536
00:00:00,000 --> 00:00:01,627
Yes. My name is
Hunter storm.

537
00:00:01,627 --> 00:00:03,187
I am the UN

538
00:00:03,187 --> 00:00:05,677
project coordinator
here in Portland.

539
00:00:05,677 --> 00:00:07,552
And I am also a student

540
00:00:07,552 --> 00:00:09,607
at Portland State
University.

541
00:00:09,607 --> 00:00:12,802
And this is my undergraduate
thesis project,

542
00:00:12,802 --> 00:00:14,002
I am predicting as

543
00:00:14,002 --> 00:00:16,372
a predator distributions
in Portland.

544
00:00:16,372 --> 00:00:18,112
I want to introduce
the other people

545
00:00:18,112 --> 00:00:19,582
who are on this
project with me.

546
00:00:19,582 --> 00:00:20,872
That's Alissa story.

547
00:00:20,872 --> 00:00:22,237
She's also a professor at

548
00:00:22,237 --> 00:00:25,417
Oregon State University,
lesly kitchen.

549
00:00:25,417 --> 00:00:26,782
She's the director
and founder

550
00:00:26,782 --> 00:00:28,012
of the smart group.

551
00:00:28,012 --> 00:00:30,382
To leave insight,
our moderator and

552
00:00:30,382 --> 00:00:31,492
also staff scientist at

553
00:00:31,492 --> 00:00:34,462
Brooklyn Audubon and
Dante from tomato.

554
00:00:34,462 --> 00:00:36,412
He has a instructor
at Portland State

555
00:00:36,412 --> 00:00:39,722
and he's also my advisor
on this project.

556
00:00:39,882 --> 00:00:41,272
Okay.

557
00:00:41,272 --> 00:00:42,742
So un is

558
00:00:42,742 --> 00:00:45,112
the urban wildlife
information network.

559
00:00:45,112 --> 00:00:47,167
This is a national

560
00:00:47,167 --> 00:00:50,152
wildlife
monitoring project

561
00:00:50,152 --> 00:00:51,532
that started in Chicago

562
00:00:51,532 --> 00:00:52,882
at the Lincoln Park Zoo.

563
00:00:52,882 --> 00:00:54,472
And they now have

564
00:00:54,472 --> 00:00:56,212
35 different
partner cities

565
00:00:56,212 --> 00:00:57,397
across the United States

566
00:00:57,397 --> 00:00:59,042
and a couple in Canada.

567
00:00:59,042 --> 00:01:01,437
All of these different
partner cities use

568
00:01:01,437 --> 00:01:04,212
a shared data
monitoring protocol.

569
00:01:04,212 --> 00:01:06,522
So we all use
camera traps.

570
00:01:06,522 --> 00:01:08,832
You have to have
25 camera traps

571
00:01:08,832 --> 00:01:10,572
to be a human
partner cities.

572
00:01:10,572 --> 00:01:11,637
So once you meet that,

573
00:01:11,637 --> 00:01:13,272
you are able to upload

574
00:01:13,272 --> 00:01:15,282
your data to a
shared database.

575
00:01:15,282 --> 00:01:16,152
And then all of

576
00:01:16,152 --> 00:01:17,262
these different
partner cities

577
00:01:17,262 --> 00:01:18,882
can access that data.

578
00:01:18,882 --> 00:01:20,352
Which allows for a very

579
00:01:20,352 --> 00:01:22,812
wide spread
wildlife research

580
00:01:22,812 --> 00:01:25,402
to take place, which
is really cool.

581
00:01:25,562 --> 00:01:29,487
So in Portland we
have 25 camera sites.

582
00:01:29,487 --> 00:01:30,642
Each of those little brown

583
00:01:30,642 --> 00:01:32,532
dots is one camera.

584
00:01:32,532 --> 00:01:35,382
They extend from Hillsboro

585
00:01:35,382 --> 00:01:37,947
in the West to the
center of Portland.

586
00:01:37,947 --> 00:01:39,222
That little triangle in

587
00:01:39,222 --> 00:01:41,402
the center is
neuroses bottom.

588
00:01:41,402 --> 00:01:42,982
And then they extend to

589
00:01:42,982 --> 00:01:44,497
aggression in the east.

