ArticlePDF Available

Species-environment associations and predicted distribution of Black Oystercatcher breeding pairs in Haida Gwaii, British Columbia, Canada

Authors:

Abstract and Figures

We present a species distribution model (SDM) for prediction of Black Oystercatcher (Haematopus bachmani) breeding pair occurrence in Haida Gwaii, British Columbia. Boosted regression trees, a machine learning algorithm, was used to fit the model. In total, 14 predictors were selected a priori through development of a conceptual model. Breeding pair occurrence data were compiled from two available surveys conducted in 2005 and 2010 (545 km of shoreline surveyed in total). All data were aggregated to common model units (vector polyline shoreline segments approximately 100 m in length), which approximate breeding territory size. The final model, which included eight predictors (distance to treeline, island area, wave exposure, shoreline type, intertidal area within 50 m, segment length, rat occurrence, and intertidal area within 1000 m), had excellent predictive ability assessed by 10-fold cross-validation (AUC = 0.89). Predictive ability was reduced when the model was trained and tested on spatially (AUC = 0.86) and temporally (AUC = 0.83) independent data. Distance to treeline and island area had greatest influence on the model (RI = 41.5% and RI = 36.7%, respectively); we hypothesized that these predictors are related to avoidance of predators. Partial dependence plots revealed that breeding pairs tended to occur: further from the treeline, on small islands, at high wave exposures, at moderate intertidal area, on bedrock or gravel shoreline types, and on islands without rats. However, breeding pairs tended not to occur on very small islands and at very high wave exposures, which we hypothesize to reflect avoidance of nest washout. Results may inform local conservation and management efforts, i.e., from predictive maps, and eventual development of a high-resolution (~100 m) model for prediction of Black Oystercatcher breeding pairs at a regional scale. Further, methods and GIS data sets developed may be used to model distribution of other coastal species in the region.
Content may be subject to copyright.
VOLUME 12, ISSUE 2, ARTICLE 9
Dalgarno, S., J. E. Mersey, Z. Gedalof, and M. Lemon. 2017. Species-environment associations and predicted distribution of Black Oystercatcher
breeding pairs in Haida Gwaii, British Columbia, Canada. Avian Conservation and Ecology 12(2):9. https://doi.org/10.5751/ACE-01094-120209
Copyright © 2017 by the author(s). Published here under license by the Resilience Alliance.
Research Paper
Species-environment associations and predicted distribution of Black
Oystercatcher breeding pairs in Haida Gwaii, British Columbia, Canada
Sebastian Dalgarno 1, Janet E. Mersey 1, Ze'ev Gedalof 1 and Moira Lemon 2
1University of Guelph, 2Canadian Wildlife Service
ABSTRACT. We present a species distribution model (SDM) for prediction of Black Oystercatcher (Haematopus bachmani) breeding
pair occurrence in Haida Gwaii, British Columbia. Boosted regression trees, a machine learning algorithm, was used to fit the model.
In total, 14 predictors were selected a priori through development of a conceptual model. Breeding pair occurrence data were compiled
from two available surveys conducted in 2005 and 2010 (545 km of shoreline surveyed in total). All data were aggregated to common
model units (vector polyline shoreline segments approximately 100 m in length), which approximate breeding territory size. The final
model, which included eight predictors (distance to treeline, island area, wave exposure, shoreline type, intertidal area within 50 m,
segment length, rat occurrence, and intertidal area within 1000 m), had excellent predictive ability assessed by 10-fold cross-validation
(AUC = 0.89). Predictive ability was reduced when the model was trained and tested on spatially (AUC = 0.86) and temporally (AUC
= 0.83) independent data. Distance to treeline and island area had greatest influence on the model (RI = 41.5% and RI = 36.7%,
respectively); we hypothesized that these predictors are related to avoidance of predators. Partial dependence plots revealed that breeding
pairs tended to occur: further from the treeline, on small islands, at high wave exposures, at moderate intertidal area, on bedrock or
gravel shoreline types, and on islands without rats. However, breeding pairs tended not to occur on very small islands and at very high
wave exposures, which we hypothesize to reflect avoidance of nest washout. Results may inform local conservation and management
efforts, i.e., from predictive maps, and eventual development of a high-resolution (~100 m) model for prediction of Black Oystercatcher
breeding pairs at a regional scale. Further, methods and GIS data sets developed may be used to model distribution of other coastal
species in the region.
Association espèce-environnement et répartition prédite de couples d'Huîtrier de Bachman nichant sur
Haida Gwaii, Colombie-Britannique, Canada
RÉSUMÉ. Nous présentons un modèle de répartition d'espèce pour prédire l'occurrence de couples nicheurs d'Huîtrier de Bachman
(Haematopus bachmani) sur Haida Gwaii, en Colombie-Britannique. La technique de boosting d'arbre de régression, un algorithme
d'apprentissage automatique, a été utilisée pour ajuster le modèle. Quatorze variables explicatives ont été sélectionnées a priori lors de
l'élaboration du modèle conceptuel. Les données d'occurrence de couples nicheurs ont été compilées à partir de deux relevés effectués
en 2005 et 2010 (545 km de rive inventoriés au total). Toutes les données ont été regroupées en une unité commune de modélisation
(segment de rive en vecteur polyligne, de 100 m de longueur environ), correspondant approximativement à la taille du territoire de
nidification. Le modèle final, qui comprenait huit variables explicatives (distance à la forêt, superficie de l'île, exposition aux vagues,
type de rive, superficie intertidale à l'intérieur de 50 m, longueur du segment, occurrence de rats, superficie intertidale à l'intérieur de
1000 m), avait une excellente capacité de prédiction, évaluée par validation croisée répétée 10 fois (surface sous la courbe [AUC] = 0,89).
La capacité de prédiction était plus faible lorsque le modèle était entraîné et testé avec des données indépendantes spatialement (AUC
= 0,86) ou temporellement (AUC = 0,83). La distance à la forêt et la superficie de l'île ont eu l'effet le plus grand sur le modèle (influence
relative [RI] = 41,5 % et RI = 36,7 %, respectivement); nous pensons que ces variables sont reliées à l'évitement des prédateurs. Les
graphiques de dépendance partielle ont révélé que les couples nicheurs avaient tendance à se trouver plus près de la limite forestière,
sur de petites îles, avec une exposition importante aux vagues, avec une superficie intertidale modérée, sur une rive rocheuse ou de
gravier et sur des îles sans rats. Toutefois, les couples semblaient éviter les très petites îles et l'exposition très importante aux vagues, ce
qui nous fait croire qu'ils évitent que leur nid ne soit emporté par les vagues. Nos résultats peuvent servir à orienter les activités locales
de conservation et de gestion, à partir des cartes de prédiction, et à l'élaboration éventuelle d'un modèle à haute résolution (~100 m)
pour prédire l'occurrence de couples nicheurs d'Huîtrier de Bachman à l'échelle régionale. De plus, les méthodes et les jeux de données
SIG que nous avons élaborés peuvent être utilisés pour modéliser la répartition d'autres espèces côtières dans la région.
Key Words: Black Oystercatcher; boosted regression trees; British Columbia; coastal; Gwaii Haanas; Haematopus bachmani; Haida
Gwaii; predictive model; species distribution model; wave exposure
Address of Correspondent: Sebastian Dalgarno, 16443 Tow Hill Rd., Massett, BC , Canada, V0T 1M0, seb.dalgarno@gmail.com
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
INTRODUCTION
The Black Oystercatcher (Haematopus bachmani) is a long-lived
shorebird that breeds annually from May–August along the west
coast of North America, from Baja California, Mexico, to the
Aleutian Islands, Alaska, USA. Breeding pairs establish and
aggressively defend territories, generally along rocky coastlines,
and forage exclusively in the intertidal zone. Black Oystercatchers
prey on a wide variety of invertebrate species, although limpets
(Lottiidae spp.) and mussels (Mytilus spp.) make up the majority
of their diet (Andres and Falxa 1995).
There is concern over the conservation status of the Black
Oystercatcher (Tessler et al. 2014) because of relatively restricted
habitat requirements (Tessler et al. 2014), small population size
(8300–12,500; Andres et al. 2012), vulnerability to a number of
coastal environmental threats, e.g., oil spill, introduced predators,
human disturbance, and ocean acidification (Sloan and Bartier
2006), and expected contraction of breeding range due to climate
change (31% by 2080; Langham et al. 2015). There is also interest
in monitoring Black Oystercatcher breeding populations because
of their role as indicators of overall intertidal ecosystem health
(Sloan and Bartier 2006, Carlson-Bremer et al. 2010) and recovery
of the endangered Northern Abalone (Haliotis kamtschatkana;
Bergman et al. 2013).
To predict vulnerability to climate change impacts, to identify
important areas for conservation activities, i.e., monitoring or
protection, and to improve understanding of the conservation
status of the Black Oystercatcher, there is a need for improved
breeding distribution models (“geospatial habitat models,
“geospatial description of good-to-optimal breeding habitat”) as
a basis for prediction (Tessler et al. 2014, Weinstein et al. 2014).
These models may be referred to broadly as species distribution
models (SDMs). SDMs statistically or physiologically relate
species occurrence data to environmental covariates (predictors)
to predict species distributions (Guisan and Zimmermann 2000)
and are increasingly being used as tools for conservation
management in coastal environments (e.g., Lindegarth et al. 2014).
To date, no SDM has been developed to predict distribution of
Black Oystercatcher breeding pairs at a landscape scale.
Several studies have compared habitat features of Black
Oystercatchers measured in situ at known breeding sites vs. random
sites (e.g., Vermeer et al. 1992), or have investigated the relationship
between reproductive success and habitat quality (e.g., Hazlitt and
Butler 2001). However, lack of environmental data covering
unsampled shorelines in these studies precludes prediction.
McFarland (2010) compared remotely sensed habitat features at
known breeding sites vs. random sites in Alaska, but did not
generate predictions at unsurveyed shoreline. In California
(Weinstein et al. 2014) and Alaska (A. Poe and B. A. Andres, 1999,
unpublished manuscript), mean breeding density at different
shoreline types was calculated to estimate population size and
identify suitable habitat within a GIS. The British Columbia
Marine Conservation Analysis program collated and mapped
observed breeding pairs from various historical and current
surveys at a regional scale, but these maps (available online) do not
provide information at unsurveyed shorelines. Finally, probability
of observation models developed by the Breeding Bird Atlas of
British Columbia predict breeding Black Oystercatcher
distribution from a set of distal predictors (latitude, longitude,
aspect, elevation, slope) across B.C. at 10 km resolution, a scale
generally not useful for landscape-level conservation
management (Richmond et al. 2015).
Here, we present a SDM for predicting Black Oystercatcher
breeding pair occurrence at a landscape scale (i.e., 10–200 km
extent, 100 m resolution; Wisz et al. 2013) in southeastern Haida
Gwaii, British Columbia, Canada. British Columbia is estimated
to hold 10–20% of the global breeding population (Tessler et al.
