U.S. State-Level eBird Data Summaries

Welcome everybody and thank you all for your interest in our project to help support information needs of state and federal agencies.

Alittle bit of background in who we are, I am Viviana, I’m an assistant director of the center for Avian population studies and the lead of the conservation Science Program.

You will also see on this website a video presentation from Oren who is a research associate or in conservation science program but he’s also a researcher on the status and Trends team that you will learn about.

You will also see work in videos that are the result of the hard work that has been done by Andrew a postdoc in our program and also by our student interns Tristan and Archie.

Believe It or Not Eber turns 20 this year and along with all of these data we have really focused our efforts and our program for the past five years and working with Partners to make sure that all of this scientific information is accessible for informing conservation and management actions included in these data are large volumes of data for each state in the US for example in New York alone we have about 2.9 Million checklists from over 54 000 e-birders for the past 15 years the eBird status and Trends project team has worked on statistical advancements and Innovations and data visualizations to generate information for all species in the U.S and Beyond this has resulted in unprecedented information on bird populations such as this animation relative abundance of the yellow worber throughout its full annual cycle.

We now have information thanks to all these efforts we now have information on the relative abundance at high spatial and temporary resolutions for almost 2000 species worldwide including all 450 species in North America that you can find on our website.

Most recently we have generated information on species Trends population trends at a 27 kilometer resolution which can also be summarized by state by BCR or across the entire range sir conservation science program is focused on making sure that partners and stakeholders understand and have access to this information to best serve conservation and management actions just one example of a recent collaboration how we use eBird status and Trends projects is a recent collaboration with the fish and wildlife service on using eBird relative abundance on Bald Eagle populations we were able to use kind of Max information a Max annual relative abundance which means that the layer that you see on the top left is the relative abundance of bald eagles across the entire full annual cycle summarize in one layer we use this information to define low risk Collision areas in collaboration with the fish and wildlife service for bald eagles to help facilitate the permitting process for wind energy development for this specific project we have used information eBird relative abundance and Trends to create an online platform to make state-level kneeper data summaries available such as maximum year-round abundance that you just saw or the percentage of populations in the breeding season we’ve also developed a workflow to enable annual debate updates of all information we hope that this information is used by state and Wildlife agencies to support their multiple information needs such as potential updates to swap plans in preparation for initiatives such as the hopefully potential passing of recovering America’s Wildlife Act we’d like to thank you again for your interest and we hope you find thisinformation useful.

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I’m Orin Robinson a research associate in the conservation science program within the center for Adrian population studies or caps at the Cornell lab of ornithology.

I’m going to talk about the eBird data that goes into the status and Trends models and how we use various filters within eBird and then statistical methods to account for the biases in citizen science data while we are creating these status Insurance products.

So the first line of defense,when it comes to data quality in eBird and the eBird products are several thousand filters that are working in the background as you are putting in data into eBird.

If we look at the right hand side of the screen there’s an example of one of these filters where

I’ve put in 15 000 Green Wing teal on Cayuga Lake here in Ithaca New York.

Now we often see green Wing teal it’s not it’s not rare to see a green winged teal on Cayuga Lake but it would be an incredibly rare event to see 15000 of them and you can see that this blue box has popped up that was triggered by one of these thousands of filters that are running that suggests that this count is higher than expected for a given location and a given date

it asks for you to provide some comments to show how you got to account of fifteen thousand green Wing teal.

iIn the middle uh panel here I have I have shown that there was a snowy owl near Ithaca New York and again another one of these these boxes has been triggered it shows that you have found a bird that is not on the local eBird checklist and again it asks for you to provide some comments to support your making that identification whether it be describing field marks describing the behavior of the bird what it was doing when you saw it or the you know specific habitat in which you saw the bird uh for both of these pictures would also be great to uh to add to the checklist to further you know validate your observation.

There are again you know 5 000 or more of these internal filters that are running and then after that there are more than 2 000 human data reviewers across the world and more than 20 percent of these checklists are reviewed by a human person if a checklist gets flagged it will almost certainly be reviewed by one of these humans um and they will work with the uh with the eBird Observer to validate that observation.

So now that we have some data that is that has been through those filters and has been validated uh we can download that data and work with it but we also have to handle all the biases that are inherent in Big Data especially citizen science data there are some issues that we need to be cognizant of when using citizen science data that may not be issues in well-structured surveys so first there are people who have an affinity for certain species or groups of species.

