden, and John H. Campbell. “Biological response to climate change on a tropical mountain.” Nature 398.6728 (1999): 611-615.

Stott, Peter A., et al. “Attribution of extreme weather and climate‐related events.” Wiley Interdisciplinary Reviews: Climate Change 7.1 (2016): 23-41.

Trenberth, Kevin E., John T. Fasullo, and Theodore G. Shepherd. “Attribution of climate extreme events.” Nature Climate Change 5.8 (2015): 725-730.


Maybe I was not clear enough, and even when I was listing California State Road Kill as an example, I was afraid it would give the wrong impression. I am not saying that one should declare an observation the result of climate change, I am saying that one could connect it to a project as a hunch that it was somehow connected. Frozen alligators in Florida, birds freezing in the southern US due to dips in the Arctic polar jet stream - I am suggesting flagging observations like these. They are going to effect biodiversity in the future and do we attribute these to pesticides or climate change? Let someone look at the data and evaluate it. But we first have to document it. Maybe a project is not the correct way, maybe just posting the observation is enough, I don’t know the answer.


An organisms affected by extreme weather project would be interesting, and would
avoid the issues involved with attributing specific weather events to climate change


and @petezani , too. I do believe that noting when first flowers are blooming, observing birds mating in January, etc. are relevant data, but I do not know the best way to document this such that later, when an algorithm is scanning all the observations, it is able to locate relevant observations. Even though in my career I developed software to pull signals out of noise, I am not an expert in this domain. Do the observations standing alone provide enough information, do comments help, or is a more formal method needed. I see an amazing resource with iNat and I am just presenting the idea that iNat needs to look at this, I do not have the solution.


The problem with utilizing the data is getting around the known biases of density- and time-dependence. There aren’t easy ways to do that, but there examples where a generic observation provides relevant insight. I’ll provide an example from my own work. In the early 2000’s I started studying lizards at a site near Lovelock, NV. I noticed something interesting. One lizard, Sceloporus uniformis, was numerically very abundant in that area. However, several key pieces of lizard literature (Parker and Pianka 1975, Wilson 1991) made no mention of them at that same site. I even went so far as to contact these scientists to ask about that. Byron Wilson checked his notes…nope, no observation of that species. That’s neat, it must have arrived sometime in the past 20 years or so. I went up and down the valley, but couldn’t find them farther north that at Lovelock. Imagine my surprise when I start looking at the iNat data on this species and notice a single observation of this species from a boat launch north of there in 2018 and now with 4 observations in 2023. Then they show up farther north still in 2022 within just a few miles of my negative survey site at the southern terminus of the Eugene Mountains just west of Imlay. I’ve revisited this site as recently as 2023, and STILL no S. uniformis there despite it being perfect habitat and plenty of other lizard prey available. I have no doubt they are coming, and this site has allowed me to estimate just how FAST they are coming. Piecing this story together, they seem to have gone from not present to really abundant to really abundant, but also expanding in both range and number of observations at those sites. Can I prove they weren’t at that boat launch before then? No, but I know from my own surveys in the area (not at the boat launch proper) that if they were there, they weren’t abundant enough for me to encounter any of them.

So, I have part of the story from my data, but iNat observations contribute a valued piece that I wouldn’t have thought to include if those kind observers didn’t snap that picture and post it. It’s not necessarily WHAT the observations say, but just THAT they exist that is key in this example. I think comments help, but they’re inconsistent. The consistent things we can affect as observers and identifiers is accurate date and time, quality pictures from which proper IDs can be made, an accurate community ID, and someone who has both the literature and the observations available to piece together stories like this. But on their own those observations aren’t obviously valuable to address species range expansion and colonization. It takes a village, as it were.

I don’t have the solution either, but lacking those observations, I wouldn’t be as keen to keep going back to my negative census site to track these things as they take advantage of the newly favorable environment. As a scientist I would be limited to my own data and that available in the literature or museum collections. This site does this fantastic job of providing a window into the natural world, an amazing resource as you aptly put it, and one that ANYONE has the power to contribute to, if they so choose. Yet I don’t think there needs to be an obvious solution to be able to say any and all observations are welcome additions regardless of the quality of the photos, the comments or attributions added, or seemingly irrelevant species observed. All I really care about is that the date, time and location are accurate and that any available pictures (not necessarily the best one or few) are contributed. The rest will take care of itself.

Parker, W. S. and E. R. Pianka (1975). “Comparative ecology of populations of the lizard Uta stansburiana.” Copeia 1975: 615-632.

Wilson, B. S. (1991). “Latitudinal variation in activity season mortality rates of the lizard Uta stansburiana.” Ecological Monographs 61(4): 393-414.


This is a good example of how continual iNat surveys of one place (Pillar Point reef) were able to show a change that is potentially attributable to climate change, or at least climate effects: This is one of, if not the most surveyed tidal area in California.

Also, check out the iNat observation history for Hopkin’s rose nudibranchs at Pillar Point:


how does one access taxon history for a specific location?

Use the “filter by place” option on the taxon page:


Keep in mind that the filter is sticky, so it will stay there for you on all taxon pages until you remove or change it.


Extreme weather events have been providing important insights into ecology and evolution even apart from climate change for literally hundreds (or at least one hundred…) years. Two good examples (both with cold winter storms):

A cold snap due to the polar vortex about a decade ago created a natural experiment that allowed observation of evolution due to cold mortality in Anolis lizards:
Surviving lizards had higher cold tolerances, indicating evolution in the population in frequencies of genes underlying cold tolerance.

A famous historical study by Dr. Hermon Bumpus (what a name!) was based on his collection of house sparrows knocked down by a winter storm in Providence RI:
He found that birds with certain morphologies survived better, providing a mechanism for evolution by natural selection. The data have famously been reanalyzed a bunch with some varying conclusions, but both studies indicate the importance/potential of data from extreme weather events.

So I would say, absolutely make observations! And remember that baseline observations are actually often the limiting factor in these types of studies. It’s comparatively easy to go out and gather data after an extreme weather event, but if you don’t have the baseline/control data from before, there’s only so much you can say. In the anole study, the authors realized that they serendipitously had collected the baseline data and essentially went out and recreated their sampling after the event, so they had a before/after design to analyze. So any data iNat folks collect might be the control/baseline data for a future study.


I’m interested in creating baseline data for one area. iNaturalsit seems like a perfect way to record what is here now for people in the future. I have no preconceived conclusions. I am seeing the transition from White Pines and Hemlocks to other trees.

My main interest is trees, so I make a lot of tree observations. At the same time, I photograph what’s growing around the trees for AI to come along and find later, when it can.

Are there any standards for recording what an area looks like now for comparison in the future?

Comparative vegetation / landscape photos for Southern Africa.

iNat needs a better way to deal with habitat photos (which plant? No - this is the wide habitat view)

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