Rowan berries

Thor’s helper

Oh how I love some mythology that comes along with my favorite trees. Out front we have a Rowan tree, Sorbus aucuparia. It’s called a Mountain Ash around here, but I prefer Rowan as it’s an Old Norse name for tree, or raun. Celtics also called it the Traveler’s Tree. The tree has a lot of other names too: variations of Quicken, Ran/Roan/Roden, Sorb apple, Whispering tree, Whitty, Wiggen, Wiggy…wait, what?

But my favorite of its nicknames is Thor’s helper. According to legends written at Anna Franklin’s website, in Scandinavian mythology:

Thor was trying to get to the land of the Frost Giants when an evil sorcerer caused the River Vimur to overflow just as he was trying to ford it.  A rowan tree bent down so that he could grasp it and scramble to safety; consequently the rowan became known as ‘Thor’s helper’ or ‘Thor’s tree’. The tree may have been conceived of as Thor’s wife Sif who is usually associated with the golden grain of the harvest, though rowan fruit matures at the same time. Sometimes, the rowan is said to have sprung from a lightening strike, and to embody the lightening. Norse ships had one plank of rowan wood inserted into the hull to protect them from the wrath of Ran, the sea goddess, in the belief that Thor would look after his own.

Also, because the wood of the tree is very thick, it’s good for making walking sticks, magician staves, and druid staffs. I need one of those!

Rowan berries

Rowan trees typically grow their fruit, little orange pomes, in late summer. We’ve not really had much of warm summer, but I noticed today that the berries are here now.

The species is native in Europe, western Asia, and north Africa in the mountains of Morocco. I’ve seen it quite a bit in British Columbia too. There’s a good deal of information here about its habitat and relationships with forest plants and animals.


What’s new in the rainforest?


Climate change scene, licensed for use by Can Stock Photo

We have been very busy this summer promoting our other project, a site that has been cataloging nature and climate themed fictional books as well as notable nature essays since August 2013. Our newest branch of this site is a new community discussion group for writers and readers of environmental-themed books, whether cli-fi, sci-fi, speculative, literary, or non-fiction. All are welcome to join; all we ask for is topical and civil discussions. Still a fairly new community, we are still up to 65 members currently and growing! We would love your input and contribution, whether you are an author or reader!

In other news:

The Parksville Qualicum Beach Newspaper reported that very small and old hedgehog and tapir fossils have been found in northern British Columbia in Driftwood Canyon near Smithers; this area was a lake bottom in the early Eocene.

Pam Mullins was named BC Photographer of the month for her wildlife photography in the Great Bear Rainforest, according to HuffPost BC. Mullins lives on the Sunshine Coast.

Global News reports the province has released its 5-year species-at-risk plan.

American Bullfrogs are invading BC, according to E Canada Now. The frogs are deemed too large to be native to Canada.


Cli-Fi Books expands to include nature fiction


Clark’s Nutcracker, courtesy Wiki Commons

BC-based Cli-Fi Books is expanding its focus to “Nature Fiction” in order to include other nature-themed books and prose. Though the current domain will not change, the archive is now also directing the URL to its site. This expansion will hopefully attract more readers and authors since the climate change community is still fairly small and sometimes exclusive. The website plan is to continue to focus on climate change fiction but include other nature-themed fiction at the site. Cli-Fi Books is gracious for patience as they find a way to keep the genres separate while still equally accessible. They are committed, as they have been since the site’s inception, to helping climate fiction authors promote their books and find a place to call “home”. Expanding to include other nature books will widen the audience, merging the climate change and eco/nature fiction genres into one place, perhaps bringing more attention to both important categories of literature.

Nature Fiction’s Google discussion community and Twitter profile have also changed their titles. They will continue to focus on climate change literature while allowing other nature fiction topics. All who are involved in these genres are welcome to come and promote their books and discuss with others anything from the writing process to publishing tips to reviewing books they love.

The website notes that this expansion will happen over time, but it has already begun.


Rainforest news


Yellow cedar foliage and cones. Photo by Walter Siegmund.

