Phase II: Using Our Toolbox

class: center, middle, inverse, title-slide

.title[
# Phase II: Using Our Toolbox
]
.subtitle[
## Module 7: Birds of a Feather
]
.author[
### Dr. Christopher Kenaley
]
.institute[
### Boston College
]
.date[
### 2025/10/11
]

---

# In class today

<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.14.0/css/all.min.css">

.pull-left[
Today we'll ....

- Kick off Project 7 (The last module)
- Explore trans-Gulf migration (TGM)
- Explore the logistic curve

]

.pull-right[
![](https://resources.tidal.com/images/585b7fda/ecf7/4b71/b24d/ac41523e5bb2/1280x720.jpg)
]

---
# Module 8: Birds of a feather

---
#Module 8: Birds of a feather
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**Overview of TGM**

The Gulf of Mexico spans ~1,000 km across — a major ecological barrier.

Many Neotropical–Nearctic migrants (e.g. warblers, thrushes, orioles) cross it twice yearly.

Crossing requires continuous flight over open water (18–24 h).
]

.pull-right[
**Energetic Demands**

Birds accumulate large fat stores (up to 40% body mass) before departure.

Departure timing depends on *wind assistance* and weather fronts.

High mortality risk if headwinds, storms, or energy stores insufficient.

]

---
#Module 8: Birds of a feather
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**Ecological Drivers**

Spring migration: reach breeding grounds early; strong selection for speed.

Fall migration: less time pressure; routes often longer but safer.

Stopover sites (Yucatán, Louisiana) are critical for refueling and survival.
]

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**Species & Strategies**

Small passerines (warblers, buntings): nonstop nocturnal flight.

Larger species (hawks, herons): circum-Gulf routes over land.

Some shorebirds use coastal stepping stones (Cuba–Florida Keys chain).

**Conservation Context**

Habitat loss at stopover sites (e.g., coastal marshes) threatens populations.

Predicting responses to climate change and storm frequency is a key focus.

]

---
# eBird and GBIF

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**What is eBird?**

- A global citizen science platform for bird observation data, freely available for research, etc.

- Developed by the Cornell Lab of Ornithology (launched 2002).

- Over 1.5 billion bird records from more than 800,000 contributors.

**What Data Are Collected?**

- Species observed

- Count (abundance)

- Location (GPS or hotspot), date, time, duration, and effort

- Optional photos, audio, or breeding codes

]

.pull-right[
<img src="https://s3.amazonaws.com/is-ebird-wordpress-prod-s3/wp-content/uploads/sites/70/eBird-2.0-Android-1040x592.jpg" width="500">
]

---
# eBird and GBIF

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**Who Provides the Data?**

- Birdwatchers (“citizen scientists”) log checklists via
the eBird mobile app or website.

- Submissions undergo automated filters and expert review.

- Contributors gain feedback, stats, and maps of their observations.

**Why It Matters**

- Enables real-time (kinda) tracking of migration, abundance, and phenology.

- Supports ecological and land-use modeling.

- Bridges the gap between public engagement and scientific discovery.

]

.pull-right[
<img src="https://s3.amazonaws.com/is-ebird-wordpress-prod-s3/wp-content/uploads/sites/70/eBird-2.0-Android-1040x592.jpg" width="500">
]

---
# eBird and GBIF
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**What is GBIF?**

- An international open-data infrastructure for biodiversity records.

- Established in 2001 under the OECD Global Science Forum.

- Provides free, standardized access to >2 billion species occurrence records worldwide.

- Integrates data from museums, herbaria, research projects, and citizen science (including eBird).
]

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**Core Mission**

To make all biodiversity data findable, accessible, interoperable, and reusable (FAIR principles).

Supports global monitoring, species distribution modeling, and policy frameworks like IPBES and the Convention on Biological Diversity.