This leads to a preferential recording of those species we also know there’s a large variation in the observation process particularly in effort right effort is how long you spend birding how far you travel while you’re doing it the time of day the number of observers that are there and we know that all of these things affect the detectability of certain species or of all species um we also know that most folks are going to sample close to home or close to roads areas of known High biodiversity highly accessible places right this creates a spatial bias in the data there are also temporal biases in this data uh and there are there are a couple of different temporal biases that we see one is that most folks are going to sample when they’re able to right and for most people that is on the weekends or around holidays so we see more data in those time periods on weekends around holidays we also tend to see more data especially in in North America we see more data during the month of May we get more Ebert checklists in May than in any other month and that is for for two reasons one it’s it’s starting to warm up the weather’s very nice um so people just want to be outside, but also that’s when uh the neo-tropical migrants are passing through a large portion of North America is throughout May so that is a second kind of of temporal bias we also know that there are a wide ranging skill levels within citizen science data and a wide range of behaviors of those Observers and we have to account for all of these things when we model this data even after all the filtering we discussed.

So the first step in acquiring some of that noise is to put restrictions on the data that we analyze so essentially here we’re putting uh or imposing rather a protocol on the eBird data there’s a really good r package called awk a UK that makes it easy to do this here you can see a screenshot from that package awk where we have filtered the the time that the birders spent on a checklist the distance they traveled the years over which we want to uh to conduct our

analyzes the number of observers there are many other things you can filter for here and you know you would really just need to filter for what made sense for your analyzes these you know this five hour five kilometers is not set in stone this is just an example.

We also discussed the spatial bias in the data remember that this comes from sampling close to home easily accessible areas places where you know a lot of birds are going to be we address this by spatially sub-sampling the data.

So the example here each dot represents an eBird checklist and what we’ve done is put a grid over that eBird checklist or over all of these eBird checklists and from within each grid cel we select one checklist and run the analyzes on those checklists in our analyzes we will do this hundreds and hundreds of times selecting checklists uh different checklists at each at each

iteration you can also begin to handle temporal

bias here by doing this spatial sub sampling over the period of a week so you would select one checklist per grid cell per week to run your analysis or you know month or three day chunk whatever makes sense for your analysis.

We know there’s a huge difference in the skill levels among e-birders we have people who are brand new to birding all the way to people who have uh you know been birding for a long time and are just incredibly skilled and we have to be able to account for this difference in skill level among individual e-birders.

We do this with checklist calibration index so we quantify the expertise of Citizen scientists we know that this has a large effect on the detectability we know this very seasonally and we know that including it we’ll improve our estimates of distribution and abundance um so now that we have some data that has been you know filtered as it was collected these these various large sources of bias have been accounted for what do we do with all of this data.

So with our status models we take the eBird checklists and translate those into these Dynamic distribution models for over 2 000 species and these models show the abundance distribution by week at a very fine scale spatial resolution and there are some challenges here when you do this so first there’s the ecological process that is what links Birds to particular locations at particular times of the year to account for this ecological process we have many many environmental variables within this model we have variables that describe habitat Types on on the landscape we have variables that are related to urbanization we have variables that are related to Tidal mud flats Islands roads elevation all of these things help account for the variation in the Environmental processes then we have the observation process which is what we we’ve spent the first part of this talking about this is a process by which the Eber data is collected essentially we collect many effort variables like we talked about and we also use those spatial and temporal filtering to help quiet the noise in the observation process within the eBird data.

Another issue is uh stationarity and scaling and this comes up when we we think about a species that may have different habitat associations in different parts of its range or different habitat associations at different times of the year if you were to model the entire range and the entire year all together those habitat types would essentially be fighting for importance within that model here’s an example with Wood Thrush that prefers different habitat Types on the breeding grounds than it does and it’s overwintering grounds to account for this we use this spacio temporal exploratory model framework we divide the data into the boxes that you see on the right and these are not the exact boxes that are used this is just for demonstration purposes so we have hundreds of overlapping boxes in each of those boxes a local model will be run so that way we are capturing the local environmental processes.