Preemptive to a short holiday, here is a summary of rainforest news:

  • Cedars Threatened by Climate Change, Logging Would Be First Alaska Tree Ever Given Federal Protection: Biological Diversity reports that Conservation groups filed a formal petition today to protect yellow cedar trees under the Endangered Species Act because of ongoing threats from climate change and logging. Vast swaths of yellow cedars have died off in the past century, with more than 70 percent of these long-lived, beautiful trees now dead in many areas of Alaska. If approved, yellow cedar would be the first Alaska tree species, and only the second plant in the state, protected by the Endangered Species Act. Yellow cedar (Calliptropsis nootkatensis) are killed as the climate changes, spring temperatures warm, and snow melts: A lack of snow exposes their fragile roots to freezing temperatures, resulting in root freezing and tree death. Despite the trees’ decline, timber sales selectively target remaining living yellow cedar because of the wood’s high quality and market value.
  • Canada’s Court’s Lands Right Rights Ruling Could Affect Oil, Gas Pipelines: The Epoch Times reports that on June 26 the Supreme Court of Canada affirmed the Tsilhqot’in Nation’s claim on roughly 700 square miles of land—and, by extension, expanded the basis of all Aboriginal land claims in Canada.
  • Islands of Orcas and Eagles: Wendy Lemlin, a freelance author, writes about her experience at the San Juan Islands.

Another milestone for the Northern Gateway


Spirit bear, courtesy Wiki Commons

Updates: Natural Resources Minister Greg Rickford has given Engbridge’s Northern Gateway federal approval, subject to the 209 recommendations already made by the Joint Review Panel–this after several years of planning and preparation. The news is heavy for those of us categorically opposing the pipeline due to: First Nations objections to these twin pipelines in their territories; a future of supertankers and oil spills on our coast; an increase to carbon emissions during a climate change crisis; and the lack of concern for all life relying on the ecological health of our rainforest–some species considered rare and endangered, many being critical to one of the last and great temperate rainforests in the world.

What is the Northern Gateway?

The Northern Gateway Pipeline proposal would involve building a 525,000 barrel per day pipeline from the Alberta oil sands hub of Edmonton to Kitimat, British Columbia, on Canada’s Pacific Coast. The route would cross salmon-bearing streams and rivers, First Nations territories, and portions of the Pacific Temperate Rainforest. The pipeline exports would introduce mega-tankers to the coast, which will ship this oil out every day. A twin pipeline would transport would import natural gas condensate (the chemical/petroleum based mixture used to dilute tar sands) from the coast to Alberta. Read more about the negative aspects of this project in our Great Bear Rainforest series, Oil Sands Overview and Oil Sands Aren’t Forever.

Some key milestones
Sources: Save the Fraser, Reuters, and National Energy Board – Public Registry

