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- So without further ado,
this is Mike Hallworth.

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He's a data scientist with
Vermont Center for Ecostudies,

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and this is his talk, Living
on the Edge in a Warming World,

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Distributions and Thermal Refugia

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of Forest Insects Across the
Northeastern United states.

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So we'll turn it over to you, Mike.

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- Great, good afternoon everybody.

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Thank you for having me here.

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I'm excited to talk about
some preliminary results

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with you today on how climate change

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may impact insights across the
broader New England region.

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All right, so insects are
the world's most species-rich

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and abundant group of macroscopic
organisms on the planet,

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and community science data

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across the broader New
England region confirm that.

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So this pie chart that you're looking at

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on the left-hand side here just shows

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that insects are by far and
away the most speciose group

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that we've identified
as community scientists,

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over 1,600 insects species.

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The problem is that they're experiencing

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precipitous declines.

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A recent study a couple
of years ago showed

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that insect biomass has been declining

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in protected areas in Germany.

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And so here is flying insect
biomass from malaise traps

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through time on the X-axis.

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And the rate of global
declines is still uncertain,

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and there's some dispute about that,

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but scientists agree that
insects are in decline globally.

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And we're seeing this decline
in the Northeast, as well.

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We're seeing beetle declines.

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One example came recently
from a Wellesley undergrad

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who published a pretty high-impact paper

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where they replicated
some window trap surveys

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for beetles at Hubbard
Brook Experimental Forest

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in New Hampshire where they
did some window trap surveys

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in the '70s, and then they
replicated the surveys

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in 2015 to 2017, which are
looking at on the Y-axis here

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is the mean captures per two-day period,

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and time on the X-axis.

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And the gray bars show
the results from the '70s,

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and the black bars show
the more recent results,

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and there's a drastic decline
in the number of beetles

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that were captured in a two-day period.

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They found that the number
of beetles that they caught

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during that time period
was strongly correlated

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with the median snow depth
during the preceding winter.

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So there's some climate
component there potentially

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of the decline in beetle captures.

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But it wasn't just beetle
captures that was reduced.

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It turns out that the species
richness was also reduced.

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So the actual species that
they captured in those surveys,

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so this is the original
species accumulation curve

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from the '70s, where you have species taxa

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on the Y-axis and the number
of samples on the X-axis,

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and there's a drastic reduction

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in the number of species
that they captured

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when comparing the two survey periods.

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So that leaves the question, like,

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what factors are contributing

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to some of these observed declines?

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And David Wagner and colleagues

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published a paper recently this year

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in Proceedings of the National
Academy of Scientists,

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and they suggested that insect declines

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are caused by the death
of a thousand cuts.

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And many of these cuts are
related to climate change,

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whether it's species disruption,

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so changing species interactions,

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more intense and larger wildfires,

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storm intensity that
cause increased flooding,

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warmer temperatures,

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which could alternate their
life stages, and droughts.

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And all of those types of stressors

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are compounding what's happening below,

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so like deforestation, insecticides,

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introduced species,
urbanization and so on.

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And so climate change is a real issue.

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And so how might climate change

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impact insect populations?

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Well, it could reduce habitat
suitability for some species,

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change habitat suitability for others.

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It could alter range limits
and change range limits.

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So we see that some species
are moving northward

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and some are moving potentially southward.

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It could alter the timing
of lifecycle events.

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So, think of when
caterpillars form cocoons

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or when dragonflies eclose.

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And why does this all matter?

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Well, insects are hugely important,

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and especially for forested ecosystems.

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They perform many, many
ecosystem services.

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For example, they're culturally important

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and they provide great aesthetics,

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like this black swallowtail caterpillar.

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They provide critical ecosystem function,

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like pollination and seed dispersal,

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but they're also bioindicators
of ecosystem health

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and for water quality.

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They perform pest control
and they help aerate soil,

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and there are so many more.

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They're also a hugely important
component of food webs,

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especially for vertebrates.