In allowing them to emerge within this model we then make our predictions through these overlapping models at various locations so that the estimate at a given location can borrow information from multiple locations throughout the modeled surface the end result then is this Dynamic map of a species relative abundance and distribution at three by three kilometers across its entire range for every week of the year foreign.

We also provide static maps with the seasons averaged all of this data is made freely available for over 2 000 species on the status and Trends website we’ve also recently calculated trends for over 400 species of birds that breed in North America and hope to add more species and regions very soon uh the trends are for the breeding season for each species from 2007 to 2021 and at 27 by 27 kilometers. uh you know a lot of the time what you see with large-scale Trend analyzes are range-wide estimates like we see here with warbling virio looking at this from a range-wide scale you would you would think that the the warbling Vireo is doing fairly well we have a slight increase in its percent change per year which suggests a growing population but we know it’s not growing uniformly and there may be some pockets of decline there may be some parts where it’s growing faster than others.

So we may wish to try to model this trend in a more spatially explicit manner and we also see this done at the regional scale so a state or bird conservation region as we see here in that that type of analysis would look something like this right here we can see that it isn’t just uniform growth across the range there are regions of decline some regions of of large increase uh some regions where there’s less increase than the average what have you now what we have done with eBird is calculate these trends at much much finer scales but also across the entire range of thespecies.

When we zoom in on this warbling vario trend and look at the regional or BCR scale versus the the landscape scale that we are are using with with eBird trends uh we can see the variation the magnitude even within these regions uh the story gets richer here because the the landscape scale more accurately captures population Trends compared to the regions and that’s critical if we want to identify the drivers of the change here the landscape scale is also more actionable by that I mean the information is specific and useful and can directly inform decisions because we can scale down to the level where most conservation decision decisions are made and that is much finer than the BCR scale in this example demonstrates that very well we see that the highlighted State here Wyoming is part of a bird conservation region on on the left that is showing a strong decline of this species however when we look at it from the landscape scale we see that the the part of that BCR that’s driving all of this change is in the far Northwest portion of that bird conservation region and the species is actually growing rather well in the state of Wyoming here

So that concludes this presentation I will be happy to answer any questions or comments that you may have my email is listed here and I thank you for your time.

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Hello and welcome, in this video I will be reviewing the state level data summaries produced using the eBird status and Trends data products that are provided for each state and table form.

To access the data I’m referring, tonavigate to your state by either clicking get your state data and finding your state alphabetically or just by scrolling until you find the appropriate rope.

Once you’re at your State’s row, the tabular data can be downloaded by clicking the left link which should be named tabular data summary.

The file that is downloaded will come as a zipped folder. This is to compress the size of the download the folder will either be automatically unzipped by your browser or you may have to unzip it yourself. Which you can see pictured here on a Mac.

Once you have done this you will be able to open the folder, which you can see pictured here. The folder itself will contain the eBird product terms of use PDF and the status and Trends dot CSV file this .csv file can be opened in Microsoft Excel numbers or any other preferred spreadsheet software you might use.

Now let’s take a look at the actual spreadsheets themselves in this video I will be running through the spreadsheet for Missouri as an example but everything I detail will apply to your State’s data as well.

Firstly it’s important to note which species were included for the analysis in your state these were any species for which the eBird status and Trends data products showed occupancy in your state throughout the year either during breeding non-breeding or migration season.

This was done to ensure that the data summary package would Encompass every species for which we can produce these data this is done intentionally to maximize the support we can provide through these summaries now onto the columns of data you will find in these tables.

First we have included the common name of each species these names follow the Clements taxonomy which is maintained by the Cornell lab of ornithology.

Next we have two categorization columns which have been included to assist you with filtering the species for which we have provided these data.

If your state sent our team a state-specific state wildlife action plan list you will see zeros and ones in this column and if not it will just contain n a’s not to worry if this is the case though as you can always add these in later if you think it will beof use to you.

For the sgcn column we have included zeros and ones if we were provided with a species of greatest conservation need list for your state and similarly Nas If not moving on we have provided some some population proportion estimates for each species the first of these columns is percentage of population to interpret these numbers picture the global population of a species as a big pie.

This column tells you how much of that pie as a percentage occurs in your state during either the breeding season for migratory species or year-round for Resident species.