  • March 6, 2002 – Enbridge makes initial plan for project
  • April 14, 2005 – Enbridge signs deal with PetroChina
  • July 15, 2005 – Enbridge begins research and engineering
  • Oct 3, 2005 –  Shipping support results in larger diameter pipeline
  • Sept 29, 2006 – National Energy Board releases Joint Review Panel draft
  • Oct 14, 2005 – Enbridge selects Kitimat, British Columbia, as terminus due to its deepwater port
  • June 27, 2006 –  Enbridge dismisses First Nations concerns about the project, projects 2010 as operational date
  • Nov 1, 2006 – Delay in Northern Gateway plan as US exports are prioritized
  • Feb 21, 2008 – Demand prompts rekindling of Gateway
  • Oct 6, 2009 – Announcement of rising costs
  • Dec 4, 2009 – National Energy Board announces Joint Review Panel agreement and terms of reference
  • January 20, 2010 – The National Energy Board announces establishment of the Joint Review Panel
  • March 23, 2010 – First Nations declare pipeline is too dangerous to environment and vow to block the pipleine
  • May 27, 2010 – Enbridge files Northern Gateway pipeline proposal with the National Energy Board
  • Nov 2010 – Yinka Dene Alliance signs the Save the Fraser Declaration
  • Jan 20, 2011 – China’s Sinopec Corp is announced as one backer to fund pipeline
  • Aug 24, 2011 – Northern Gateway terms agreed upon by Enbridge and its shippers
  • Sept 22, 2011 – Joint Review Panel gives more time to speakers
  • Dec 2, 2011 – Gitxsan First Nation becomes first aboriginal partner for Northern Gateway project (the deal collapses one month later)
  • Jan 4, 2012 – Suncor Energy Inc, Cenovus Energy Inc, MEG Energy Corp, Nexen Inc and Total SA back Northern Gateway
  • Jan 9, 2012 – Natural Resources Minister Joe Oliver accuses Northern Gateway pipeline opponents as foreign-funded radicals
  • Jan 10, 2012 – Joint Review Panel hearings open in Kitimat
  • Jan 18, 2012 – Gitxsan First Nation no longer backs Northern Gateway
  • Feb 10, 2012 – Canadian Prime Minister Stephen Harper backs pipeline
  • June 5, 2012 – Enbridge claims it has 60% of First Nations (who live along proposed route) approval
  • July 20, 2012 – Enbridge pledges new safety measures
  • July 23, 2012 – British Columbia government lays out 209 conditions for pipeline
  • Sept 26, 2012 – Environmental groups sue Canada over Northern Gateway
  • April 12, 2013 – Joint Review Panel issues draft conditions
  • May 31, 2013 – Government of British Columbia rejects Northern Gateway
  • Nov 5, 2013 – Alberta and British Columbia announce framework agreement
  • December 5, 2013 – The Yinka Dene Alliance (YDA) welcomes a new signatory for the Save the Fraser Declaration and announces Solidarity Accord (PDF)
  • Dec 19, 2013 – The National Energy Board releases its Northern Gateway decision
  • June 17, 2014 – Natural Resources Minister Greg Rickford approves the Northern Gateway project, subject to the 209 recommendations already made by the Joint Review Panel

The Great Bear Rainforest

Moon Willow Press hosts an ongoing series about the Great Bear Rainforest. Update: We are working on a book and video about the rainforest, which will be published in 2015. After this time, we will continue with the series. Read more here.

The Great Bear Rainforest of British Columbia (BC), Canada, is part of the Northwest Pacific Temperate Rainforest and was named by environmentalists campaigning to protect and conserve this 70,000 km2 kilometer area. Temperate rainforests are rare, and the Great Bear, along with the rest of the rainforest, makes up one-fourth of all temperate rainforests in the world. It’s also special because it is the largest undeveloped temperate rainforest on the planet and has a number of unique species. Moon Willow Press’s goals are to educate people about the forest and about threats to the area, including the proposed Enbridge Northern Gateway Pipeline project, which would run twin pipelines through a portion of this forest as well as introduce more tankers, along with supertankers, to the coastal ecosystem that makes up the Great Bear.

Note that some of these articles date back a few years, and links inside have changed but originally were intact. We make attempts to find where URL archives have been changed.

Read more: Part 1: Oil Sands Overview | Part 2: Ancient Realm | Part 3: The Spirit Bear | Part 4: Wolves Lost in Time | Part 5: A Journey in the Making | Part 6: The Old-Growth | Part 7: Serengeti of the North | Part 8: Oil Sands Aren’t Forever | Part 9: Wild Salmon  | Part 10: People of the Rainforest, Since Time Immemorial | Part 11: Inside and Outside of the Skeena


Vancouver-based climate change short story contest


New: Short story submission form

As announced today at the Vanpoet’s blog, we will be hosting Vancouver’s 3rd annual 100,000 Poets for Change event–an event that takes place around the world in hundreds of cities simultaneously. This year, unlike in past years, we will be hosting a virtual event in the form of a short story contest. The contest begins whenever you are ready (now if you want!) and ends sharply at August 30, midnight EST. I will try to get the Vancouver authors I know on board, but this contest is global and EVERYONE is welcome to join. Even though we’re based out of Vancouver, we need to stand together from all corners of the Earth when it comes to climate change. In a potential multi-media presentation, Vancouver will be represented well.

Visit the Vanpoet’s blog above or join our newly formed Cli-Fi community group on Google to stay updated!

Here are the contest rules. Please follow them. I’d hate to see any disqualifications. Click here to submit your story.