590
00:01:44,497 --> 00:01:47,512
So these cameras
are placed in

591
00:01:47,512 --> 00:01:50,872
public parks and greenways
across Portland.

592
00:01:50,872 --> 00:01:53,617
They're placed
every season

593
00:01:53,617 --> 00:01:54,742
of the year and for

594
00:01:54,742 --> 00:01:56,647
30 days during
that season.

595
00:01:56,647 --> 00:01:59,197
So at the end of
that 30 day period

596
00:01:59,197 --> 00:02:01,102
than our volunteers
go in and take

597
00:02:01,102 --> 00:02:03,967
the cameras out and
then they upload

598
00:02:03,967 --> 00:02:05,842
the photographs that
the cameras stuck

599
00:02:05,842 --> 00:02:07,972
to the national
UN Database.

600
00:02:07,972 --> 00:02:09,682
And then they go
through and tag

601
00:02:09,682 --> 00:02:12,397
each photograph
by species,

602
00:02:12,397 --> 00:02:14,122
by what species is

603
00:02:14,122 --> 00:02:16,102
present at the
Canvas site.

604
00:02:16,102 --> 00:02:18,502
So the species that

605
00:02:18,502 --> 00:02:19,642
I'm looking at
for my project

606
00:02:19,642 --> 00:02:22,792
are possums, Katie's
and raccoons.

607
00:02:22,792 --> 00:02:24,907
These are mezzo creditors

608
00:02:24,907 --> 00:02:27,441
or medium-sized
predator species.

609
00:02:27,441 --> 00:02:29,017
They are urban adaptive,

610
00:02:29,017 --> 00:02:31,387
say you probably
seen them around.

611
00:02:31,387 --> 00:02:33,442
They live closely with
humans and they have

612
00:02:33,442 --> 00:02:35,887
a pretty wide distribution
across Portland.

613
00:02:35,887 --> 00:02:38,887
So there's been
quite a bit of work

614
00:02:38,887 --> 00:02:40,252
looking at how coyotes are

615
00:02:40,252 --> 00:02:42,187
distributed across
urban space,

616
00:02:42,187 --> 00:02:43,522
but that is not the case

617
00:02:43,522 --> 00:02:44,812
for possums and raccoons.

618
00:02:44,812 --> 00:02:46,537
They're pretty
understudied.

619
00:02:46,537 --> 00:02:49,072
And there's also
some work being

620
00:02:49,072 --> 00:02:51,382
done about meso
predator as possibly

621
00:02:51,382 --> 00:02:53,662
increasing in
urban areas due to

622
00:02:53,662 --> 00:02:56,287
apex predators like
the mountain lion

623
00:02:56,287 --> 00:02:58,237
getting pushed out
of urban space.

624
00:02:58,237 --> 00:02:59,722
Due to more fragmented

625
00:02:59,722 --> 00:03:01,537
habitat and human
development.

626
00:03:01,537 --> 00:03:03,892
That is causing
massive predators

627
00:03:03,892 --> 00:03:05,002
to increase in

628
00:03:05,002 --> 00:03:07,192
some areas as they have

629
00:03:07,192 --> 00:03:09,292
less competition
with apex predators

630
00:03:09,292 --> 00:03:11,852
and less predation
from them.

631
00:03:11,892 --> 00:03:14,062
So the point of

632
00:03:14,062 --> 00:03:15,682
this project is
to look at where

633
00:03:15,682 --> 00:03:19,432
meso predators are
located in urban space.

634
00:03:19,432 --> 00:03:20,902
And also look at what

635
00:03:20,902 --> 00:03:23,947
landscaped features are
at those same sites.

636
00:03:23,947 --> 00:03:26,332
So we have a lot of
different variables that

637
00:03:26,332 --> 00:03:27,352
we're looking at whenever

638
00:03:27,352 --> 00:03:29,002
we're doing this project.

639
00:03:29,002 --> 00:03:32,512
There's environmental
variables like water.

640
00:03:32,512 --> 00:03:35,587
There's also
how fragmented

641
00:03:35,587 --> 00:03:37,072
these habitat patches are

642
00:03:37,072 --> 00:03:38,977
and how cohesive they are.