2014), approximately one-third of which are thought to inhabit
Haida Gwaii (Harfenist et al. 2002). The study area serves as a
useful location to test various aspects of model and predictor
performance because of availability of a rich set of occurrence
and environmental data. Also, because the habitat is relatively
undisturbed and in the core of the Black Oystercatcher range, the
SDM should provide insights into its breeding habitat
requirements throughout much of its range (within the limits of
sampled environmental space). Prior to model fitting, we
developed a conceptual model for breeding pair occurrence based
on a review of relevant literature on breeding biology and habitat;
feedback was received from several experts and local practitioners
(Dalgarno 2016). This feedback informed our predictor selection,
model interpretation, and improved our understanding of the
model’s limitations. In general we expect that important
exogenous factors influencing breeding pair occurrence are either
directly or indirectly related to (1) prey abundance and availability,
and (2) reproductive failure and mortality of adults. Endogenous
and observer processes influencing observed occurrence are
discussed, although not modeled explicitly.
The main objectives of this study are the following: (1) to test the
use of several GIS-derived and remotely sensed predictors to
predict breeding pair occurrence at a landscape scale; (2) to
identify important species-environment associations through
interpretation of species-response curves and relative influence
of individual predictors on model performance; (3) to evaluate
performance (discrimination) and spatial and temporal
transferability of the model; and (4) to predict distribution at
unsurveyed shoreline in southeastern Haida Gwaii.
METHODS
Study area
The distribution of Black Oystercatcher breeding pairs was
predicted on 1423 km of shorelines in southeastern Haida Gwaii,
an archipelago located approximately 100 km west of the British
Columbia mainland (Fig. 1). Most shoreline lay within Gwaii
Haanas National Park Reserve, National Marine Conservation
Area Reserve, and Haida Heritage Site (Gwaii Haanas) or K'uuna
Gwaay Conservancy and Haida Heritage Site (K'uuna Gwaay;
also known as Laskeek Bay). The study area was confined to the
Hecate Strait ecosection (Zacharias et al. 1998), as far north as
Cumshewa Inlet. These boundaries were chosen to avoid
extrapolation into unsampled geographic and environmental
space: most notably, higher wave exposures and different
oceanographic and climatic conditions west of the study area and
increasing human disturbance north of the study area. The Hecate
Strait ecosection is characterized by shallow to photic water depth
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
(over 20% 0–20 m), high wave exposure, high subsurface relief,
low current speeds, and a mixture of sand and hard bottom
substrate (Zacharias et al. 1998). Within the study area, the
shoreline is primarily bedrock (38%) and mixed bedrock/sediment
(26%; Howes et al. 1994). Of 846 islands in the study area, 80%
are less than 1 ha, 32% are less than 0.1 ha, and 28% are forested.
Dominant wind directions recorded at Cumshewa Island, the
nearest weather station, are southeasterly and, to a lesser extent,
northwesterly.
Fig. 1. Study area. Shoreline surveyed in 2010 is shown in red
(Laskeek Bay and Juan Perez); shoreline surveyed in 2005 is
shown in blue (Gwaii Haanas, south of Juan Perez); and
unsurveyed shoreline over which breeding pair occurrence was
predicted is shown in black. Inset shows study area in relation
to Haida Gwaii and British Columbia mainland.
Model unit creation
We developed a loosely coupled, vector-based (polyline) modeling
approach. Methods used to create model units were informed by
the need to integrate ShoreZone attributes and other GIS data
sets. ShoreZone used 1:5000 aerial video captured in 1991–1992
to create 1:50,000 scale digital physical shoreline maps delineating
discrete, systematically described “units” of homogenous
shoreline type (34 coastal classes). Units were also assigned a
biological wave exposure class (six ordinal classes), and a
designation of “absent, “patchy,” or “continuous” for
“biobands” of highly visible species or species assemblages (for
details on how qualitative ShoreZone classes were defined see
Sloan and Bartier 2006). To maintain the boundaries of
ShoreZone units whilst creating a model unit at higher and more
consistent resolution (ShoreZone units range from 10 m to 13,000
m in length), we split ShoreZone units into “segments” with a
length as close to 100 m as possible using an automated procedure
in a GIS (e.g., a “unit” of 321 m was split into 3 segments of 107
m). The model unit size was chosen to approximate mean Black
Oystercatcher breeding territory size (Nysewander 1977, Hazlitt
2001). All occurrence and environmental data, which were of
various data types (Table 1), were aggregated to each model unit
in a GIS.
Breeding pair occurrence
Occurrence data were compiled from two available surveys
completed 22–29 June 2005 and 6–10 June 2010 in two separate
regions, by Canadian Wildlife Service (CWS) and Laskeek Bay
Conservation Society (LBCS), respectively (Fig. 1). This timing
coincides with a period of relatively minimal permanent breeding
pair ingress or egress as territories are usually established mid-
May and fledging occurs July–August; despite difference in survey
date, the use of both data sets was therefore justified (Vermeer et
al. 1992, Hazlitt 1999, Hipfner et al. 2012). Of 1423 km of
shoreline within the study area, LBCS and CWS surveys covered
approximately 164 km (12%) and 381 km (27%) in the northern
(Juan Perez sound and Laskeek Bay) and southern (south of Juan
Perez) parts of the study area, respectively. Note that survey routes
covered both poor and good quality habitat and were completed
prior to commencement of rat eradication efforts in Gwaii
Haanas.
In both CWS and LBCS surveys, at least two observers scanned
the shoreline by boat for territorial Black Oystercatcher breeding
pairs. Boats remained approximately 50 m from land and travelled
at a speed of less than 11 km/hr. Surveys were not conducted in
rainy (except in light rain) or windy/choppy (> 15 knots)
conditions to improve detectability. Birds were deemed territorial
if they were observed to exhibit two or more key behavioral
criteria: sentry behavior, e.g., one or both birds sitting in a location
within the territory where they have a good view of surroundings;
alarm behavior, e.g., crouching, scurrying, to draw attention away
from nest; group size, e.g., commonly as a pair, particularly when
alarmed; location, e.g., clearly associated with potential breeding
habitat. Upon finding a territorial pair, shoreline was surveyed
by foot to search for evidence of a scrape, chicks, or eggs. Any
areas previously occupied by a breeding pair were also searched
on foot. In CWS surveys, more poor quality habitat was surveyed,
e.g., sand beaches, although these sections were scanned from a
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
Table 1. Predictors hypothesized to influence Black Oystercatcher (Haematopus bachmani) breeding pair occurrence at shoreline segments,
selected a priori from a conceptual model and constrained by data availability. Scale of Aggregation: original resolution and geometry
of the data before aggregated to shoreline segments; Retained: Indicates whether predictor was retained following pairwise correlation
analyses (if rho > 0.7; the least ecologically relevant predictor was removed).
Biological
Function
Predictor Description Scale of
Aggregation
Data Type Coverage Source Retained
NA SegLength Length of model unit, i.e.,
shoreline segment
Segment Continuous NA Dalgarno (2016) Yes
Prey ShoreType ShoreZone coastal class
reclassified into 10 repetitive
shore types
ShoreZone unit Categorical
(10 classes)
B.C. (Harper et al. 1994) Yes
Mussel ShoreZone “bioband” for
California Mussel (Mytilus
californianus)
ShoreZone unit Ordinal
(3 levels)
B.C. (Howes et al. 1994) Yes
Fucus ShoreZone “bioband” for
rockweed (Fucus spp.)
ShoreZone unit Ordinal
(3 levels)
B.C. (Howes st al. 1994) Yes
BioExp Wave exposure estimate,
validated with in situ biological
data
ShoreZone unit Ordinal
(5 levels)
B.C. (Howes st al. 1994) No
Slope Estimated slope of intertidal
area
ShoreZone unit Ordinal
(3 levels)
B.C. (Howes st al. 1994) Yes
IT50 Total intertidal area within a
circular 50 m buffer
10 m points Continuous Study Area Dalgarno (2016) Yes
IT200 Total intertidal area within a
circular 200 m buffer
10 m points Continuous Study Area Dalgarno (2016) No
IT500 Total intertidal area within a
circular 500 m buffer
10 m points Continuous Study Area Dalgarno (2016) No
IT1000 Total intertidal area within a
circular 1000 m buffer
10 m points Continuous Study Area Dalgarno (2016) Yes
Fetch Proxy for wave exposure. Fetch
summed at 10 degree intervals.
10 m points Continuous Haida Gwaii Dalgarno (2016) Yes
TreeDist Distance to nearest treeline 10 m points Continuous Study Area Dalgarno (2016) Yes
HumanDist Distance to nearest site of
anthropogenic disturbance
10 m points Continuous Study Area Dalgarno (2016) Yes
IslandArea Area of island polygons Island polygon Continuous B.C. (Howes st al. 1994) Yes
Nest
Failure/
Mortality
RatStatus Detected or known occurrence of
R. Rattus or R. Norvegicus in
2010 (prior to rat eradications)
Island polygon Categorical
(2 classes)
Study Area Dalgarno (2016) Yes
farther distance (up to 200 m) and at greater boat speed (Moira
Lemon, July 2016, personal communication).
During surveys, GPS coordinates were taken at the location of a
territorial pair or nest-site. GPS coordinates that were not available
for 11 of 91 sites in CWS surveys were determined post hoc from
satellite imagery and field notes. Coordinates were imported into
a GIS and assigned to the nearest shoreline segment (see below)
using a tool within ArcGIS 10.2. Any shoreline segment with at
least one territorial breeding pair was deemed present. Any
shoreline segment that was surveyed but did not have a territorial
breeding pair present was deemed absent. In 12 cases, breeding
pairs were “moved” to an incorrect segment. This occurred when
ShoreZone shoreline resolution was too low to accurately represent
small islets. The actual nest site location, i.e., small islet, was visible
from high-resolution satellite imagery, but was absent in the
ShoreZone data. These cases were removed prior to analysis.
Environmental data
Fourteen potential predictors were selected a priori (Table 1),
following development of a conceptual model (Dalgarno 2016).
Predictors were (1) linked as directly as possible to some known or
hypothesized biological mechanism; (2) appropriate to the scale
of study; and (3) available for the study area at sufficient quality,
coverage, and resolution.
Several predictors were available as attributes within the
ShoreZone shoreline mapping system (ShoreZone): biological
wave exposure index (BioExp) comprised 5-level ordinal estimates
of wave exposure (protected, semiprotected, semiexposed,
exposed, very exposed), validated with in situ biological data;
shoreline type (ShoreType) comprised 10 classes, which were
shoreline types reclassified from 34 coastal classes (Harper et al.
1994; Appendix 1); slope of the intertidal area (Slope) comprised
3-level ordinal estimates (5–20, 20–50, > 50); occurrence of mussel
beds (Mytilus californianus) on a 3-level ordinal scale (absent,
patchy, continuous), visible from aerial video (Mussel);
occurrence of rockweed beds (Fucus spp.) on a 3-level ordinal
scale (absent, patchy, continuous), visible from aerial video
(Fucus); Area of islands (IslandArea) was calculated from island
polygons.