  • The stories will be due at midnight EST, August 30, but in order to carefully read the stories, we will not announce the winner until September 26, the day before  the 100,000 Poets for Change event. We’ll have a big to-do about it on the 27th, the big day of the event. (Keep in mind “poets for change” is for all artists–short story writers included!)
  • The winner will receive $100.00 USD (there is no entry fee). The award is offered via Paypal, or, if you prefer, as an Amazon gift card.
  • The story must be between 3,500-5,000 words.
  • Your story must relate to climate change and must be fiction.
  • The story must be your own. You may not plagiarize another story.
  • The story will be judged by Mary Woodbury, of Cli-Fi Books. If available, we may get a panel to help judge the stories.
  • The submission form requires you to upload your story (and optionally a photo, explained below) and check off a few boxes that say you agree we can host your story at our site. For instance, the story must not have been previously published. While you retain copyright, we want to host the story at Cli-Fi Books for a short period of time after the contest ends.
  • You must be at least age 18, and if not, you must send in writing a permission form signed by your parents to Cli-Fi Books.
  • For each story submission, there will be extra credit given if you also submit a photo of your city showing any nature scene, environment being impacted, climate change impact, or something similar. The photo doesn’t need to synch with your story, just with your city!
  • The photo you provide MUST be one that you have taken and have the rights to share. You will be credited as the photographer.
  • Permissions to use these photos in a collage, on the website, and potentially in video, must be given. While it will help if the photos are in early (you can submit any time), they are due no later than August 30.
  • Your full name and email address is required for the stories and photos to make it into this contest! Your email must match either a Paypal or Amazon account so that we can properly transfer you the reward if you win. Only one entry per person. Your email address will not be published.

Republished from Cli-Fi Books


Consumers can support ethical treatment of food animals


Holstein cow, courtesy Wiki Commons and Agricultural Research Service

Recently CBC reported that an undercover employee at Chilliwack Cattle Sales, Canada’s largest dairy farm, videotaped other employees blatantly abusing cattle at the business. The undercover agent was from Mercy for Animals Canada, an advocacy organization dedicated to preventing cruelty to farmed animals and promoting compassionate food choices and policies. The video has a strong content warning as it shows violence toward animals. The owner of Chilliwack Cattle Sales, Jeff Kooyman, is currently under investigation. The company’s management fired the eight employees who were caught abusing cattle, and the BC SPCA is recommending criminal charges against the employees.

Mercy for Animals states on its website:

Over 99 percent of the cruelty to animals in Canada occurs at the hands of the meat, dairy, and egg industries, which confine, mutilate, and slaughter approximately 700 million land animals each year. Despite the fact that huge numbers of farmed animals are badly abused in Canada, they have very few advocates. That is why it is so important that we stand up and speak out for the most defenseless.

This travesty begs the question: what can we, as consumers, do to make ethical food choices? We might have the best intentions, but how do we know for sure that the farms we buy from are responsible? It was an active discussion among some peers recently. One, Courtney Niesh, has been inspirational to me to become a vegetarian. But it’s not that I think eating meat is bad. I think that the process of assembly-lined living animals, that are treated badly for my meat, dairy, and egg consumption, is an act that I want no participation in, even if the end product is a sizzling steak or my mom’s great southern fried chicken. I sought an end to eating that kind of meat after watching this clip from the film Baraka.

As consumers, you and I have all the power in the world to investigate and decide where we get our food sources. For instance, Courtney took it in her own hands to write to farms that produce the cheese she buys–curious about their ethics when it came to animals. Her question was:

Hi there, I am from Vancouver BC and have recently read an article about cows that were mistreated at a dairy farm in Chilliwack, BC. I am a vegetarian as I am concerned about the mistreatment of farm animals, but I do consume cheese. I am wondering if you could tell me what farms you get your dairy from and if you make an effort to ensure animals at the farm are treated humanely; this includes living conditions.

Quebec-based Saputo is currently also under fire since Chilliwack Cattle Sales is the main supplier for Dailyland, owned by Saputo since 2001. Saputo’s response was immediate and thanked Courtney for her questions. Saputo said that they are committed to the highest standards and ethics and were horrified by the abuse of dairy cows in BC. As Canada’s largest dairy processor, they stated that they will not tolerate animal abuse and commended the termination of the Chilliwack Cattle Sales employees who abused the animals.