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So just one example,

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the chickadees that
presumably are breeding

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in your backyard, to
successfully raise a brood,

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they have to forage
and bring caterpillars,

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a lot of them, every day,

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between 350 and 570 caterpillars per day

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to successfully raise a brood.

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So they're hugely
important to our food webs.

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And since they occur
ubiquitously in our food webs

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and they serve as bioindicators,

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it's imperative that we
understand how their populations

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and species richness
responds to climate change.

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And so today we set out, I'm
gonna tell you a little bit

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about what we are working on,

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and we set out to develop
species distribution models

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to better understand where
species are within the landscape

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and how their distributions
may be changing

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given climate projections.

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And we need these detailed maps

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to understand both the current
risks and the future risks

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that these species may encounter,

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and these distribution maps
help fill critical data gaps

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as identified in a lot of the
state wildlife action plans

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across the Northeast,

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and this data will help allow stakeholders

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to make really informed decisions

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about where to place management
or take management action

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in the future, given climate change.

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So I'm gonna talk briefly
about the methods.

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We used community science observations

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submitted to GBIF

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or the Global Biodiversity
Information Facility

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through iNaturalist and
eButterfly and other sources.

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So we took the presence data for species,

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and all the insects with more than five

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independent research grade observations,

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so that means that someone else said,

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oh, yes, that is a black swallowtail,

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so more than one person, it
was confirmed by many people.

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And we know that there's sampling bias

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in where people submit their observations,

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that, like, they tend to
submit where they live

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and near roads.

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So we accounted for that sampling
bias in the location data,

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and then we associated
those occurrence records

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with bioclimactic data,

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so things like mean annual temperature,

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precipitation, for example.

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And we used that relationship
between the current data,

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the occurrence data, and the climate data,

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to make predictions about
how occurrence will change

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given climate change,

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and that allowed us to make
both current distribution maps,

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like shown here on the top,

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at one by one kilometer resolution.

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And then we also are able to feed, okay,

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well, how will the climate change

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given some ensemble models,

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and what is the projected relationship

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or the distribution of these
species moving forward?

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So given the climate projection,

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the current relationship between climate,

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we can get a distribution map,

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and if those relationships stay similar,

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then this is the projected distribution

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for that species in the future.

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So I'm gonna dive right
into some results here.

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Where are forest insects
across the Northeast?

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So what you're looking at is
the estimated species richness

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on the left here, this
is the current range,

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where they are, and on the right

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is where they're projected to be

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given the most likely
climate emission scenario,

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which is RCP 7.0 in the
year where the climate

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that encompasses 2071 to 2100.

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You can see the range
and species richness here

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between the current and the future.

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So this is a great first
picture of where things are,

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but I was kind of really interested

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in where things will be changing.

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And so what you're
looking at in this slide

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is this is the previous map

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on the previous slide for reference.

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This histogram here on the lower left

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shows the change in species
richness on the X-axis

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and the frequency of cells on the Y-axis.

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So there are some places
where we're losing

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200-ish species, and
there are lots of places

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where we're gaining lots
of species, upwards of 800.

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I should say gaining, I should say moving.

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Species are moving, because
I eliminated the taxa

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to what is currently in New England

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and not what is potentially
moving into New England.

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And this is where it's happening in space.

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The red indicates areas
where species richness

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will be lower in 2100,
and the warmer colors

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means that more species are
potentially leaving that spot.

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And the cooler colors
in blue indicate areas

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where there's a species richness increase.

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On this histogram on top
shows how that change

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is distributed in longitude.

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So this is on the west, and this is east,

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and then this on the right,
this histogram on the right,

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shows how it will change with latitude,

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so this is south and north.

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So you can see

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where the projected changes
may occur at latitude.

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So this is all taxa,

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so what I'm gonna do now is
talk about some insect groups.

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So how will climate impact
the major insect orders?

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And I just picked these
major insect orders

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because I thought they were interesting,

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but we did 20 insect orders.

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So on the maps,

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you'll see more of these
maps in a little while,

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but the current species
richness is on the left,

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and the species richness for those orders

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is on the right in in 2071 to 2100.