A quantitative wave exposure index (Fetch) was available from
the haidawave R package, which was codeveloped by the lead
author of this study (Dalgarno and Thorley 2017). This index,
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
which uses algorithms developed by Murtojärvi et al. (2007), is
the sum of fetch calculated at 10 radiating lines for each point
spaced 10 m along the shoreline. Distance to forest cover
(TreeDist) was determined from treeline, i.e., transition from trees
to bare rock /shrub/grass, digitized at 1:5000 scale from 2010
cloud-free Digitalglobe satellite imagery freely available in
ArcGIS 10.2 at resolution ranging from 0.5 m to 2.5 m. Distance
to human disturbance (HumanDist) was calculated by identifying
sites of heavy boat traffic and human habitation, i.e., from
tourism, seasonal accommodations, and monitoring or research
activities. Intertidal area within a circular buffer with a radius of
50 m (IT50), 200 m (IT200), 500 m (IT500), and 1000 m (IT1000)
were calculated from intertidal area polygons available from
Canadian Hydrographic Service. Different circular buffer sizes
were intended to account for various hypotheses regarding Black
Oystercatcher foraging distance. Data on occurrence of rats
(Rattus rattus or Rattus norvegicus) at islands in 2010 (RatStatus)
were obtained from Gwaii Haanas and LBCS. Occurrence was
determined through use of camera-trap surveys. Islands with
unknown presence/absence were left as NA in the data set, and
were handled appropriately by the statistical model (see below).
We hypothesized that IslandArea, TreeDist, HumanDist, and
RatStatus are related to nest failure or mortality; wave exposure
indices (Fetch and BioExp) are related to both prey abundance/
availability and reproductive failure/mortality (via nest washout);
Slope, intertidal area (IT50, IT200, IT500, IT1000), Mussel,
Fucus, and ShoreType are related to prey abundance/availability
and in the case of ShoreType, nesting material.
Statistical analyses
All statistical analyses were conducted in R version 3.4.1 (R
Development Core Team 2017). Results may be reproduced from
publicly available code archived within a Github repository
(Dalgarno 2017). We fit models using boosted regression trees
(BRTs), a machine-learning ensemble method that creates and
averages many classification trees (Breiman 2001) using a
boosting algorithm. After the initial tree is built, subsequent trees
model the residuals, with diminishing overall contribution. The
main strengths of BRT are the following: (1) high predictive
performance relative to other commonly used SDM methods
(Elith et al. 2006, Leathwick et al. 2006, De'ath 2007, Palialexis
et al. 2011, Valle et al. 2013); (2) ability to incorporate large
number of predictors, including categorical predictors; (3) ability
to model nonlinear species response curves and complex
interactions; and (4) ability to handle missing data in predictors,
i.e., by using surrogate splits, whereby missing values are filled-in
with values of a correlated variable. These advantages are
balanced by several drawbacks that are generally true for
machine-learning methods: (1) tendency toward model
overfitting, which can lead to poor model transferability (but see
Heikkinen et al. 2012, Bahn and McGill 2013); and (2) limited
model interpretability. In general, this trade-off is justified when
the goal of the study is prediction, not testing biological
hypotheses.
Model fitting and prediction was performed using the gbm
package (Ridgeway 2006) and with several functions in the dismo
package (Hijmans et al. 2012). Three model parameters are user-
defined: learning rate specifies the rate at which the contribution
of each subsequent tree to the overall model shrinks; tree
complexity specifies the number of nodes per tree, which in effect
controls the maximum level of interaction between predictors;
and bag fraction determines the proportion of observed data
randomly selected for each tree. Altogether, these parameters
determine the number of trees used to fit the model and can be
adjusted to optimize model performance or, if necessary, limit
computation time. Parameters were generally set according to
recommendations in Elith et al. (2008). For all models, we used a
bag fraction of 0.5 and maximum tree complexity of 3. Because
any bag fraction less than one introduces model stochasticity
(De'ath 2007), all model summary statistics, e.g., evaluation or
interpretation, were calculated from the mean of 10 model
iterations. Learning rate was adjusted until the optimal number
of trees exceeded 1000, as calculated through a 10-fold cross
validation procedure implemented with the gbm.step function in
the dismo package.
Prevalence of breeding pairs was low, which led to an unequal
balance of presence to absence segments (166:5220; 3%). Low
prevalence (especially < 10%) reduces BRT model performance
unless corrected (Barbet-Massin et al. 2012). We down-weighted
absence data according to prevalence in the training set so that
presence and absence data had equal weight in model fitting.
Species-response curves and relative influence scores were used
to interpret the association between individual predictors and
occurrence. Response curves were determined using partial
dependence plots, which plot the effect of different predictor
values on the response, while all other predictors are held at their
mean (Elith et al. 2008). Relative influence (RI) of each predictor
was determined following a method outlined in Friedman (2001).
Each time a predictor is selected for splitting, the squared
improvement on the model is summed, and averaged across all
trees. This value is then normalized so that all predictor RI scores
sum to 100, with higher numbers indicating higher influence (Elith
et al. 2008). Because model unit size was variable, segment length
(SegLength) was included as a model predictor to test its
influence.
Several procedures were implemented to reduce risk of model
overfitting. First, the aforementioned 10-fold cross validation
procedure in the gbm.step function determined the optimal
number of trees to use before predictive performance on out-of-
bag data, i.e., 10% of data withheld for testing, declined. Second,
if any predictors had pairwise Spearman’s correlation coefficient
(rho) greater than 0.7, the predictor deemed least ecologically
relevant or accurate was removed (Dormann et al. 2013; Table 2).
This resulted in the removal of three predictors (BioExp, IT200,
IT500); all retained predictors had rho less than 0.6. Finally,
noninformative predictors were removed using the gbm.simplify
function, within the dismo R package. In this procedure, the
model is progressively simplified by dropping the least important
predictor and refitting models until predictive performance
(average CV error from 10-fold CV) is less than the original model
(see Elith et al. 2008 for details). This procedure was run for each
of 10 model iterations and the final predictor set excluded any
predictors that were removed in five or more iterations.
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
Table 2. Pairwise Spearman correlation coefficients (rho) for ordinal and continuous predictors. Pairwise predictors with rho > 0.7 are
shown in bold.
Mussel Fucus BioExp Slope IT50 IT200 IT500 IT1000 Fetch TreeDist HumanDist IslandArea
Mussel -
Fucus -0.32 -
BioExp 0.59 0.34 -
Slope -0.11 0.08 -0.14 -
IT50 -0.06 -0.05 -0.07 0.42 -
IT200 -0.01 -0.03 -0.04 0.44 0.81 -
IT500 -0.03 -0.01 -0.06 0.42 0.73 0.95 -
IT1000 -0.06 0.01 -0.10 0.38 0.63 0.83 0.92 -
Fetch 0.50 -0.34 0.77 -0.15 -0.06 -0.07 -0.11 -0.17 -
TreeDist 0.30 -0.18 0.34 0.07 0.24 0.15 0.12 0.08 0.32 -
HumanDist 0.00 -0.01 -0.14 0.05 0.09 0.05 0.04 0.03 -0.05 -0.02 -
IslandArea -0.23 0.06 -0.34 0.04 0.03 -0.02 -0.02 -0.01 -0.30 -0.31 0.17 -
We assessed the ability of models to discriminate between
observed presence and absence using Area Under the receiver
operator characteristic Curve (AUC: Fielding and Bell 1997).
AUC does not require selection of a threshold to reclassify
continuous predicted values into binary presence/absence. In
models with good discrimination, higher predicted values will
tend to be associated with observed presence and lower predicted
values will tend to be associated with observed absence, with
minimal overlap (Lawson et al. 2014). AUC value of 1 indicates
perfect discrimination and AUC value of 0.5 indicates that the
model performed no better than random. AUC of 0.9 indicates
excellent, 0.8–0.9 very good, 0.7–0.8 satisfactory, and < 0.7
indicates poor discrimination ability (Hosmer and Lemeshow
2000). All reported AUC values were derived from 10-fold cross-
validation.
Evaluation of a model’s ability to predict independent data, i.e.,
data not used for model training, is generally recommended when
sufficient occurrence data are available (Araújo and Guisan 2006).
We used three methods to assess predictive ability (indicated by
AUC): (1) we trained the model on all data and tested predictive
ability using 10-fold cross-validation; (2) we trained the model on
2005 occurrence data (~75% of segments) and tested predictive
ability on 2010 occurrence data; and (3) we trained the model on
the northernmost 75% of presence and northernmost 75% of
absence segments and tested predictive ability on the remaining
25% of segments in each category. Finally, spatial autocorrelation
of model residuals was tested using Moran’s I, calculated with
the lctools R package (Kalogirou 2017).
RESULTS
A total of 221 territorial breeding pairs were located in the
surveys: 118 breeding pairs in 2005 and 103 in 2010. A nest with
eggs or chicks was located for 81% of breeding pairs in 2005 (n
= 96) and 87% of breeding pairs in 2010 (n = 90). Of the 5386
total segments surveyed, 3% (n = 166) were occupied by at least
one territorial breeding pair. Breeding pair density on surveyed
shoreline was 0.4 pairs/km.
Approximately 50% of breeding pairs (n = 80) were observed on
islands < 10,000 m² (1 hectare), despite these islands composing
only 15% of surveyed segments. Only 21 (13%) and 54 (33%)
breeding pairs were observed within 20 m and 50 m of a treeline,
respectively; 121 breeding pairs (73%) occurred on bedrock (shore
types 1 and 2) and 151 (91%) occurred on bedrock or mixed
bedrock (shore types 1–6). In general, the range of environmental
space within the study area was well covered by surveyed
segments, although the 2005 survey sampled a broader range of
habitats than the 2010 survey (Table 3 and Table 4).
Model performance
The final model included eight predictors: TreeDist, IslandArea,
Fetch, ShoreType, IT50, IT1000, SegLength, RatStatus. Four
predictors (Slope, Fucus, Mussel, HumanDist) were removed
from the final predictor set during the model simplification
process. The final model had high discrimination ability when
evaluated on all data through 10-fold cross-validation (AUC =
0.886). Discrimination ability was reduced when tested on
independent data, especially on temporally independent data
(2005/2010 temporal partition AUC = 0.829; north/south spatial
partition AUC = 0.858; Table 5). There was evidence of moderate
positive spatial autocorrelation (Moran’s I = 0.34, p-value = 0).
Species-environment associations
Distance to treeline (TreeDist) had the greatest influence on the
model (RI = 41.5%), followed by Island area (IslandArea; RI =
36.7%), wave exposure (Fetch; RI = 5.1%), shoreline type
(ShoreType; RI = 5%), intertidal area within 50 m (IT50; RI =
3.8%), rat occurrence (RatStatus; RI = 3%), and intertidal area
within 1000 m (IT1000; RI = 1.6%). Model unit size (SegLength)
was retained in the final model, although its influence was
relatively low (RI = 3.1%; Table 6).
Partial dependence plots revealed complex nonlinear species
response curves (Figs. 2 and 3). Breeding pair likelihood of
occurrence increased with distance to the treeline (especially > 15
m), although this effect plateaued beyond ~100 m (Fig. 2, Panel
A). Probability of occurrence tended to be higher on smaller
islands, although not on islands less than ~2000 m². Islands greater
than 10 hectares (100,000 m²) had a negative association with
occurrence (Fig. 2, Panel B). Shoreline types that breeding pairs
tended to prefer included wide rock, wide rock with sand and
gravel, narrow rock, and gravel (Fig. 3, Panel A). Probability of
occurrence generally increased with wave exposure, although both
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
Avian Conservation and Ecology (): r
http://www.ace-eco.org/volXX/issYY/artZZ/
Table 3. Range and median for continuous predictors retained in final models over different extents of study area. Values are scaled
for readability. Surveyed 2005: shoreline surveyed in 2005 (south of Juan Perez) by Canadian Wildlife Service; All Surveyed: all surveyed
shoreline including 2005 and 2010 survey conducted by Laskeek Bay Conservation Society in Juan Perez and Laskeek Bay; Study Area:
includes all surveyed shoreline and unsurveyed shoreline within the study area (southeastern Haida Gwaii).