Saputo also told Courtney that they have asked the BC Milk Marketing Board (BCMMB) and other BC authorities to put enforceable standards in place to ensure such incidents do not occur in the future. Supportive of  the BC SPCA’s recommendation that the Canadian Code of Practice for the Care and Handling of Dairy Cattles, Saputo said that they asked the BCMMB and other BC authorities to likewise support the BC SPCA’s recommendation.

Saputo clarified that it does not own or oversee any dairy farms in BC or elsewhere in Canada. Along with all other dairy processors in BC they purchase milk solely from the BCMMB, which is responsible for the pooling of milk from farms throughout BC. However, they still recognized their responsibility to act as a leader in the industry in order to help bring about change.

Courtney also wrote to Kraft–asking the same question she had asked Saputo. Kraft’s response was less enthusiastic, just a quick sentence stating that the requested information was not currently available.

Courtney buys her Parmesan from Clover Leaf Cheese Ltd., and asked them the question she had posed to Kraft and Saputo. They responded that Clover Leaf Cheese is a federally registered packaging plant that purchases all their Canadian cheese from other federally registered companies such as Agropur, Saputo, and Parmalat. They count on these companies and the CFIA to monitor the environment the cows live in on the farms they contract.

When it comes down to it, it seems that most corporations have it in their best interest to monitor the living conditions, health, and well-being of food animals, but there are people working in the industry who will not comply and even, as shown in the video, do just the opposite: treat animals cruelly and sadistically.

The consumer always has responsibility, and thus the power, to make choices, including what food we buy. Fortunately, some organizations have already done the legwork to help us know which food sources are ethically produced. Writing to companies, like Courtney did, is one avenue.  You can also visit the BC SPCA and find a SPCA Certified Retailer.


Gray wolf differentiation in BC rainforest

Population genetic structure of gray wolves (Canis lupus) in a marine archipelago suggests island-mainland differentiation consistent with dietary niche

Authors: Astrid V Stronen12*, Erin L Navid3, Michael S Quinn4, Paul C Paquet56, Heather M Bryan567 and Christopher T Darimont567*


Emerging evidence suggests that ecological heterogeneity across space can influence the genetic structure of populations, including that of long-distance dispersers such as large carnivores. On the central coast of British Columbia, Canada, wolf (Canis lupus L., 1758) dietary niche and parasite prevalence data indicate strong ecological divergence between marine-oriented wolves inhabiting islands and individuals on the coastal mainland that interact primarily with terrestrial prey. Local holders of traditional ecological knowledge, who distinguish between mainland and island wolf forms, also informed our hypothesis that genetic differentiation might occur between wolves from these adjacent environments.


We used microsatellite genetic markers to examine data obtained from wolf faecal samples. Our results from 116 individuals suggest the presence of a genetic cline between mainland and island wolves. This pattern occurs despite field observations that individuals easily traverse the 30 km wide study area and swim up to 13 km among landmasses in the region.


Natal habitat-biased dispersal (i.e., the preference for dispersal into familiar ecological environments) might contribute to genetic differentiation. Accordingly, this working hypothesis presents an exciting avenue for future research where marine resources or other components of ecological heterogeneity are present.


Recent evidence indicates that ecological and environmental variation can result in genetic differentiation within many taxa, including highly mobile species. Examples include sea turtles (reviewed in Bowen and Karl [1]), fish species such as herring (Clupea harengus L., 1758; André et al. [2]) and hake (Merluccius merluccius L., 1758; Milano et al. [3]), and mammal species including the orca (Orcinus orca L., 1758; Hoelzel et al. [4]), cougar (Puma concolor L., 1771; McRae et al., [5]), lynx (Lynx canadensis Kerr, 1792; Rueness et al., [6]), coyote (Canis latrans Say, 1823; Sacks et al. [7]), and wolves (C. lupus L., 1758; Musiani et al. [8]; Pilot et al. [9]; Weckworth et al. [10]–[12]). For example, Muñoz-Fuentes et al. [13] showed strong genetic divergence over distances less than 500 km between wolves of coastal and interior regions of British Columbia (BC), Canada. Ecological and environmental dimensions such as climate and prey availability between areas, not distance, best explained population structure. These patterns arise because individuals may be more likely to survive and reproduce within their natal habitats (Davis and Stamps [14], Nosil et al. [15], Edelaar et al. [16]), which, in turn, can influence population genetic structure. A prediction from this body of work is that genetic divergence might be detected even over short geographical distances, and for highly mobile animals, should there be a sharp gradient in environmental conditions.