643
00:03:38,977 --> 00:03:40,582
We also are looking at

644
00:03:40,582 --> 00:03:43,162
socio-demographic
variables like roads,

645
00:03:43,162 --> 00:03:44,812
population density,

646
00:03:44,812 --> 00:03:46,672
and even income in

647
00:03:46,672 --> 00:03:47,722
these areas to look

648
00:03:47,722 --> 00:03:49,102
at what variables might be

649
00:03:49,102 --> 00:03:51,022
affecting where
we're seeing

650
00:03:51,022 --> 00:03:53,812
these different mezzo
predator species.

651
00:03:53,812 --> 00:03:57,637
So just to give a broad
idea of the project,

652
00:03:57,637 --> 00:03:59,242
the first step, getting

653
00:03:59,242 --> 00:04:00,682
our meso predator
detections,

654
00:04:00,682 --> 00:04:02,992
we are pretty
much done with.

655
00:04:02,992 --> 00:04:05,452
In order for these
detections to

656
00:04:05,452 --> 00:04:07,432
be considered like
research grade,

657
00:04:07,432 --> 00:04:08,902
they have to be tagged by

658
00:04:08,902 --> 00:04:10,297
at least two volunteers.

659
00:04:10,297 --> 00:04:11,602
Right now the
data I'm showing

660
00:04:11,602 --> 00:04:12,802
has only been
tagged by one,

661
00:04:12,802 --> 00:04:15,712
so it's technically not
quite done yet, but

662
00:04:15,712 --> 00:04:17,092
But it should
give a great,

663
00:04:17,092 --> 00:04:18,787
good idea of where
we're going.

664
00:04:18,787 --> 00:04:20,662
And then we also have,

665
00:04:20,662 --> 00:04:22,792
the next step is looking
at land cover and

666
00:04:22,792 --> 00:04:24,277
socioeconomic variables

667
00:04:24,277 --> 00:04:26,842
across multiple,
multiple radii.

668
00:04:26,842 --> 00:04:29,137
This we're pretty
far along with.

669
00:04:29,137 --> 00:04:30,832
This is where I'm
going to be getting

670
00:04:30,832 --> 00:04:32,422
the data that I'm
showing today.

671
00:04:32,422 --> 00:04:34,357
The next step though
is pretty important.

672
00:04:34,357 --> 00:04:35,482
It's looking at the

673
00:04:35,482 --> 00:04:37,012
relationship
between all of

674
00:04:37,012 --> 00:04:40,087
our landscape variables
and multiple radii.

675
00:04:40,087 --> 00:04:42,532
So the next step is
instead of taking

676
00:04:42,532 --> 00:04:44,962
only one radius
around the site,

677
00:04:44,962 --> 00:04:47,152
we're going to look
at multiple radii and

678
00:04:47,152 --> 00:04:48,517
compare how the

679
00:04:48,517 --> 00:04:50,167
landscape variables
are changing

680
00:04:50,167 --> 00:04:52,747
as we move from
like five meters

681
00:04:52,747 --> 00:04:54,157
away from the site to

682
00:04:54,157 --> 00:04:56,257
100 meters away
from the site.

683
00:04:56,257 --> 00:04:58,462
How are those landscape
variables going

684
00:04:58,462 --> 00:05:00,667
to look different at
those different scales?

685
00:05:00,667 --> 00:05:03,277
So that's going to
inform our fourth step,

686
00:05:03,277 --> 00:05:05,857
which is determining
a modeling approach.

687
00:05:05,857 --> 00:05:07,192
And then the final step is

688
00:05:07,192 --> 00:05:08,902
going to be building
a model to help

689
00:05:08,902 --> 00:05:10,432
us understand which of

690
00:05:10,432 --> 00:05:12,082
these landscape
variables are going

691
00:05:12,082 --> 00:05:13,522
to be important
for looking

692
00:05:13,522 --> 00:05:15,561
at our meso predator
detections.

693
00:05:15,561 --> 00:05:18,202
And which can be you,
which can we use to

694
00:05:18,202 --> 00:05:21,202
understand where are
these meso creditors

695
00:05:21,202 --> 00:05:24,392
are located in
urban space.

696
00:05:24,462 --> 00:05:27,022
So this gives
an idea of kind

697
00:05:27,022 --> 00:05:28,807
of where the
state is going.