Predictor Surveyed 2005 Surveyed 2010 All Surveyed Study Area
Median Range Median Range Median Range Median Range
IslandArea (m2) 1.3 e+08 76.7–2.6 e+09 3.3 e+05 208.7–2.7 e+08 4.0 e+06 76.7–2.6 e+09 1.7 e+08 76.7–2.6 e+09
TreeDist (m) 15.2 0–2.7 e+03 19.6 0–1.1 e+03 16.5 0–2.7 e+03 12.2 0–2.7 e+03
Fetch (m) 1.6 e+05 367.9–5.5 e+06 4.3 e+05 1.1 e+03–3.3 e+06 2.3 e+05 367.9–5.5 e+06 5.7 e+04 1–5.5 e+06
IT50 (m2) 2.9 e+03 245–7.9 e+03 2.9 e+03 217.9–7.9 e+03 2.9 e+03 217.9–7.9 e+03 2.9 e+04 179.5–7.9 e+03
HumanDist (m) 9.3 e+03 61.6–2.9 e+04 3.3 e+03 29.9–8.7 e+03 7.7 e+03 29.9–2.0 e+04 7.7 e+03 29.1–2.3 e+04
Table 4. Percent shoreline for each shoreline type (ShoreType) over different extents of study area. Surveyed 2005: shoreline surveyed
in 2005 by Canadian Wildlife Service (south of Juan Perez); Surveyed 2010: shoreline surveyed in 2010 by Laskeek Bay Conservation
Society (Juan Perez and Laskeek Bay regions); All surveyed: all surveyed shoreline combined; Study Area: all shoreline in study area
including surveyed and unsurveyed, i.e., predicted, shoreline.
ShoreType Description Surveyed 2005 (%
Shoreline)
Surveyed 2010 (%
Shoreline)
All Surveyed (%
Shoreline)
Study Area (%
Shoreline)
1 Wide rock ramp/platform 18.7 25.8 20.7 10.6
2 Narrow rock cliff/ramp 31.1 40.1 33.6 27.0
3 Wide rock ramp/platform with gravel beach 10.5 6.0 9.3 6.4
4 Narrow rock cliff/ramp with gravel beach 8.0 6.6 7.6 9.4
5 Wide rock ramp/platform with sand/gravel
beach
7.5 9.8 8.2 7.2
6 Narrow rock cliff/ramp with sand/gravel beach 2.7 0.6 2.1 3.0
7 Gravel beach 7.5 5.0 6.8 8.3
8 Sand and gravel beach 6.6 2.8 5.6 19.4
9 Sand beach 0.6 1.4 0.8 2.1
10 Estuary/lagoon 6.8 1.8 5.4 6.5
.
Table 5. AUC of final model over different evaluation
methods. Values are the mean over 10 model iterations.
Evaluation Method AUC
Training 0.926
10-fold Cross-Validation 0.886
North/South Partition 0.858
2005/2010 Partition 0.829
.
Table 6. Relative Influence (RI) of predictors in final
model. Values are the mean over 10 model iterations.
Predictor RI (%)
TreeDist 41.5
IslandArea 36.7
Fetch 5.1
ShoreType 5
IT50 3.8
SegLength 3.1
RatStatus 3
IT1000 1.6
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
Fig. 2. Partial dependence plots for continuous predictors
retained in final model. Partial dependence plots show the
effect of a given predictor on the response, with all other
predictors held at their mean. Rug plots at the top show the
distribution of surveyed segments across that predictor.
very low and very high exposures had a negative influence (Fig.
2, Panel C). Intertidal area within 50 m had a positive effect on
breeding pair occurrence up to values of ~6000 m², i.e., > 75% of
area covered in intertidal, beyond which the trend was reversed
(Fig. 2, Panel D). Effect of intertidal area within 1000 m was
generally negative, although this plateaued beyond ~50,000
(Fig. 2, Panel F). Breeding pairs tended to occur on islands
without rats present (Fig. 3, Panel B). Finally the effect of model
unit size (SegLength) on occurrence was generally positive,
although the strength of the effect decreased with segment lengths
around 100 m (Fig. 2, Panel E).
Predictive maps
In Figure 4, we present a map of predicted probability of
occurrence, with values generated from the final model (Panel A).
As an example, we also show predicted probability of occurrence
on Bischof Islands (Fig. 4, Panel B), Murchison/Faraday Island
group (Fig. 4, Panel C), and Swan/Bolkus Island group (Fig. 4,
Panel D), with known occurrence of breeding pairs displayed as
Fig. 3. Partial dependence plots for categorical predictors
retained in final model. Partial dependence plots show the
effect of a given predictor on the response, with all other
predictors held at their mean.
white dots. From these enlarged areas, it is clear that the model
correctly predicted low density of breeding pairs on Murchison
and Faraday mainland and high density on Bischoff’s Islands. As
well, the model generally correctly predicted high occurrence on
small, rocky offshore islands with higher wave exposure.
DISCUSSION
Interpreting predictions
The main output of this study was a map of predicted probability
of occurrence of Black Oystercatcher breeding pairs at ~100 m
shoreline segments in southeastern Haida Gwaii. Segments
approximate mean territory size. Probability of occurrence, which
is on a continuous scale (bounded by 0 and 1), can be converted
to predicted presence/absence using a threshold, although this
may lead to an unnecessary loss of information (Guillera-Arroita
et al. 2015). If predictions of presence/absence are required,
choice of threshold should depend on the application (Fielding
and Bell 1997, Lawson et al. 2014) because different thresholds
will maximize different aspects of model performance (Liu et al.
2005).
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
Fig. 4. Predicted probability of occurrence of breeding pairs in southeastern Haida Gwaii. Insets
show locations of observed breeding pairs (white points) relative to predicted probability of
occurrence at sites of interest (Bischof Islands, Panel B; Murchison and Faraday Islands, Panel C;
Swan and Bolkus Islands, Panel D).
Species-environment associations
In this study we aimed to test the ability of several GIS data sets,
hypothesized to be proxies for some underlying biological
mechanisms, to predict breeding pair occurrence. Although
causation cannot be determined from correlative models, species-
response curves may inform biological hypotheses that can be
tested through experimentation (Dormann et al. 2012). Species-
response curves identified here closely matched what was expected
based on literature reviewed.
In general, predictors hypothesized to be associated with nest
failure/mortality (IslandArea, TreeDist, RatStatus), not prey
abundance/availability (Fetch, IT50, IT1000, ShoreType), had
higher influence on the models. Tendency for breeding pairs to
occur at small islands, far from treeline has been observed
previously in Haida Gwaii (Vermeer et al. 1992) and elsewhere
(Hazlitt 2001, McFarland 2010). We suggest that these predictors
act as proxies for occurrence of a wide variety of predators known
to cause nest failure or mortality, including Peregrine Falcon
(Falco peregrinus; Hipfner et al. 2012); Gulls (Larus glaucescens;
Hartwick 1973, Nysewander 1977), Crows (Corvus caurinus;
Hartwick 1973, Nysewander 1977, Hipfner et al. 2012), black
bears (Ursus americanus), Common Ravens (Corvus corax),
wolverines (Gulo gulo), and Bald Eagles (Haliaeetus
leucocephalus; Morse et al. 2006). We expect that breeding pairs
avoid nesting in areas that increase their vulnerability to nest
failure through the process of natural selection. They are also able
to adapt habitat use within several years of environmental change,
as revealed by experimental removal of predators and human
disturbance from islands (Ainley and Lewis 1974, Nysewander
1977, Byrd et al. 1997, Croll et al. 2015).
Nest failure may also be attributed to washout, i.e., from waves
(Vermeer et al. 1992), extreme high tides (Morse et al. 2006), and
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
severe storms (Hartwick 1973, Nysewander 1977). We found some
evidence to support the hypothesis that these factors may be driving
breeding pair occurrence. First, breeding pairs avoided very small
islands (< 1500 m²), likely because these were inundated at high
tide, or were more vulnerable to inundation from extreme tides or
waves. However, it should be noted that very small islets were not
represented by the shoreline data set and any breeding pairs that
occurred on these were removed from the occurrence data set.
Thus, the apparent avoidance of very small islets should be further
examined. Second, breeding pairs tended to occur at moderate
wave exposures (Fetch), with a sharp decline in predicted
probability of occurrence at extreme exposures. Response to wave
exposure appears to reflect a trade-off between prey, which is more
abundant (Dalgarno 2016) and available, i.e., from gaping mussels
(Falxa 1992) at high exposures, and vulnerability to nest washout
or risk of injury at the most extreme exposures. For example, Falxa
(1992) observed foraging adults avoiding sites at the highest
exposures, which he attributed to avoidance of injury by pounding
waves (Falxa 1992). To our knowledge, no previous studies have
previously identified these nonlinear relationships, i.e., tendency
to avoid very small islands and very high exposures.
Breeding pairs generally preferred rock or gravel shoreline types
that were wide, and generally avoided sand, mixed sand/gravel, and
narrow shoreline types. A tendency for breeding pairs to occur on
rocky or gravel shoreline types has been observed throughout its
range (Andres 1998, Poe et al. 2009, Weinstein et al. 2014).
However, avoidance of wide rock with gravel and narrow rock with
sand and gravel shoreline types observed in this study did not follow
this general pattern. Prey abundance is likely to be higher at rocky
shore types and at low exposure gravel sites (Dalgarno 2016), which
we hypothesize to be the reason for preference of these shoreline
types. Wide shoreline types are also likely to provide more habitat
for prey and facilitate foraging, because Black Oystercatchers
cannot forage on vertical surfaces (Lindberg et al. 1998), and
breeding pairs are able to deliver more prey to chicks (Hazlitt et
al. 2002).
Similarly, we hypothesize that increasing probability of occurrence
with increasing IT50 values (intertidal area within 50 m circular
buffer) may be explained by prey abundance and prey availability
(Lindberg et al. 1998, Hazlitt et al. 2002). The observed decline in
probability of occurrence at extreme values of IT50, i.e., very flat
areas with > 75% intertidal area, may be attributed to correlation
with shoreline type, i.e., estuary, lagoon, or wide sandy beach.
Better performance of the IT50 predictor over IT1000 (intertidal
area within 1000 m) provides some evidence that foraging occurs
primarily within close proximity to nest-sites, i.e., within a breeding
territory, despite observations of foraging adults travelling up to
a kilometer from breeding territories (Hartwick 1978).
There is particular interest in the effect of introduced rats on
breeding Black Oystercatchers in Haida Gwaii, which has seen
eradication of rats from Langara Island (Taylor et al. 2000), and
more recently, several islands within Gwaii Haanas (Night Birds
Returning Project, unpublished data). Although it is generally
assumed that rats depredate Black Oystercatcher chicks and eggs
(Kurle et al. 2008, Gruman 2013), the effect of rats on Black
Oystercatcher breeding pair occurrence is not well known.