Such sharp ecological transitions occur between mainland and adjacent island environments within coastal BC. Although distances between mainland and neighbouring islands are small (<1500 m), the environments have striking geological and ecological differences. The mainland is topographically rugged, contains less shoreline for a given area and is relatively species-rich. In contrast, the neighbouring islands are less mountainous, have more complex shorelines, and host fewer species; notably absent are grizzly bears, (Ursus acrtos horribilis Ord, 1815), which compete with wolves for marine resources (Darimont and Paquet [17]; Paquet et al. [18]). Owing to these different environments, analyses of faeces and stable isotope data have identified distinctly different realized niches. Wolves from island populations rely on marine resources for up to 85% of their diet, whereas mainland conspecifics rarely include more than 30% (Darimont et al. [19,20]). Additionally, the coastal mainland supports moose (Alces alces L., 1758) and mountain goats (Oreamnus americanus Blainville, 1816) that are absent or rare on coastal islands. Consequently, these major prey items are commonly detected in wolf diet in mainland areas and only very rarely on islands (Darimont et al. [21]). Moreover, likely reflecting these distinct habitat and dietary niches, parasite prevalence also differs between areas; there is higher faecal prevalence of Giardia sp. infections on islands and a lower prevalence of Diphyllobothrium sp. relative to mainland sites (Bryan et al. [22]).

Our objective was to examine genetic data from wolves of coastal BC over a limited geographic area (~2000 km2, with a generally east–west mainland-island axis of <30 km) to test the hypothesis that ecological heterogeneity can drive population genetic structure of a highly mobile animal within a small area. We note that this prediction was also informed by holders of traditional ecological knowledge (TEK) in the Heiltsuk First Nation area, who distinguish between mainland “timber wolf” and island “coastal wolf” forms. Given these scholarly- and TEK-informed hypotheses and the sharp environmental gradients on the BC coast, we expected mainland-island genetic differentiation that mirrors ecological differences among neighbouring social groups.


Study area

The central coast of BC is a remote network of islands and naturally fragmented mainland landmasses with limited (but increasing) industrial anthropogenic disturbance. The area is characterized by a wet and temperate climate, and annual precipitation typically exceeds 350 cm (Darimont and Paquet [17]). A core area (~2000 km2) centered on Bella Bella (52°10’ N, 128° 09’ W) served as the location for this study (Additional file 1). This landscape is surrounded by ocean, which separates a mainland landmass (823 km2) and five main islands ranging in size from 150–250 km2. Distances from island to mainland range from 250 m to 1450 m. Observational and genetic data (Darimont et al. [19]; Navid [23]) suggest that wolf packs, defined by the multi-year association of genetically and morphologically distinct individuals, have either island or mainland home ranges. However, one group (Yeo-Coldwell [YC]) primarily uses island habitat but also a portion of the adjacent mainland. Other units are either mainland groups (Upper Roscoe [UR], Lower Roscoe [LR] or island groups (Cunningham-Chatfield [CC], Denny-Campbell [DC]). Moreover, wolves are commonly observed swimming among landmasses, and home ranges of social groups often include multiple islands or mainland landmasses (e.g. peninsulas; Paquet et al. [18]; Darimont [24]; McAllister and Darimont [25]).

Additional file 1. Map of the study area on the central coast of British Columbia, Canada. Shown are estimated home ranges of five wolf (Canis lupus) social groups.