We have also provided an additional column which is similar but there’s an important twist the percentage of Maximum population weak column gives the percentage of population during the week in which your state has its maximum abundance of each species as an example note here that the American Golden Plover has a nine percent max week percentage of population.

This means that during the week in which Missouri had its highest abundance of American golden Plover nine percent of this species entire population was in the state this is intended to support state agencies in identifying migrants passing through their states by highlighting species for which this state is a vital stopover site during the spring or fall migration.

We also then provide the week of the year in which that maximum abundance of each species occurred for species like this American oyster catcher note that the percentage of population columns here contain zeros and the max weak column contains a one.

This data is likely irrelevant given that American oyster catcher is seen here to have no occurrence in Missouri this tabular data is not all we provide in this data summary package though for each species we have also created a relevant set of maps showing the fine scale distribution of their relative abundance across your state.

Please watch the next video for a full description of these data products and how to interpret them.

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Hello and welcome. In this video I will be reviewing the state level relative abundance and maximum abundance Maps produced using the eBird status and Trends data products.

To access the data I’m referring to navigate to your state by either clicking get your state data and finding your state alphabetically or just by scrolling until you find the appropriate row.

Once you are at your State’s data Row the map data can be downloaded by clicking the the right link which should be called all data, please note that this file is much larger than the data summary file on the left as it contains fine scale maps of each species within your state.

The file that is downloaded will come as a zip folder this is done to compress the size of the download the folder will either be automatically.

Unzipped by your browser or you may have to unzip it yourself which you can see pictured here on a Mac.

Once you have done this you will be able to open the folder as you can see here the folder itself will contain all data summaries we provide specifically you will find the eBird products terms of use PDF the tabulardata.csv file which is reviewed in the previous video along with three neatly organized folders with fine scale state-specific Maps.

As I mentioned earlier this video is intended to explain the relative abundance and maximum abundance Maps which occur in their respective folders please watch the following video to learn more about the stewardship Maps, first let’s take a look at the relative abundance Maps

Within this folder you will find all of the relative abundance Maps produced for your state all these maps are DOT PNG files which can be opened using just about any image viewing software in this folder you will find two types of relative abundance Maps those being breeding season relative abundance Maps which are produced for migratory species that pass through your state which you can see here have the prefix relative abundance breeding and also year-round relative abundance Maps which are produced for non-migratory species that occur in your state.

Here you can see examples of these files with the prefix relative abundance year round now let’s take a look at what these Maps look like here’s an example of a relative abundance map this map is the breeding season mean relative abundance for yellow-breasted chat in Missouri all the relative abundance Maps produced for download in this data package are at the granularity of approximately three kilometers by three kilometers which is the finest granularity produced by ebote status and trans data products you will see the outline of your state in the form of a black border along with the scale to gauge how prevalent the species is relative to elsewhere in its range.

You will also find the percentage of breeding population in the information panel along the bottom edge of the map in this particular map you can see that the southern portion of Missouri has a higher relative abundance of the yellow-breasted chat than the northern half of the state in other words purples and blues indicate higher relative abundance while oranges and yellows indicate lower relative abundance while ever so slightly different all of.

This also holds true for the year-round relative abundance maps in the same folder the only difference being that the data in those Maps is the mean relative abundance across the can of the year let’s now shift over to the other set of abundance Maps the maximum abundance Maps like the relative abundance Maps these.

Maps also come in dot PNG format in this folder you will find a map for every species for which eBird status and Trends data products showed any occurrence within your state here’s an example maximum abundance map, this map is the cellwise maximum relative abundance map for bay-breasted warbler in New York these Maps can be a bit tricky to understand so I’ll take a few moments to explain them using some Nifty animations to help explain why we produced these-

Maps let’s look at the breeding season relative abundance map for the same species and State here you can see that the breeding season relative abundance map barely shows any breeding locations for babe rested warbler in New York and while this is true that neglects to identify the incredible numbers of the species that migrate through the state each year moving through almost every cell in the state this is where maximum maximum abundance maps come in to understand just how they are made I have a fun analogy that we can use.

First let’s look at the relative abundance map of the species in each week throughout the year in this animation you can see the species migrating North in the spring and in the fall you can see them returning South now for that analogy, the maximum abundance maps are the equivalent of pressing each of these weekly relative abundance frames into one of these impression boards that you can see pictured here.