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Now I was interested in
how to kind of numerically

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or quantitatively measure

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how things will change in the future,

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and so this is like a
climate velocity analog

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to species richness.

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And so what you're looking at here is,

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this is the current richness,

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and then this is the projected
richness in the future.

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And so what this velocity is measuring

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is the rate of change

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between the current species richness value

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and where the closest analog
pixel will be in the future.

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So how far does this cell
need to move per year

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to represent this cell in the future?

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That's the rate of change.

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So it's in kilometers per year.

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The direction of change
measures which direction

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this particular cell will need
to move with respect to north

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to represent this cell in the future.

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So there are two metrics here,

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velocity, which is rate, and direction.

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You'll see a few of these
histograms coming forward,

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so I just want to describe
them a little bit.

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On the X-axis is the rate of
change in kilometers per year.

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So this value here, there's a few values

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of six kilometers per
year, and a lot of values

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that are close to zero,

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whereas that pixel does not
have to move through time.

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The rose plot up on the upper right

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shows the direction of change,

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where north is zero,
east, south, and west.

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And so in this particular example,

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almost all the pixels
were moving northward

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to match their species analog future.

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So diving into these results,

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how will climate impact
the major insect orders?

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What you're looking at
here is Lepidoptera.

260
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And so this is the histogram on the left

261
00:13:05,680 --> 00:13:09,060
of climate or the velocity,
kilometers per year,

262
00:13:09,060 --> 00:13:12,300
and the rose plot, so which
direction do they have to go

263
00:13:13,320 --> 00:13:15,050
to meet that analog?

264
00:13:15,050 --> 00:13:17,950
Most of the species are moving northward.

265
00:13:17,950 --> 00:13:19,420
Pixels, sorry.

266
00:13:19,420 --> 00:13:21,360
Some pixels are moving towards the east

267
00:13:21,360 --> 00:13:22,980
and some are moving towards the west,

268
00:13:22,980 --> 00:13:25,280
but the majority are moving northward.

269
00:13:25,280 --> 00:13:30,120
And the main velocity, it was
about 1.5 kilometers per year

270
00:13:30,120 --> 00:13:33,360
between the current distribution

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00:13:33,360 --> 00:13:36,633
and the distribution in 2100, let's say.

272
00:13:39,040 --> 00:13:41,280
So for Coleoptera, for beetles,

273
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the mean is 1.48 kilometers per year,

274
00:13:44,500 --> 00:13:46,760
so about the same as Lepidoptera,

275
00:13:46,760 --> 00:13:49,383
but almost all of those
pixels are moving due north.

276
00:13:50,790 --> 00:13:53,680
For Diptera, we see a similar pattern.

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00:13:53,680 --> 00:13:57,970
The mean is about 1.6 kilometers per year,

278
00:13:57,970 --> 00:14:00,360
so Diptera are the flies.

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00:14:00,360 --> 00:14:03,610
And most of those pixels
need to move north

280
00:14:03,610 --> 00:14:06,770
to reach that species
richness analog in the future.

281
00:14:08,620 --> 00:14:11,580
Hymenoptera, bees, ants, et cetera,

282
00:14:11,580 --> 00:14:16,140
their mean is about 1.5
kilometers per year.

283
00:14:16,140 --> 00:14:18,040
That's how far the
pixel would need to move

284
00:14:18,040 --> 00:14:20,830
to reach a species analog in the future,

285
00:14:20,830 --> 00:14:23,330
and almost all of those
pixels need to move north.

286
00:14:24,650 --> 00:14:28,150
Hemiptera, so the true
bugs, things like cicadas,

287
00:14:28,150 --> 00:14:30,653
aphids, plant hoppers and the like,

288
00:14:31,500 --> 00:14:33,763
their mean is about 1.45,

289
00:14:35,190 --> 00:14:38,500
and almost all the
cells would need to move

290
00:14:38,500 --> 00:14:40,070
in a northward direction to meet

291
00:14:40,070 --> 00:14:42,463
their species richness analog future.