Eradication of rats on Hawadax Island, Alaska, led to an increase
in breeding population from one pair to six pairs over five years
(Croll et al. 2015). Within our study area, Gruman (2013)
demonstrated that breeding pair density is lower on islands with
rats, although that study did not take into consideration other
habitat qualities.
In this study, the influence of rat occurrence (RatStatus) was
relatively low. There may be several reasons for the apparent lack
of influence of rat occurrence on the model: at the scale of this
study, TreeDist may act as a general proxy for occurrence of all
predators, including rats; Black Oystercatchers may still occur on
rat-infested islands, i.e., out of necessity or ignorance, but because
our model does not account for reproductive success, we cannot
observe and model the detrimental effect of rats, i.e., mismatch
between reproductive success and habitat selection (Arlt and Pärt
2007). It is also possible that the quality of our rat occurrence
data set was poor. Further studies may look at change in breeding
population following rat eradication in our study area, although
these data are not yet published.
Observation bias and territoriality
Models that explicitly account for the observation process are
generally called occupancy models. In this study, we could not
assume perfect detection probability (Lyons et al. 2012), nor that
detection probability was not correlated with a predictor of
occurrence. Thus, model predictions may be biased (MacKenzie
et al. 2002, Guillera-Arroita et al. 2015). In repeated boat-based
count surveys of Black Oystercatcher breeding pairs in
Washington, detection probability was 0.75 (95% BCI: 0.42–0.91;
Lyons et al. 2012). Thus, two or more repeated surveys on each
shoreline segment would increase detection probability. However,
repeating surveys is costly given minimal resources typically
available for monitoring activities, and thus the required data for
occupancy modeling is rarely available.
In this study, only 20% of shoreline was surveyed twice because
of logistical constraints. However, even if all shoreline had been
surveyed twice, the length of time between each survey (about a
month) may have violated assumptions of occupancy modeling:
that repeated surveys occur within a closed population
(MacKenzie et al. 2002). Thus, although we acknowledge
potential bias in our results given imperfect detection, we also
suggest that research be undertaken to weigh the costs and
benefits of collecting occurrence data suitable for occupancy
modeling of Black Oystercatcher breeding pairs.
Another source of error may have been failure to adequately
account for territoriality, which may explain moderate spatial
autocorrelation in our model. Breeding pairs aggressively defend
territories (Hazlitt 2001) and the study scale was chosen to
approximately reflect mean territory size (Nysewander 1977,
Hazlitt 2001). However, variability in territory size is large (0.79–
1.84 ha; Nysewander 1977; 25–245 m length; Hazlitt 2001). Thus,
on shorelines where model unit size is smaller than territory size,
segments surrounding an observed breeding pair may have high
predicted probability of occurrence, i.e., suitable habitat, but
observed absence. An alternative approach to take in future
studies may be to model breeding density in relation to features
of islands (e.g., Heinänen and Von Numers 2009). As well, a
sensitivity analysis could be conducted to determine the effect
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
that changing model unit size has on model performance.
However, it should be noted that modeling at the island scale or
using larger average model unit size (e.g., ~200m) would lead to
reduced accuracy of predictors.
Model unit size (SegLength) was retained in the final model, with
probability of occurrence increasing on larger segments. However,
we suggest that alternative modeling approaches would lead to
reduced predictor accuracy (e.g., highest proportion of shore type
rather than exact shore type) and that given its relatively low
influence on the model (3%), this trade-off may not be justified.
Toward a regional model
We developed this model with the intention of providing a
modeling framework for predicting Black Oystercatcher breeding
habitat in Haida Gwaii, B.C., Canada, and to test a set of
predictors that may be used in other regions, or at broader extents.
Of the predictors retained in the final model, only ShoreType is
currently available with B.C.-wide coverage. The Fetch predictor
(and additional fetch-based wave exposure indices) is available for
Haida Gwaii, and all remaining predictors, which were generated
for use in this study, are only currently available for the study area.
However, using methods developed here, and given good quality
satellite imagery, shoreline and intertidal area data, predictors
could be generated for other regions or broader extents at
relatively low cost. We do not recommend collecting data on rat
occurrence for the purpose of a Black Oystercatcher model
because it is expensive to collect and influence on the model was
low.
A regional model will require compilation of occurrence data
from various surveys that have been conducted (historically or
recently), typically from monitoring activities. There are several
challenges associated with this requirement. First, it will be
necessary to ensure that occurrence data adequately cover
geographic and environmental space of prediction area. Although
we show that model predictive ability on independent data was
reasonably good (AUC > 0.85), model transferability may be an
issue, especially in regions that have different environmental
conditions. Moreover, our study demonstrated that predictive
ability on temporally split data, i.e., trained on 2005 occurrence
data, tested on 2010 occurrence data, was relatively poor. We
suspect that this may have been caused by differences in survey
methods/detection probability between surveys, or less likely,
differences in habitat use between years. Temporal partition of
data was also spatially partitioned (2005 occurrence data was
collected in southernmost 75% of study area), so it was difficult
to determine whether poor transferability could be attributed to
different habitat use in the southern part of the study area, which
is dominated by the presence of Moresby Island, and is generally
less accessible. In general, we demonstrate that transferability may
be an issue when occurrence data sets are compiled from multiple
years/organizations.
Second, compiling occurrence data from historical monitoring
activities may be challenging. In this study, we compiled survey
data collected by two organizations: CWS and LBCS. For both
data sets, nest locations in the form of GPS coordinates were
readily available (as well as some absence data in the form of GPS
coordinates from CWS). However, the survey route, which was
needed to determine absence, was not immediately available and
was determined by browsing field notes, reports, and through
direct communication with the organizations. The use of
historical survey data sets may be difficult if data or metadata are
lost, especially in the event that data are used for purposes that
they were not originally intended for (as in this study). Our study
generally supports calls to digitize and make freely available
historical and current ecological data sets, which are collected
carefully and often at great cost (Hampton et al. 2012).
Finally, model predictors will have to be selected based on the
study scale or region of interest. For example, water temperature
and salinity may have greater influence on intertidal species
distributions at broader scales (Raffaelli and Hawkins 1996,
Nyström Sandman et al. 2013). As well, tendency of breeding
pairs to occur at different shoreline types, e.g., rocky vs. gravel,
may vary geographically (Nysewander 1977, Byrd et al. 1997,
Andres 1998, Weinstein et al. 2014) and different predators may
have more or less influence on occurrence dependent on the
region, e.g. foxes (Byrd et al. 1997), rats (Croll et al. 2015),
raccoons (Vermeer et al. 1992), etc. Finally, although our study,
Morse et al. (2006), and Poe et al. (2009) found no influence of
low-level human disturbance on occurrence, human disturbance
may be an important predictor of occurrence in regions outside
of protected areas or on islands permanently inhabited by humans
(Ainley and Lewis 1974, Vermeer et al. 1989, Andres and
Christensen 2009).
CONCLUSION
The conservation status and landscape-level distribution of the
Black Oystercatcher is largely unknown because of sparse survey
data. The need to develop breeding pair distribution models has
been identified as a key research priority by leading experts
(Tessler et al. 2014). To our knowledge, this study is the first to
develop a SDM for predicting probability of occurrence of
breeding pairs at unsurveyed shoreline, at a scale relevant to
landscape-level management. This modeling approach may be
applied to other regions where adequate environmental and
occurrence data exist. Further, our coastal SDM framework,
which integrates ShoreZone attributes and novel GIS data sets,
may be generally useful for predicting distribution of other coastal
species in Haida Gwaii and B.C. more broadly.
Responses to this article can be read online at:
http://www.ace-eco.org/issues/responses.php/1094
Acknowledgments:
This research was funded by Gwaii Haanas National Park Reserve,
National Marine Conservation Area Reserve, and Haida Heritage
Site, and by Janet Mersey, Department of Geography, University
of Guelph. Occurrence data were collected by volunteers and staff
at Laskeek Bay Conservation Society (northern area) and by
Canadian Wildlife Service (southern area). Funding was provided
to Laskeek Bay Conservation Society by Gwaii Haanas. We are
grateful to Mark Hipfner for helping to supply occurrence data from
Canadian Wildlife Service; Jake Pattison and Vivian Pattison from
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
Laskeek Bay Conservation Society for supplying occurrence data
and information on field methods; Patrick Bartier for supplying
various GIS data sets; Mika Murtojarvi, Marji Puotinen, and Joe
Thorley for help developing the wave exposure model; Laurie Wein
and Carita Bergman for supplying rat occurrence data; Thomas
Nudds, Anthony Gaston, and Jake Pattison for help with research
design; Lorne Bennett for comments on an early draft; Brad Andres,
Mark Hipfner, Vivian Pattison, Jake Pattison, and Stephanie
Hazlitt for comments on the conceptual model. Finally, we thank
the Haida Nation for permitting access to its traditional lands and
waters.
LITERATURE CITED
Ainley, D. G., and J. T. Lewis. 1974. The history of Farallon Island
marine bird populations, 1854-1972. Condor 76:432-446. http://
dx.doi.org/10.2307/1365816
Andres, B. A. 1998. Shoreline habitat use of Black Oystercatchers
breeding in Prince William Sound, Alaska. Journal of Field
Ornithology 69:626-634.
Andres, B. A., and R. E. Christensen. 2009. Dramatic changes in
the number of Black Oystercatchers nesting in Sitka Sound,
Alaska. Wader Study Group Bulletin 116:181-184.
Andres, B. A., and G. A. Falxa. 1995. Black Oystercatcher
(Haematopus bachmani). In P. G. Rodewald, editor. The Birds of
North America. Cornell Lab of Ornithology, Ithaca, New York,
USA. http://dx.doi.org/10.2173/bna.155
Andres, B. A., P. A. Smith, R. I. G. Morrison, C. L. Gratto-Trevor,
S. C. Brown, and C. A. Friis. 2012. Population estimates of North
American shorebirds, 2012. Wader Study Group Bulletin
119:178-194.
Araújo, M. B., and A. Guisan. 2006. Five (or so) challenges for
species distribution modeling. Journal of Biogeography
33:1677-1688. http://dx.doi.org/10.1111/j.1365-2699.2006.01584.
x
Arlt, D., and T. Pärt. 2007. Nonideal breeding habitat selection:
a mismatch between preference and fitness. Ecology 88:792-801.
http://dx.doi.org/10.1890/06-0574
Bahn, V., and B. J. McGill. 2013. Testing the predictive
performance of distribution models. Oikos 122:321-331. http://
dx.doi.org/10.1111/j.1600-0706.2012.00299.x
Barbet-Massin, M., F. Jiguet, C. H. Albert, and W. Thuiller. 2012.
Selecting pseudo-absences for species distribution models: how,
where and how many? Methods in Ecology and Evolution
3:327-338. http://dx.doi.org/10.1111/j.2041-210X.2011.00172.x
Bergman, C. M., J. Pattison, and E. Price. 2013. The Black
Oystercatcher as a sentinel species in the recovery of the Northern
Abalone. Condor 115:800-807. http://dx.doi.org/10.1525/
cond.2013.120182
Breiman, L. 2001. Statistical modeling: the two cultures.