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One thousand and seventy-four (1074) wolf faecal samples were collected between winter 2003 and winter 2004. We collected the following number of samples per season: spring: n?=?416 summer: n?=?297 fall: n?=?292 winter: n?= 69. Sampling areas included wildlife trails, logging roads, and electrical power rights-of-way. We preserved each sample in a 50-ml Falcon tube with 95% ethanol. We selected samples for genetic analysis based on characteristics of the samples and collection sites that best predicted amplification success (minimal physical decay, high moisture content, canopy cover; Navid [23]). We extracted DNA from faecal samples with Qiagen QIAamp® DNA Stool Mini Kits and the ‘Protocol for isolation of DNA from larger amounts of stool’ (QIAamp® DNA Stool Mini Kit handbook, We performed DNA extractions in a room physically separated from amplified PCR products and used exclusively for this study to reduce the risk of contamination. Final purified extracts were refrigerated at +4°C until use.

Microsatellite amplification

We amplified a panel of 14 microsatellite markers (13 autosomal and one Y chromosome marker). These were FH2001, FH2010, FH2017, FH2054, FH2088, FH2096, FH2422 (Breen et al. [26]), FH3313, FH3725 (Guyon et al. [27]), PEZ06, PEZ08, PEZ15, PEZ19 (Halverson J. in Neff et al. [28]), and the Y-chromosome marker MS41B (Sundquist et al. [29]). We genotyped n?=?477 faecal samples. Polymerase chain reaction (PCR) conditions optimized for the markers, based on the Qiagen multiplexing kit, were: initial denaturation at 95°C for 15 minutes, then 35 cycles of denaturation at 94°C for 30 sec, annealing at 58°C for 90 sec, extension at 72°C for 60 sec, with final extension at 60°C for 30 min. Organisation of markers into multiplexes is shown in Additional file 2. Amplified PCR product was loaded into a 6.5% denaturing polyacrylamide gel, and run on a LICOR4300s DNA analyzer. Genotyping was done with a LICOR’s SAGA GT version 3.3 microsatellite analysis software.

Additional file 2. Multiplex combinations of 14 microsatellite markers for genetic analyses of wolves from the central coast of British Columbia, Canada.

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We accepted for further analyses samples that amplified at least 9/14 loci, and used the Excel Microsatellite Toolkit (Park [30]) to test for the presence of matching profiles. We consolidated matches (i.e., profiles with???75% matching alleles to account for uncertainty in genotyping) into one profile and retained the profile with the highest amplification rate. Matches were tested across all samples, but only observed within wolf groups and 33 profiles were removed.

N?=?116 individual profiles were identified. Only 28 wolves (24%) were identified as males, and we observed six alleles for marker MS41B (209, 211, 213, 217, 219, 221). We used MICRO-CHECKER 2.2.3 (van Oosterhout et al. [31]) to assess possibilities of null alleles, large allele dropout, and scoring errors due to stutter peaks. We repeated genotyping of 50 samples collected during the fall season (deemed to represent 50 different individuals from all four groups [CC, DC, LR, YC] based on the abovementioned criteria) to evaluate data quality and estimate genotyping error. Here we estimated per-locus error rates based on the percentage of loci that did not show the same result twice (Additional file 3). Loci for which we obtained the same results twice were accepted as duplicated loci. From these results we obtained duplicate genotypes comprising five or more loci for 18 individuals (i.e. every locus in each of these 18 genotypes provided consistent results when re-tested). Based on amplification and error rates (Additional file 3) we removed MS41B, PEZ08, FH2017, and FH3313 from further analyses.

Additional file 3. Calculation of amplification and error rates and assessment of null alleles, large allele dropout, and stutter peaks for wolf samples (n?=?116) from the central coast of British Columbia, Canada.