292
00:14:43,780 --> 00:14:47,460
So one thing to take home
from this part of the talk

293
00:14:47,460 --> 00:14:52,200
is that most of the
cells and all the orders

294
00:14:53,769 --> 00:14:55,219
would need to move northward.

295
00:14:56,930 --> 00:14:59,640
Almost all of them, so
four of these five groups,

296
00:14:59,640 --> 00:15:01,800
are moving almost exclusively north,

297
00:15:01,800 --> 00:15:04,150
with the caveat of Lepidoptera,

298
00:15:04,150 --> 00:15:08,050
and that's likely because there
are many, many more species

299
00:15:08,050 --> 00:15:09,050
in the Lepidoptera.

300
00:15:09,050 --> 00:15:11,180
There are thousands of species in Lep,

301
00:15:11,180 --> 00:15:15,240
so they could be responding
slightly differently

302
00:15:15,240 --> 00:15:18,080
than some of the other insect orders here.

303
00:15:18,080 --> 00:15:21,170
So how does velocity
differ by forest type?

304
00:15:21,170 --> 00:15:22,640
So I'm gonna skip through here.

305
00:15:22,640 --> 00:15:26,440
The take-home here is that that all forest

306
00:15:26,440 --> 00:15:30,020
at the top here, deciduous forest,

307
00:15:30,020 --> 00:15:32,130
has the fastest climate velocity,

308
00:15:32,130 --> 00:15:37,130
and that pattern holds for
most of all the five orders.

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00:15:38,890 --> 00:15:40,380
So I want to get to this slide,

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00:15:40,380 --> 00:15:43,400
where insect velocity is
really high across New England,

311
00:15:43,400 --> 00:15:44,920
so where is that happening?

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00:15:44,920 --> 00:15:48,060
The cool colors represent
slower velocity areas,

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00:15:48,060 --> 00:15:53,060
and the warm colors shown in here in red,

314
00:15:53,680 --> 00:15:57,890
and I should note that the
insect velocity is way higher

315
00:15:57,890 --> 00:16:01,850
than a lot of the other
taxa in Western hemisphere.

316
00:16:01,850 --> 00:16:02,850
Birds are about 0.6,

317
00:16:04,110 --> 00:16:08,240
and here are the 20 insect
orders that we looked at.

318
00:16:08,240 --> 00:16:11,120
They're in order of
magnitude higher than birds,

319
00:16:11,120 --> 00:16:13,700
amphibians, and mammals.

320
00:16:13,700 --> 00:16:15,350
And why might that be?

321
00:16:15,350 --> 00:16:18,680
It's likely because the insects
are closely tied to climate,

322
00:16:18,680 --> 00:16:21,010
so they're following the
climate velocity faster.

323
00:16:21,010 --> 00:16:24,120
So their development,
phonology, their migrations,

324
00:16:24,120 --> 00:16:26,870
and because their life
histories are faster,

325
00:16:26,870 --> 00:16:29,200
they may be able to respond faster

326
00:16:29,200 --> 00:16:30,973
to how climate is changing.

327
00:16:32,190 --> 00:16:33,890
How can we use these data?

328
00:16:33,890 --> 00:16:36,450
Well, this is a, the climate velocity

329
00:16:36,450 --> 00:16:40,300
and the protected areas
overlap on that map,

330
00:16:40,300 --> 00:16:42,130
and you can see, there are lots of areas

331
00:16:42,130 --> 00:16:44,380
with slower velocity,

332
00:16:44,380 --> 00:16:46,920
so these cooler colors
that are not protected.

333
00:16:46,920 --> 00:16:50,010
And I should note that
that's the species-richness.

334
00:16:50,010 --> 00:16:52,230
It doesn't necessarily
mean that the community

335
00:16:52,230 --> 00:16:54,380
under those cells will be the same,

336
00:16:54,380 --> 00:16:55,870
and so that's the next step.

337
00:16:55,870 --> 00:16:58,440
So where in the landscape will we see

338
00:16:58,440 --> 00:17:01,710
novel communities in the
future that we don't see now?