698
00:05:28,807 --> 00:05:30,922
I've mapped the
national land cover

699
00:05:30,922 --> 00:05:35,077
database sites for
you and you can see,

700
00:05:35,077 --> 00:05:36,712
you can start to see our,

701
00:05:36,712 --> 00:05:38,631
our distributions
of detections

702
00:05:38,631 --> 00:05:40,342
laid out across
all of our sites,

703
00:05:40,342 --> 00:05:42,202
which is pretty exciting.

704
00:05:42,202 --> 00:05:44,642
This just gives
an idea of,

705
00:05:44,642 --> 00:05:46,267
of what we're kind
of looking at,

706
00:05:46,267 --> 00:05:48,202
looking at different
distributions

707
00:05:48,202 --> 00:05:49,672
between each species

708
00:05:49,672 --> 00:05:50,932
at each site and also

709
00:05:50,932 --> 00:05:53,497
relative to the site
characteristics.

710
00:05:53,497 --> 00:05:55,867
What distributions of
wildlife we're seeing.

711
00:05:55,867 --> 00:05:58,972
And like I said, this
is only 11 radius.

712
00:05:58,972 --> 00:06:00,562
So looking at
multiple radii is

713
00:06:00,562 --> 00:06:03,172
going to give us a
lot clearer picture.

714
00:06:03,172 --> 00:06:06,052
This is another way of
visualizing this data.

715
00:06:06,052 --> 00:06:07,552
You can look at
the number of

716
00:06:07,552 --> 00:06:09,502
days that wildlife
were detected.

717
00:06:09,502 --> 00:06:11,706
So this is not number
of detections.

718
00:06:11,706 --> 00:06:14,947
This is detections
within a 24 hour period.

719
00:06:14,947 --> 00:06:16,972
And then I've
mapped that with

720
00:06:16,972 --> 00:06:18,952
the percent impervious
surface and all of

721
00:06:18,952 --> 00:06:20,602
our sites is

722
00:06:20,602 --> 00:06:22,852
just one variable
that we can look at.

723
00:06:22,852 --> 00:06:24,952
We have 22
different variables

724
00:06:24,952 --> 00:06:26,272
that we've pulled
at this point.

725
00:06:26,272 --> 00:06:28,252
So this is going to
be a lot of work

726
00:06:28,252 --> 00:06:30,172
looking at
correlations between

727
00:06:30,172 --> 00:06:31,851
a lot of different
variables.

728
00:06:31,851 --> 00:06:33,412
And you can tell
by this graph,

729
00:06:33,412 --> 00:06:34,882
this isn't telling
us a whole lot.

730
00:06:34,882 --> 00:06:36,411
The points are
pretty scattered

731
00:06:36,411 --> 00:06:37,582
and you don't really

732
00:06:37,582 --> 00:06:38,991
see much of a correlation.

733
00:06:38,991 --> 00:06:40,882
This might be a case
where looking at

734
00:06:40,882 --> 00:06:42,382
a different radii could

735
00:06:42,382 --> 00:06:44,227
give us a little bit
of a clearer picture.

736
00:06:44,227 --> 00:06:45,592
Or this could be a case

737
00:06:45,592 --> 00:06:47,242
where maybe impervious
surface isn't

738
00:06:47,242 --> 00:06:49,162
the best variable
to predict where

739
00:06:49,162 --> 00:06:51,997
these mezzo predator
species are located.

740
00:06:51,997 --> 00:06:53,992
This is another example

741
00:06:53,992 --> 00:06:55,432
of a variable
we can look at.

742
00:06:55,432 --> 00:06:57,757
This is average number
of housing units.

743
00:06:57,757 --> 00:06:59,662
And so you can
see this one

744
00:06:59,662 --> 00:07:01,461
might be a little
bit more correlated.

745
00:07:01,461 --> 00:07:03,682
You can tell that
predators might be

746
00:07:03,682 --> 00:07:06,262
trending towards areas
with less housing.

747
00:07:06,262 --> 00:07:08,947
But it's still very
preliminary data.

748
00:07:08,947 --> 00:07:10,507
We're going to have
to take another look,

749
00:07:10,507 --> 00:07:12,172
looking at many
different variables

750
00:07:12,172 --> 00:07:13,192
to see which of these are

751
00:07:13,192 --> 00:07:14,422
going to be the
most important

752
00:07:14,422 --> 00:07:16,957
whenever we end up
building our model.