Statistical Science 16:199-231. http://dx.doi.org/10.1214/ss/1009213726
Byrd, G. V., E. P. Bailey, and W. Stahl. 1997. Restoration of island
populations of Black Oystercatchers and Pigeon Guillemots by
removing introduced foxes. Colonial Waterbirds 20:253-260.
http://dx.doi.org/10.2307/1521691
Carlson-Bremer, D., T. M. Norton, K. V Gilardi, E. S. Dierenfeld,
B. Winn, F. J. Sanders, C. Cray, M. Oliva, T. C. Chen, S. E. Gibbs,
M. S. Sepu, and C. K. Johnson. 2010. Health assessment of
American Oystercatchers (Haemotopus palliatus) in Georgia and
South Carolina. Journal of Wildlife Diseases 46:772-780. http://
dx.doi.org/10.7589/0090-3558-46.3.772
Croll, D. A., K. M. Newton, M. McKown, N. Holmes, J. C.
Williams, H. S. Young, S. Buckelew, C. A. Wolf, G. Howald, M.
F. Bock, J. A. Curl, and B. R. Tershy. 2015. Passive recovery of
an island bird community after rodent eradication. Biological
Invasions 18:1-13.
Dalgarno, S. 2016. Predictive modelling of Black Oystercatcher
(Haemotopus Bachmani) breeding pair occurrence and prey
abundance in Haida Gwaii, British Columbia. Thesis. University
of Guelph, Guelph, Ontario, Canada.
Dalgarno, S. 2017. Bloy-distribution-model. Github repository.
[online] URL: https://doi.org/10.5281/zenodo.997576
Dalgarno, S., and J. Thorley. 2017. haidawave: Wave exposure from
fetch and wind data. R package version 0.0.1. R Foundation for
Statistical Computing, Vienna, Austria. [online] URL: https://
github.com/sebdalgarno/haidawave
De'ath, G. 2007. Boosted trees for ecological modeling and
prediction. Ecology 88:243-251. http://dx.doi.org/10.1890/0012-9658
(2007)88[243:BTFEMA]2.0.CO;2
Dormann, C. F., J. Elith, S. Bacher, C. Buchmann, G. Carl, G.
Carré, J. R. G. Marquéz, B. Gruber, B. Lafourcade, P. J. Leitão,
T. Münkemüller, C. McClean, P. E. Osborne, B. Reineking, B.
Schröder, A. K. Skidmore, D. Zurell, and S. Lautenbach. 2013.
Collinearity: a review of methods to deal with it and a simulation
study evaluating their performance. Ecography 36:27-46. http://
dx.doi.org/10.1111/j.1600-0587.2012.07348.x
Dormann, C. F., S. J. Schymanski, J. Cabral, I. Chuine, C.
Graham, F. Hartig, M. Kearney, X. Morin, C. Römermann, B.
Schröder, and A. Singer. 2012. Correlation and process in species
distribution models: bridging a dichotomy. Journal of
Biogeography 39:2119-2131. http://dx.doi.org/10.1111/
j.1365-2699.2011.02659.x
Elith, J., C. H. Graham, R. P. Anderson, M. Dudík, S. Ferrier, A.
Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A.
Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, C.
Moritz, M. Nakamura, Y. Nakazawa, J. M. M. Overton, A.
Townsend Peterson, S. J. Phillips, K. Richardson, R. Scachetti-
Pereira, R. E. Schapire, J. Soberón, S. Williams, M. S. Wisz, and
N. E. Zimmermann. 2006. Novel methods improve prediction of
species’ distributions from occurrence data. Ecography
29:129-151. http://dx.doi.org/10.1111/j.2006.0906-7590.04596.x
Elith, J., J. R. Leathwick, and T. Hastie. 2008. A working guide
to boosted regression trees. Journal of Animal Ecology 77:802-813.
http://dx.doi.org/10.1111/j.1365-2656.2008.01390.x
Falxa, G. A. 1992. Prey choice and habitat use by foraging Black
Oystercatchers: interactions between prey quality, habitat
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
availability, and age of bird. Dissertation. University of California,
Davis, California, USA.
Fielding, A. H., and J. F. Bell. 1997. A review of methods for the
assessment of prediction errors in conservation presence/absence
models. Environmental Conservation 24:38-49. http://dx.doi.
org/10.1017/S0376892997000088
Friedman, J. H. 2001. Greedy function approximation: a gradient
boosting machine. Annals of Statistics 29:1189-1232 http://dx.
doi.org/10.1214/aos/1013203451
Gruman, C. A. 2013. Context-dependence of a cross-system
trophic cascade in Gwaii Haanas, British Columbia. Thesis. Simon
Fraser University, Burnaby, British Columbia, Canada.
Guillera-Arroita, G., J. J. Lahoz-Monfort, J. Elith, A. Gordon,
H. Kujala, P. E. Lentini, M. A. McCarthy, R. Tingley, and B. A.
Wintle. 2015. Is my species distribution model fit for purpose?
Matching data and models to applications. Global Ecology and
Biogeography 24:276-292. http://dx.doi.org/10.1111/geb.12268
Guisan, A., and N. E. Zimmermann. 2000. Predictive habitat
distribution models in ecology. Ecological Modelling 135:147-186.
http://dx.doi.org/10.1016/S0304-3800(00)00354-9
Hampton, S. E., J. J. Tewksbury, and C. A. Strasser. 2012.
Ecological data in the Information Age. Frontiers in Ecology and
the Environment 10:59. http://dx.doi.org/10.1890/1540-9295-10.2.59
Harfenist, A., N. A. Sloan, and P. M. Bartier. 2002. Living marine
legacy of Gwaii Haanas. IV: marine mammal baseline to 2003
and marine mammal-related management issues throughout the
Haida Gwaii region. Parks Canada Technical Reports in
Ecosystem Science 36:164.
Harper, J. R., W. T. Austin, M. Moms, P. D. Reimer, and R.
Reitmeier. 1994. Ecological classification of Gwaii Haanas: a
biophysical inventory of the coastal resources. Report prepared for
Parks Canada, Calgary, Alberta, Canada by Coastal and Ocean
Resources Inc., Sidney, British Columbia, Canada.
Hartwick, E. B. 1973. Foraging strategy of the Black
Oystercatcher. Thesis. University of British Columbia,
Vancouver, British Columbia, Canada.
Hartwick, E. B. 1978. The use of feeding areas outside of the
territory of breeding Black Oystercatchers. Wilson Bulletin
90:650-652.
Hazlitt, S. L. 1999. Territory quality and parental behaviour of
the black oystercatcher in the Strait of Georgia, British Columbia.
Thesis. Simon Fraser University, Burnaby, British Columbia,
Canada.
Hazlitt, S. L. 2001. Territory quality and reproductive success of
Black Oystercatchers in British Columbia. Wilson Bulletin
113:404-409. http://dx.doi.org/10.1676/0043-5643(2001)113[0404:
TQARSO]2.0.CO;2
Hazlitt, S. L., and R. W. Butler. 2001. Site fidelity and reproductive
success of Black Oystercatchers in British Columbia. Waterbirds
24:203-207. http://dx.doi.org/10.2307/1522031
Hazlitt, S. L., R. C. Ydenberg, and D. B. Lank. 2002. Territory
structure, parental provisioning, and chick growth in the
American Black Oystercatcher (Haematopus bachmani). Ardea
90:219-228.
Heikkinen, R. K., M. Marmion, and M. Luoto. 2012. Does the
interpolation accuracy of species distribution models come at the
expense of transferability? Ecography 35:276-288. http://dx.doi.
org/10.1111/j.1600-0587.2011.06999.x
Heinänen, S., and M. Von Numers. 2009. Modelling species
distribution in complex environments: an evaluation of predictive
ability and reliability in five shorebird species. Diversity and
Distributions 15:266-279. http://dx.doi.org/10.1111/
j.1472-4642.2008.00532.x
Hijmans, R. J., S. Phillips, J. R. Leathwick, and J. Elith. 2012.
dismo: species distribution modeling. R package "dismo" version
1.1-4. R Foundation for Statistical Computing, Vienna, Austria.
[online] URL:http://CRAN.R-project.org/package=dismo
Hipfner, M. J., K. W. Morrison, and A.-L. Kouwenberg. 2012.
Biology of Black Oystercatchers breeding on Triangle Island,
British Columbia, 2003–2011. Northwestern Naturalist 93:145-153.
http://dx.doi.org/10.1898/nwn12-02.1
Hosmer, D. W., and S. Lemeshow. 2000. Applied logistic
regression. Second edition. John Wiley and Sons, New York, New
York, USA. http://dx.doi.org/10.1002/0471722146
Howes, D., J. Harper, and E. Owens. 1994. Physical shore-zone
mapping system for British Columbia. Resources Inventory
Committee Publication 8:71.
Kalogirou, S. 2017. lctools: Local correlation, spatial inequalities,
geographically weighted regression and other tools. R package
version 0.2-6. R Foundation for Statistical Computing, Vienna,
Austria. [online] URL:http://CRAN.R-project.org/package=
lctools
Kurle, C. M., D. A. Croll, and B. R. Tershy. 2008. Introduced rats
indirectly change marine rocky intertidal communities from
algae- to invertebrate-dominated. Proceedings of the National
Academy of Sciences of the United States of America
105:3800-3804. http://dx.doi.org/10.1073/pnas.0800570105
Langham, G. M., J. G. Schuetz, T. Distler, C. U. Soykan, and C.
Wilsey. 2015. Conservation status of North American birds in the
face of future climate change. PLoS ONE 10:e0135350. http://dx.
doi.org/10.1371/journal.pone.0135350
Lawson, C. R., J. A. Hodgson, R. J. Wilson, and S. A. Richards.
2014. Prevalence, thresholds and the performance of presence-
absence models. Methods in Ecology and Evolution 5:54-64. http://
dx.doi.org/10.1111/2041-210X.12123
Leathwick, J. R., J. Elith, M. P. Francis, T. Hastie, and P. Taylor.
2006. Variation in demersal fish species richness in the oceans
surrounding New Zealand: an analysis using boosted regression
trees. Marine Ecology Progress Series 321:267-281. http://dx.doi.
org/10.3354/meps321267
Lindberg, D. R., J. A. Estes, and K. I. Warheit. 1998. Human
influences on trophic cascades along rocky shores. Ecological
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
Applications 8:880-890. http://dx.doi.org/10.1890/1051-0761
(1998)008[0880:HIOTCA]2.0.CO;2
Lindegarth, M., U. Bergström, J. Mattila, S. Olenin, M.
Ollikainen, A.-L. Downie, G. Sundblad, M. Bučas, M. Gullström,
M. Snickars, M. von Numers, J. R. Svensson, and A. K. Kosenius.
2014. Testing the potential for predictive modeling and mapping
and extending its use as a tool for evaluating management
scenarios and economic valuation in the Baltic Sea (PREHAB).
Ambio 43:82-93. http://dx.doi.org/10.1007/s13280-013-0479-2
Liu, C., P. M. Berry, T. P. Dawson, and R. G. Pearson. 2005.
Selecting thresholds of occurrence in the prediction of species
distributions. Ecography 28:385-393. http://dx.doi.org/10.1111/
j.0906-7590.2005.03957.x
Lyons, J. E., J. A. Royle, S. M. Thomas, E. Elliott-Smith, J. R.
Evenson, E. G. Kelly, R. L. Milner, D. R. Nysewander, and B. A.