]

---
# eBird and GBIF
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**What Data Are Included**

- Species occurrences (presence records)

- Taxonomic names & classifications

- *Georeferenced* coordinates

- *Temporal data* (collection dates)

- Environmental & sampling metadata

**Who Contributes**

Museums, universities, government agencies, NGOs, and citizen-science platforms (e.g., iNaturalist, eBird, iDigBio).
]

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**Why It Matters**

- Enables global biodiversity synthesis and macroecological research (us!).

- Informs conservation priorities, invasive species management, and climate change responses.

]
---
# When do birds arrive in Mass?

---

# When do birds arrive in Mass?

### eBird and GBIF

``` r
yr <- c(2021,2022,2023)

bhv_l <- list()

for(i in yr){
 bhv_l[[i]] <- occ_data(scientificName = "Vireo solitarius",
                year=i,
                month="3,6",
                limit=1000,
                country="US",
                basisOfRecord = "HUMAN_OBSERVATION",
                stateProvince="Massachusetts")[[2]] %>% 
   select(individualCount,
          year,month,day,
          decimalLongitude,
          decimalLatitude)
}

bhv <- do.call(rbind, bhv_l)
```

---
# When do birds arrive in Mass?

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``` r
mass <- ne_states(country = 
                    "United States of America",
                  returnclass = "sf") %>% 
  filter(name=="Massachusetts")

p <- mass %>% 
  ggplot() +
  geom_sf()+
  geom_point(data=bhv,
             aes(decimalLongitude,
                 decimalLatitude,
                 col=year))
```
]

.pull-right[

``` r
print(p)
```

![](3140_f25_11-10_files/figure-html/unnamed-chunk-4-1.png)
]

---

# When do birds arrive in Mass?

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### The logistic curve
  
![](https://www.billharlan.com/papers/logistic/fig1.png)
]

---
# When do birds arrive in Mass?

### The logistic curve

``` r
bhv_arrive <- bhv%>%
  mutate(n=1:n()) %>%
  group_by(n) %>%
  mutate(date=as.Date(paste0(year,"-",month,"-",day)),
         j.day=julian(date,
                      origin=as.Date(paste0(unique(year),"-01-01")))
         )%>%
  na.omit() %>%
  group_by(year,j.day,date)%>%
  reframe(day.tot=sum(individualCount,na.rm=T))%>%
  group_by(year)%>%
  mutate(prop=cumsum(day.tot/sum(day.tot,na.rm = T)))
```

---
# When do birds arrive in Mass?

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### The logistic curve

``` r
p <- bhv_arrive %>% 
  ggplot(aes(j.day,prop,col=as.factor(year))) +
  geom_point()
```
]

.pull-right[

``` r
print(p)
```

![](3140_f25_11-10_files/figure-html/unnamed-chunk-7-1.png)
]

---
# When do birds arrive in Mass?

### The logistic curve

``` r
bhv_pred <- bhv_arrive%>%
  group_by(year)%>%
 reframe(
   pred=predict(
     nls(prop~SSlogis(j.day,Asym, xmid, scal)),
     newdata=data.frame(j.day=min(j.day):max(j.day))),
   j.day=min(j.day):max(j.day),
  )%>%
  left_join(bhv_arrive%>%dplyr::select(j.day,date,prop)) 
```

---
# When do birds arrive in Mass?

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### The logistic curve

.pull-right[

non-linear least squares using `nls` package

`SSlogis`:

- vector `input` *
- asymptote `Asym`*
- inflection point `xmid` *
- scale `scale` *

``` r
SSlogis(input,Asym, xmid, scal)
```
 
 * functions that compute these values
 ]
 
---
# When do birds arrive in Mass?

.pull-left[

### The logistic curve

``` r
p <- bhv_pred %>% 
  ggplot(aes(j.day,prop),alpha=0.3) +
  geom_point()+
  geom_line(aes(j.day,pred,group=year),
            col="blue")
```
]

.pull-left[

```
## Warning: Removed 14 rows containing missing values or values outside the scale range
## (`geom_point()`).
```

![](3140_f25_11-10_files/figure-html/unnamed-chunk-12-1.png)
]