753
00:07:16,957 --> 00:07:19,942
So once we have this model

754
00:07:19,942 --> 00:07:21,982
to tell us where
meso predators are

755
00:07:21,982 --> 00:07:23,707
located potentially
and what kind of

756
00:07:23,707 --> 00:07:25,042
resources they
might be looking

757
00:07:25,042 --> 00:07:26,947
for. How can we use that?

758
00:07:26,947 --> 00:07:29,152
Well, one way is

759
00:07:29,152 --> 00:07:31,162
looking at habitat
corridors.

760
00:07:31,162 --> 00:07:32,542
Habitat corridors are

761
00:07:32,542 --> 00:07:34,702
a pretty hot topic
in urban ecology.

762
00:07:34,702 --> 00:07:36,577
Looking at how these
different animals

763
00:07:36,577 --> 00:07:38,902
are moving throughout
urban space.

764
00:07:38,902 --> 00:07:41,782
And looking at these
different variables

765
00:07:41,782 --> 00:07:44,287
can be super helpful
in telling us

766
00:07:44,287 --> 00:07:46,192
both weirdly animals are

767
00:07:46,192 --> 00:07:47,662
located and
also how we can

768
00:07:47,662 --> 00:07:49,522
build corridors
that are going to

769
00:07:49,522 --> 00:07:51,877
be better suited for
these different species.

770
00:07:51,877 --> 00:07:53,872
What resources
we might need

771
00:07:53,872 --> 00:07:56,286
to include in
urban corridors.

772
00:07:56,286 --> 00:07:59,197
And also how we can best

773
00:07:59,197 --> 00:08:01,672
suit these landscapes for

774
00:08:01,672 --> 00:08:03,997
the species that they're
meant to be serving.

775
00:08:03,997 --> 00:08:05,572
So we can also look at

776
00:08:05,572 --> 00:08:06,982
social impacts
of this data,

777
00:08:06,982 --> 00:08:09,292
looking at where
the animals

778
00:08:09,292 --> 00:08:10,672
are located relative to

779
00:08:10,672 --> 00:08:12,652
people and which
people might be having

780
00:08:12,652 --> 00:08:15,187
the most access with
wildlife species.

781
00:08:15,187 --> 00:08:17,557
There has been work
that shows that

782
00:08:17,557 --> 00:08:19,462
some wealth
demographics may have

783
00:08:19,462 --> 00:08:21,142
more access to
wildlife than others.

784
00:08:21,142 --> 00:08:22,282
And so we want
to make sure

785
00:08:22,282 --> 00:08:24,472
that wildlife
is equitable.

786
00:08:24,472 --> 00:08:25,792
We also want to look

787
00:08:25,792 --> 00:08:26,692
at the flip side of that,

788
00:08:26,692 --> 00:08:28,972
looking at human
wildlife conflict.

789
00:08:28,972 --> 00:08:31,132
Where is their
highest risk

790
00:08:31,132 --> 00:08:31,852
that there could be

791
00:08:31,852 --> 00:08:33,337
a human wildlife conflict?

792
00:08:33,337 --> 00:08:34,732
As these meso
predators are

793
00:08:34,732 --> 00:08:36,652
increasing and
urban space,

794
00:08:36,652 --> 00:08:38,122
it's going to
become more and

795
00:08:38,122 --> 00:08:39,577
more important that we,

796
00:08:39,577 --> 00:08:40,732
that we create space for

797
00:08:40,732 --> 00:08:42,202
them and make sure
that they have

798
00:08:42,202 --> 00:08:44,092
the resources that
they need to alleviate

799
00:08:44,092 --> 00:08:45,532
that human
wildlife conflict

800
00:08:45,532 --> 00:08:46,867
as much as we can.

801
00:08:46,867 --> 00:08:49,342
So with that,
I just want to

802
00:08:49,342 --> 00:08:51,742
thank our partners
in this project.

803
00:08:51,742 --> 00:08:53,632
Everyone has been
great to work with.

804
00:08:53,632 --> 00:08:54,352
We're working with

805
00:08:54,352 --> 00:08:55,926
the organ Wildlife
Foundation,

806
00:08:55,926 --> 00:08:58,162
the Lincoln Park
Zoo, Portland State,

807
00:08:58,162 --> 00:08:58,719
Colin Audubon and
the smart group.