Andres. 2012. Large-scale monitoring of shorebird populations
using count data and N-mixture models: Black Oystercatcher
(Haematopus bachmani) surveys by land and sea. Auk
129:645-652. http://dx.doi.org/10.1525/auk.2012.11253
MacKenzie, D. L., J. D. Nichols, G. B. Lachman, S. Droege, J. A.
Royle, and C. A. Langtimm. 2002. Estimating site occupancy rates
when detection probabilities are less than one. Ecology
83:2248-2255. http://dx.doi.org/10.1890/0012-9658(2002)083[2248:
ESORWD]2.0.CO;2
McFarland, B. A. 2010. Habitat characteristics of Black
Oystercatcher breeding territories. Thesis. University of Alaska,
Fairbanks, Alaska, USA.
Morse, J. A., A. N. Powell, and M. D. Tetreau. 2006. Productivity
of Black Oystercatchers: effects of recreational disturbance in a
national park. Condor 108:623-633. http://dx.doi.org/10.1650/0010-5422
(2006)108[623:POBOEO]2.0.CO;2
Murtojärvi, M., T. Suominen, H. Tolvanen, V. Leppänen, and O.
S. Nevalainen. 2007. Quantifying distances from points to
polygons: applications in determining fetch in coastal
environments. Computers & Geosciences 33:843-852. http://dx.
doi.org/10.1016/j.cageo.2006.10.006
Nysewander, D. R. 1977. Reproductive success of the Black
Oystercatcher in Washington State. Thesis. University of
Washington, Seattle, Washington, USA.
Nyström Sandman, A., S. A. Wikström, M. Blomqvist, H.
Kautsky, and M. Isaeus. 2013. Scale-dependent influence of
environmental variables on species distribution: a case study on
five coastal benthic species in the Baltic Sea. Ecography
36:354-363. http://dx.doi.org/10.1111/j.1600-0587.2012.07053.x
Palialexis, A., S. Georgakarakos, I. Karakassis, K. Lika, and V.
D. Valavanis. 2011. Prediction of marine species distribution from
presence-absence acoustic data: comparing the fitting efficiency
and the predictive capacity of conventional and novel distribution
models. Hydrobiologia 670:241-266. http://dx.doi.org/10.1007/
s10750-011-0673-9
Poe, A. J., M. I. Goldstein, B. A. Brown, and B. A. Andres. 2009.
Black Oystercatchers and campsites in Western Prince William
Sound, Alaska. Waterbirds 32:423-429. http://dx.doi.
org/10.1675/063.032.0307
R Development Core Team. 2017. R: a language and environment
for statistical computing. R Foundation for Statistical Computing,
Vienna, Austria. [online] URL: http://www.R-project.org/
Raffaelli, D., and S. Hawkins. 1996. Intertidal ecology. First
edition. Chapman & Hall, London, UK. http://dx.doi.
org/10.1007/978-94-009-1489-6
Richmond, S., A. R. Couturier, P. D. Taylor, and D. Lepage. 2015.
Analyses and mapping. In P. J. A. Davidson, R. J. Cannings, A.
R. Couturier, D. Lepage, and C. M. Di Corrado, editors. The atlas
of the breeding birds of British Columbia, 2008-2012. Bird Studies
Canada, Delta, British Columbia, Canada.
Ridgeway, G. 2006. Generalized boosted regression models. R
Package “gbm,” version 2.1.3. R Foundation for Statistical
Computing, Vienna, Austria. [online] URL: http://CRAN.R-
project.org/package=gbm
Sloan, N. A., and P. M. Bartier. 2006. Living marine legacy of
Gwaii Haanas. V: coastal zone values and management around
Haida Gwaii. Parks Canada Technical Reports in Ecosystem
Science 42:1-413.
Taylor, R. H., G. W. Kaiser, and M. C. Drever. 2000. Eradication
of Norway rats for recovery of seabird habitat on Langara Island,
British Columbia. Restoration Ecology 8:151-160. http://dx.doi.
org/10.1046/j.1526-100x.2000.80022.x
Tessler, D. F., J. A. Johnson, B. A. Andres, S. Thomas, and R. B.
Lanctot. 2014. A global assessment of the conservation status of
the Black Oystercatcher (Haematopus bachmani). International
Wader Study Group 20:83-96.
Valle, M., M. M. van Katwijk, D. J. de Jong, T. J. Bouma, A. M.
Schipper, G. Chust, B. M. Benito, J. M. Garmendia, and Á. Borja.
2013. Comparing the performance of species distribution models
of Zostera marina: implications for conservation. Journal of Sea
Research 83:56-64. http://dx.doi.org/10.1016/j.seares.2013.03.002
Vermeer, K., K. H. Morgan, and G. E. J. Smith. 1989. Population
and nesting habitat of American Black Oystercatcher in the Strait
of Georgia. Pages 118-122 in K. Vermeer and R. W. Butler, editors.
The ecology and status of marine and shoreline birds in the Strait
of Georgia, British Columbia, Canada. Canadian Wildlife Service
Special Publication, Ottawa, Ontario, Canada.
Vermeer, K., K. H. Morgan, and G. E. J. Smith. 1992. Black
Oystercatcher habitat selection, reproductive success, and their
relationship with Glaucous-winged Gulls. Colonial Waterbirds
15:14-23. http://dx.doi.org/10.2307/1521350
Weinstein, A., L. Trocki, R. Levalley, R. H. Doster, T. Distler,
and K. Krieger. 2014. A first population assessment of Black
Oystercatcher (Haematopus bachmani) in California. Marine
Ornithology 1972:49-56.
Wisz, M. S., J. Pottier, W. D. Kissling, L. Pellissier, J. Lenoir, C.
F. Damgaard, C. F. Dormann, M. C. Forchhammer, J. A. Grytnes,
A. Guisan, R. K. Heikkinen, T. T. Høye, I. Kühn, M. Luoto, L.
Maiorano, M. C. Nilsson, S. Normand, E. Öckinger, N. M.
Schmidt, M. Termansen, A. Timmermann, D. A. Wardle, P.
Aastrup, and J. C. Svenning. 2013. The role of biotic interactions
in shaping distributions and realised assemblages of species:
implications for species distribution modelling. Biological
Avian Conservation and Ecology 12(2): 9
http://www.ace-eco.org/vol12/iss2/art9/
Reviews of the Cambridge Philosophical Society 88:15-30. http://
dx.doi.org/10.1111/j.1469-185X.2012.00235.x
Zacharias, M. A., D. E. Howes, J. R. Harper, and P. Wainwright.
1998. The British Columbia marine ecosystem classification:
rationale, development, and verification. Coastal Management
26:105-124. http://dx.doi.org/10.1080/08920759809362347
Editor-in-Chief: Keith A.Hobson
Subject Editor: Scott Wilson
Appendix 1: Reclassification of ShoreZone coastal class into “shoreline type” and “substrate type.”
Substrate
Sediment
Slope
(°)
Coastal
Class
Coastal Class Description
Shoreline
Type
Rock
NA
>20
NA
5-20
1
Rock ramp, wide
1
<5
2
Rock platform, wide
1
>20
3
Rock cliff
2
5-20
4
Rock ramp, narrow
2
<5
5
Rock platform, narrow
2
Rock +
Sediment
Gravel
5-20
6
Ramp w/ gravel beach, wide
3
<5
7
Platform w/ gravel beach wide
3
>20
8
Cliff w/ gravel beach
4
5-20
9
Ramp w/ gravel beach
4
<5
10
Platform w/ gravel beach
4
Sand +
Gravel
5-20
11
Ramp w/ gravel & sand beach,
wide
5
<5
12
Platform w/ gravel & sand beach,
wide
5
>20
13
Cliff w/ gravel & sand beach
6
5-20
14
Ramp w/ gravel & sand beach
6
<5
15
Platform w/ gravel & sand beach
6
Sand
5-20
16
Ramp w/ sand beach, wide
5
<5
17
Platform w/ sand beach, wide
5
>20
18
Cliff w/ sand beach
6
5-20
19
Ramp w/ sand beach, narrow
6
<5
20
Platform w/ sand beach, narrow
6
Sediment
Gravel
>20
21
Gravel flat, wide
7
5-20
22
Gravel beach, narrow
7
<5
23
Gravel flat or fan
7
Sand +
Gravel
<5
24
Sand & gravel flat or fan
8
5-20
25
Sand & gravel beach, narrow
8
<5
26
Sand & gravel flat or fan
8
Sand/Mud
5-20
27
Sand beach
9
<5
28
Sand flat
9
29
Mudflat
10
5-20
30
Sand beach
9
Organics/
Fines
NA
31
Estuaries
10
Anthropog
enic
Man-made
NA
32
Man-made, permeable
10
33
Man-made impermeable
10
Current-dominated
34
Channel
10
!
... Several studies have linked either higher or lower fetch to the presence of coastal breeding sites for different species of waders, alcids and divers (R€ onk€ a et al. 2008(R€ onk€ a et al. , Haynes et al. 2014. Dalgarno et al. (2017) found evidence of a trade-off in Black Oystercatchers Haematopus bachmani, which nested most commonly in moderately high fetch areas that provided good foraging resources, even though breeders were excluded from the most exposed areas where the risk of nest-washout was too high. Given the variety of different mechanisms that may link wind and wave exposure to breeding success and nest-site location, and the diverse ways that aquatic bird species interact with the shoreline environment, it is possible that fetch may be one measure that distinguishes niche space in a breeding bird assemblage. ...
... In a similar way, a colony located along a coastline with low fetch could come at the cost of a larger commuting distance to productive foraging areas, and it is possible that differences in foraging behaviour among pursuit divers alter the balance of this trade-off. In other systems, birds often interact with fetch and wind via vegetation structure or food supplies (Allen et al. 2008, Dalgarno et al. 2017) but we were unable to investigate the effects that fetch and wind-related ocean mixing may have on the marine community on which Antarctic seabirds rely. Considering the many ways that both sea-ice and oceanographic forcing might impact both krill and fish distribution in the region, and the potential differences in how and where these four species forage, it is reasonable to assume that multiple mechanisms probably link fetch to marine habitat suitability. ...
Article
Avian breeding sites located along shorelines may allow easy access to aquatic food sources, but risk exposing birds and nests to high wind and wave action. One measure of exposure is wind fetch, the distance of open water over which wind can blow uninterrupted. By calculating fetch weighted by prevailing wind direction for breeding colonies of pursuit‐diving seabirds in the Antarctic Peninsula, we show that different members of this guild have opposing relationships to coastline exposure. Gentoo Penguins Pygoscelis papua preferentially occupied more enclosed sites with lower fetch. Surprisingly, however, Chinstrap Penguins P. antarcticus and Antarctic Shags Leucocarbo bransfieldensis appear to prefer more exposed sites. While considerable research has been devoted to understanding Antarctic seabird habitat suitability, the role of wind and wave exposure has not been considered in depth, in part because comprehensive data on colony presence and absence has only recently been made available. We propose several mechanisms for why fetch may act to differentiate niches among this guild. These findings may increase our ability to identify suitable breeding areas for these and other near‐shore breeding species as they respond to climate change.
... It can also provide information about the probability of Yellow Rail occurrence, by means of a threshold, although this would result in a loss of resolution (Guillera-Arroita et al. 2015). Several authors have noted that different thresholds can maximize variable aspects of model performance, therefore the choice of a threshold should be tailored to the specific application (Dalgarno et al. 2017;Lawson et al. 2014;Liu et al. 2005). ...