339
00:17:01,710 --> 00:17:03,730
That's the next step
that we're gonna take.

340
00:17:03,730 --> 00:17:06,240
And I just want to, I'd be remiss

341
00:17:06,240 --> 00:17:10,280
if I didn't thank everyone
that's submitting observations

342
00:17:10,280 --> 00:17:12,730
to iNaturalist or eButterfly.

343
00:17:12,730 --> 00:17:15,580
This work is only possible
because that data are there

344
00:17:15,580 --> 00:17:19,120
and to play with, so thank
you for being curious,

345
00:17:19,120 --> 00:17:23,140
and I just want to say
thank you to all the funders

346
00:17:23,140 --> 00:17:25,810
and my collaborators
and co-authors on this

347
00:17:25,810 --> 00:17:29,340
that have been helping this process.

348
00:17:29,340 --> 00:17:32,333
So I apologize for going
over, but thank you very much.

349
00:17:34,510 --> 00:17:35,343
- [Tim] Tim Howard here.

350
00:17:35,343 --> 00:17:36,380
This is just a quick question.

351
00:17:36,380 --> 00:17:37,653
Can you hear me?

352
00:17:37,653 --> 00:17:38,486
- Yes.

353
00:17:38,486 --> 00:17:40,510
- [Tim] Yeah, so that's fascinating stuff

354
00:17:40,510 --> 00:17:41,560
and very interesting.

355
00:17:42,860 --> 00:17:45,210
So the velocity was richness to richness.

356
00:17:45,210 --> 00:17:48,520
So if the cell stayed
from a richness of three

357
00:17:48,520 --> 00:17:51,700
to a richness of three,
but changed the species,

358
00:17:51,700 --> 00:17:54,130
that would be a velocity of zero,

359
00:17:54,130 --> 00:17:55,639
am I understanding that correct?

360
00:17:55,639 --> 00:17:56,680
- Exactly.
- And that is,

361
00:17:56,680 --> 00:17:58,700
and that's why you said
gotta look at species next,

362
00:17:58,700 --> 00:17:59,780
is that what's going on?

363
00:17:59,780 --> 00:18:03,130
- Yeah, so looking at novel
communities in the future

364
00:18:03,130 --> 00:18:03,963
is the next step.

365
00:18:03,963 --> 00:18:07,660
So will we see new communities
popping up elsewhere

366
00:18:08,990 --> 00:18:10,260
that we don't see today?

367
00:18:10,260 --> 00:18:13,910
And that will have to
involve modeling species

368
00:18:13,910 --> 00:18:15,440
that could be coming into New England

369
00:18:15,440 --> 00:18:16,690
that aren't currently in New England.

370
00:18:16,690 --> 00:18:20,430
So yes, this is a species
richness velocity.

371
00:18:20,430 --> 00:18:22,100
- [Tim] Thank you.

372
00:18:22,100 --> 00:18:23,710
- [Woman] We had one quick
question in the chat,

373
00:18:23,710 --> 00:18:26,100
which is, what was behind the decision

374
00:18:26,100 --> 00:18:27,980
to focus on temperature refugia

375
00:18:27,980 --> 00:18:31,561
versus hydrologic or
other gradient refugia?

376
00:18:31,561 --> 00:18:33,610
- That's a great question,

377
00:18:33,610 --> 00:18:38,320
and the simple answer is
that in my former life,

378
00:18:38,320 --> 00:18:40,730
I was at the Climate Science Center

379
00:18:40,730 --> 00:18:42,240
and we focused a lot on climate,

380
00:18:42,240 --> 00:18:46,100
and so hydrology changes would
be a great thing to look at,

381
00:18:46,100 --> 00:18:48,350
but, you know, I'm very familiar

382
00:18:48,350 --> 00:18:50,610
with how temperature will change.

383
00:18:50,610 --> 00:18:52,210
But adding those types of components

384
00:18:52,210 --> 00:18:56,180
will give us a great
things to do in the future

385
00:18:56,180 --> 00:18:58,430
and will likely be very
interesting, as well.