Each week presses into the board and it stores the maximum relative abundance at each cell throughout the year looking at this in the form of an animation watch The Right image develop as the year progresses in the spring most cells light up and hold their values through the summer and then in the fall some of those cells get even darker that is exactly how the maximum maximum abundance maps are created the maps show the cell-wise maximum relative abundance across the entire year meaning that cells with higher values have more individual Bay breasted warblers at some point in the year then at any point in the year in all cells with lower values these maps are thus an attempt to visualize the overall spatial extent of where the species passes through or spends time in your state throughout the year.

This concludes the video on two on the two types of relative abundance Maps you will find in your State’s data summary package please watch the next video to learn more about the stewardship maps that complete the data provided in this download.

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Welcome, this video is about the state level stewardship connections and uniqueness of stewardship connections maps that are provided on this website

to access these data products click the get your state data button or scroll to the Links at the bottom of the page.

Find your state and click the other day the link to download a zip file that contains these Maps along with all other data products for your state

these maps come in both PNG images and Tif file formats which you can easily load into your GIS software.

Our goal for providing these products is to fill in gaps for Regions without much information on migratory connections stewardship connections Maps shown on the left here highlight options for conservation Investments within and Beyond the US during the non-breeding season.

Because many species winter in the same regions these Maps may look similar between different States since we wish to emphasize uniqueness as well we provide uniqueness of stewardship connections Maps shown on the right here which emphasize a given State’s unique ability to contribute to certain areas

these maps are best used in Tandem and are meant to support full annual cycle conservation of migratory birds and informed corresponding stewardship actions.

Here’s how we select the species to put in these Maps. we start with migratory species that have high quality eBird status and Trends data for both breathing and non-breeding seasons.

Out of these we select species for which at least one percent of the species total breeding population breeds in a given State and at least 85 percent of its breeding population in said State migrates out during the non-breeding season.

You can find a complete list of species used to create these maps for your state in the same zip file as the maps themselves the species list is a CSV file which you can open in Excel now we’ll demonstrate how we create these Maps so you can get a better understanding of what they represent stewardship connections maps are essentially weighted sums of species non-breeding abundances with the percentage of a species breeding population in a given State as the weight for example for the state of Missouri we would multiply the percent breeding population of wood ducks in Missouri with its map of non-breeding abundance multiply the percent breeding population of yellow boat Cuckoos with its map of non-breeding abundance do the same for all other species in Missouri species list and sum them all to create the map on the right here

if a species has a higher percent breeding population in a state it’s non-breeding abundance will contribute more to that State’s stewardship connections.

It is important to note however that the non-breeding connections shown in these maps are at the species and not a population level in other words the individual birds that winter in the areas highlighted on the map here might not necessarily be the same ones that breed in Missouri they are however members of the same species.

Hence this map tells us that the non-breathing abundance of species that breathe in Missouri weighed by their percent breeding population as highest in areas such as Florida, the Yucatan Peninsula and Venezuela.

This information can be supplemented by on-the-ground migration tracking data if available.

Uniqueness of stewardship connections Maps represent a given State’s proportion of stewardship connections relative to all U.S states to produce these Maps we first calculate a total by summing the stewardship connections maps of all U.S states.

We add Alaska’s stewardship connections to Arkansas and so on then for a given state Missouri in this case, we divided stewardship connections by the total that generate its uniqueness of stewardship connections map which indicates areas where Missouri has a high proportion of stewardship connections relative to other states.

At first sight this map may look similar to the stewardship connections map shown earlier to clearly see what each map emphasizes it is best to compare them side by side.

As we can see areas where Missouri has strong stewardship connections such as Florida do not always have high uniqueness values, this is because many other states also have strong connections to Florida a popular winner in place, thus Missouri does not have a high proportion of stewardship Connections in Florida relative to other states.

On the other hand areas where Missouri has weak stewardship connections such as Northeastern Peru and Northwestern Brazil might end up with high uniqueness values, even though Missouri has limited stewardship connections to the area they still account for a high proportion of U.S stewardship Connections. In other words for this area the way that non-breeding abundance of species that breed in other states is low compared to Missouri

Again it is important to note that these maps are the species and not population level ultimately they are meant to be used for reference in conjunction with other resources when making stewardship decisions.

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