Article
Full-text available
Yellow Rail (Coturnicops noveboracensis) are a highly specialized wetland obligate bird. They are a species at risk in Canada and very little is known about their abundance in the wet-lands of the western boreal forest. Emerging technologies have enabled us to effectively survey for Yellow Rail and other wetland birds in remote areas by using ground-based remote sensors (autonomous recording units; ARUs) to conduct passive acoustic monitoring. We analyzed bird data from the first four years (2013–2016) of an ongoing monitoring program led by the Bioacoustic Unit at the Alberta Biodiversity Monitoring Institute. We developed species abundance models using satellite data from Sentinel-1 and Sentinel-2 processed in Google Earth Engine. We identified covariates from both synthetic aperture radar and optical remote sensing that had strong predictive capacity for this wetland bird (AUC = 0.96). Approximately 1.5% of available wetland habitat in our northeast Alberta study area was predicted to be highly suitable for Yellow Rail.
... It can also provide information about the probability of Yellow Rail occurrence, by means of a threshold, although this would result in a loss of resolution . Several authors have noted that different thresholds can maximize variable aspects of model performance, therefore the choice of a threshold should be tailored to the specific application (Dalgarno et al., 2017;. ...
Thesis
Full-text available
The Yellow Rail (Coturnicops noveboracensis) is a small, secretive, wetland bird, which is apparently rare throughout most of its range. Almost nothing is known about its abundance and density in the wetlands of the western boreal forest. Emerging technologies have enabled us to effectively survey for Yellow Rail in remote wetlands by using ground-based remote sensors (autonomous recording units; ARUs) to conduct passive acoustic monitoring. This technique was employed to survey Yellow Rail populations across two large study areas: one in the taiga plains of the Northwest Territories, and the other in the boreal plains of Alberta, Canada. For the Edéhzhíe Indigenous Protected Area (NWT), a predictive map of Yellow Rail density was developed based on data obtained from a systematic avian survey conducted in 2016, using 205 ARUs. Counts of Yellow Rail were converted to density estimates using habitat specific effective detection radii obtained via call-playback experiments. Generalized linear models and covariates from a detailed landcover classification effort were used to develop the spatial model. Yellow Rail appeared to breed at relatively high densities (0.07 males/ha compared to average densities of 0.04-0.05 males/ha) in Edéhzhíe and they were strongly associated with marsh wetlands. The Mills Lake wetland complex was identified as an important breeding area for Yellow Rail in the Northwest Territories based on a population estimate of ca. 560 breeding pairs. For the Alberta Oilsands Region, a predictive map of Yellow Rail breeding abundance was developed using acoustic data compiled from the first five years (2013-2017) of an ongoing bioacoustic monitoring program. Recent developments in open-access satellite data, cloud computing (Google Earth Engine), and data science were leveraged to secure large-scale, high-resolution (10 m) landcover data. Multiple satellite remote sensors were used to derive fifteen predictor variables: Sentinel-1 synthetic aperture radar, Sentinel-2 optical imagery, and Advanced Land Observation Satellite digital elevation maps. Gradient boosted regression was used to develop the spatial model. Six remote sensing predictors (DPOL, ΔVH, REIP, ARI, VH, and SWI), were identified as having strong predictive capacity. Several predictors had complex non-linear responses and multiple important interactions were identified. Approximately 1.5% of available wetland habitat in the region was predicted to be highly suitable for Yellow Rail.
... Species distribution models (SDMs) that tie species occurrence throughout a landscape to the environment are now common practice (e.g. Fournier et al. 2017;Dalgarno et al. 2017;Evangelista et al. 2018;Reino et al. 2018). High-resolution land-use and land-cover data that can inform SDMs are freely available (United States Geological Survey 2011). ...
Article
Full-text available
ContextSpecies are influenced by factors operating at multiple scales, but multi-scale species distribution and abundance models are rarely used. Though multi-scale species distribution models outperform single-scale models, when compared through model selection, multi- and single-scale models built with computer learning algorithms have not been compared.Objectives We compared the performance of models using a simple and accessible, multi-scale, machine learning, species distribution and abundance modeling framework to pseudo-optimized and unoptimized single-scale models.Methods We characterized environmental variables at four spatial scales and used boosted regression trees to build multi-scale and single-scale distribution and abundance models for 28 bird species. For each species and across species, we compared the performance of multi-scale models to pseudo-optimized and lowest-performing unoptimized single-scale models.ResultsMulti-scale distribution models consistently performed as well or better than pseudo-optimized single-scale models and significantly better than unoptimized single-scale models. Abundance model performance showed a similar, but less pronounced pattern. Mixed-effects models, that controlled for species, provided strong evidence that multi-scale models performed better than unoptimized single-scale models. Although mean improvement in model performance across species appeared minor, for individual species, arbitrary selection of scale could result in discrepancies of up to fourteen percent for area of suitable habitat and population estimates.Conclusions Scale selection should be explicitly addressed in distribution and abundance modeling. The multi-scale species distribution and abundance modeling framework presented here provides a concise and accessible alternative to standard pseudo-scale optimization while addressing the scale-dependent response of species to their environment.
Research
Full-text available
The main purpose of lctools is to provide researchers and educators with easy-to-learn user friendly tools for calculating key spatial statistics and to apply simple as well as advanced methods of spatial analysis in real data. These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients, Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation (Global and Local Moran's I), several Geographically Weighted Regression techniques and other Spatial Analysis tools (other geographically weighted statistics). This package also contains functions for measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.
Article
Full-text available
The number and scale of island invasive species eradications is growing, but quantitative evidence of the conservation efficacy of passive recovery is limited. We compare relative abundances of breeding birds on Hawadax Island (formerly named Rat island), Aleutian Archipelago, Alaska, pre- and post- rat eradication to examine short-term (<1 year post-eradication) changes due to rodenticide application, and medium-term (5 years post-eradication) changes due to the absence of invasive rats. In the short term, Bald Eagle (Haliaeetus leucocephalus) numbers decreased from 24 individuals pre-eradication to two individuals <1 year post-eradication, but recovered to 10 individuals (42 % of pre-eradication) 5 years post-eradication, with all individuals nesting (63 % of the pre-eradication nesting). Five years post-eradication relative abundances of most terrestrial birds surveyed using point counts either significantly increased [Gray-crowned Rosy Finch (Leucosticte tephrocotis), Lapland Longspur (Calcarius lapponicus), Snow Bunting (Plectrophenax nivalis), Song Sparrow (Melospiza melodia)] or did not differ [Pacific Wren (Troglodytes troglodytes)]. Shorebirds also increased 5 years post-eradication with Black Oystercatchers (Haematopus palliates) increasing fivefold, and Rock Sandpiper (Calidris ptilocnemis) nesting increasing from one to five nests. We confirmed two species of ground nesting seabirds [Tufted Puffin (Fratercula cirrhata) and Leach’s Storm-petrel (Oceanodroma leucohoa)] as nesting (puffin) or engaged in courtship behavior (Storm-petrel) 5 years post-eradication. Our results indicate that despite the short-term impact on Bald Eagles, and without further human intervention, most terrestrial and marine birds have newly-colonized, re-colonized, or increased in abundance following the eradication of invasive rats.
Article
Human-induced climate change is increasingly recognized as a fundamental driver of biological processes and patterns. Historic climate change is known to have caused shifts in the geographic ranges of many taxa and future climate change is expected to result in even greater redistributions of species. As a result, predicting the impact of climate change on future patterns of biodiversity will greatly aid conservation planning. Using the North American Breeding Bird Survey and Audubon Christmas Bird Count, two of the most comprehensive continental datasets of vertebrates in the world, and correlative distribution modeling, we assessed geographic range shifts for 588 North American bird species during both the breeding and non-breeding seasons under a range of future emission scenarios (SRES A2, A1B, B2) through the end of the century. Here we show that 314 species (53%) are projected to lose more than half of their current geographic range across three scenarios of climate change through the end of the century. For 126 species, loss occurs without concomitant range expansion; whereas for 188 species, loss is coupled with potential to colonize new replacement range. We found no strong associations between projected climate sensitivities and existing conservation prioritizations. Moreover, species responses were not clearly associated with habitat affinities, migration strategies, or climate change scenarios. Our results demonstrate the need to include climate sensitivity into current conservation planning and to develop adaptive management strategies that accommodate shrinking and shifting geographic ranges. The persistence of many North American birds will depend on their ability to colonize climatically suitable areas outside of current ranges and management actions that target climate adaptation.
Article
Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose.
Article
The nesting population, habitat selection, and reproductive success of American Black Oystercatchers (Haematopus ostralegus bachmani), and their relationship with nesting Glaucous-winged Gulls (Larus glaucescens) was examined at Skidegate Inlet in the Queen Charlotte Islands, British Columbia. Nesting densities (1.6 pairs/km shoreline) were higher, and a larger proportion of islands in Skidegate Inlet (28%) supported nesting oystercatchers than in three southern locations in British Columbia. Fifty-three pairs of oystercatchers produced 81 clutches with an average clutch size of 2.26 +/- 0.43 eggs. The clutch size declined significantly over the nesting season. Overall hatching success was 38.3% and the fledging rate was 0.49 fledglings per pair. The fledging rate was significantly higher for oystercatchers nesting on islands with gulls than those on islands without gulls. Oystercatchers nested in equal proportions on small nesting islands which either connected to, or were separated from large islands at low tide, but they hatched significantly fewer eggs on the former than on the latter; large islands contained raccoons (Procyon lotor), which preyed upon the birds' eggs. Oystercatchers and gulls were significantly associated with one another, and the two species selected the same type of nesting islands, which were relatively small and supported little or no forest. The positive and negative aspects of gulls and oystercatchers nesting together are discussed.
Article
We compared reproductive success and territory fidelity in Black Oystercatchers (Haematopus bachmani) in the Strait of Georgia, British Columbia. Twenty-four of 34 nesting pairs hatched eggs in at least one year of the study, and of which 16 pairs raised chicks that fledged. Mean fledging production for 34 pairs in 1996 and 1997 was 0.44 fledglings per breeding pair per year. Thirty of the 34 pairs observed used the same territory, in 1996 and 1997. Of the 30 pairs that occupied the same territory in both years, 16 pairs failed to raise chicks in both nears, seven pairs fledged chicks in one year and sewn pairs fledged chicks in both tears. Oystercatchers showed stronger site fidelity to territories where chicks were fledged than territories where they failed to raise young.
Article
In order to restore Black Oystercatchers (Haematopiis bachmani) and Pigeon Guillemots (Cepphus columba), 2 species injured by the T/V Exxon Valdez oil spill, the introduced predator, arctic fox (Alopex lagopus), was removed from 2 islands near the western edge of the trajectory of the oil. In 1994, most of the foxes (33 animals) were removed from Simeonof Island, and all foxes (3 animals) were eliminated from Chernabura Island. The remaining 5 foxes were removed from Simeonof by July 1995. Surveys indicated that although adequate nesting habitat was available at Simeonof and Chernabura, oystercatcher and guillemot population densities were much lower than at nearby fox-free islands. Elimination of foxes is expected to dramatically increase populations of these injured species as well as other native birds.