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Statistical analyses

We calculated allelic diversity and observed and expected heterozygosity (with correction for sample size bias; Nei [32]) per locus in GENEPOP 3.4 (Raymond and Rousset [33]) and Genetix 4.05.2 (Belkhir et al. [34]), and FIS according to Weir and Cockerham [35], for mainland and island wolves. We tested for departures from Hardy-Weinberg equilibrium per locus in GENEPOP 3.4 with the Markov chain method (Guo and Thompson [36]). The results were adjusted to account for multiple comparisons with the false discovery rate (FDR, Verhoeven et al. [37]). Subsequently, we performed centered and scaled principal component analyses (PCA) with the adegenet-package (Jombart [38]) in R 2.14.2 (R development Core Team [39]). The PCA approach does not assume genetic equilibrium conditions and is well-suited for identifying spatial patterns such as genetic clines (i.e., gradients rather than separate clusters or complete admixture) that can be difficult to detect (Jombart et al. [40]). We repeated the PCA with the 18 individuals for which we had duplicated genotypes (and thus higher confidence), and all wolves identified as males to confirm the presence of the observed cline. On average, male wolves may disperse longer distances than females and are more likely to join new packs. As our data set comprised relatively few confirmed males, we tested these results separately to check if the island-mainland gradient remained consistent. A high proportion of females in our sample might otherwise have contributed to the observed gradient if females disperse less frequently and/or shorter distances than males. We then performed a spatial PCA (henceforth sPCA; Jombart et al. [40]), which also takes spatial sampling information into account. As multiple samples were at times collected from the same location, we added 100 m of jitter (small amount of noise) to the UTM coordinates. We performed a spatial autocorrelation in GenAlEx (Peakall and Smouse [41] and references therein) to examine the possible existence of isolation-by-distance in our data set. We used distance classes of 5 km to obtain fine-scale results for our study area. Finally, we performed a partial Mantel-test in R with the Vegan package (Oksanen et al. [42]) to examine the relationship between genetic distance and island-mainland habitat type while controlling for geographic distance. This allowed us to test whether there was an effect of habitat type on fine-scale genetic structure after accounting for the effect of geographic distance. Geographic distance and habitat may be co-linear and their effects could be difficult to separate. Consequently, we also examined the relationship between geographic distance and habitat type. For these tests we incorporated co-dominant genotypic and Euclidean geographic distance matrices exported from GenAlEx and a third matrix with island-mainland habitat designations. We used Pearson’s correlation coefficient with n?=?999 permutations.


The average number of alleles per locus was 5.8 for mainland wolves and 6.8 for island wolves (Table 1). For mainland wolves, expected heterozygosity was 0.632 and five loci showed departures from Hardy-Weinberg equilibrium with observed levels of heterozygosity lower than expected. FIS results were positive for all loci with a mean value of 0.264. For island wolves, expected heterozygosity was 0.690 and seven loci showed departures from Hardy-Weinberg equilibrium (four of these were consistent between mainland and island wolves). FIS results for island wolves were positive for all except two loci, with a mean value of 0.211. We identified possible null alleles and stutter peaks for the overall sample, but dropout of large alleles was not detected (Additional file 3).

Table 1. Genetic diversity measures for wolves ( Canis lupus ) from the central coast of British Columbia, Canada

PCA results indicated the presence of a genetic cline between island and mainland wolves (Figure 1a, c). Although overlap was extensive, the results suggested an east–west gradient in profiles across?<?30 km. Examination of genetic profiles based on the known wolf groups in the area (UR was not represented in the second analysis) suggested limited overlap between LR (Mainland) and YC (Island) wolves (Figure 1b, d). The CC and DC island groups occupy an intermediate position, along with the UR group from the mainland. Colour plots (Additional file 4) show the individual genetic profiles throughout the study area, and display a similar east–west gradient from the mainland to the islands. The PCA results for individuals identified as males (n?=?28) were consistent with island-mainland differentiation (Additional file 5). For the sPCA, one global structure (and no local structure) was apparent (Additional file 6). When mapped across the geographic space, the global structure revealed an east–west gradient where YC and LR were the most differentiated groups (Figure 2). The partial Mantel test gave a correlation coefficient of 0.011 (p-value 0.351) between genetic distance and habitat matrices. The test between geographic distance and habitat matrices produced a correlation coefficient of 0.568 (p-value 0.001). Spatial autocorrelation results were positive for the first 17 km, negative from approximately 17–45 km, and subsequently positive (though this may be considered as zero autocorrelation at the larger distance classes with wide confidence intervals; Additional file 7).


Figure 1. Principal component analyses of wolves (Canis lupus) from the central coast of British Columbia, Canada showing geographic distribution of individuals. a) Individual (n?=?116) profiles based on???10 microsatellite loci labelled according to mainland (MA) and island (IS) sample locations. b) Individual profiles (n?=?116) based on???10 microsatellite loci labeled according to membership in five wolf family groups: Upper Roscoe (UR) and Lower Roscoe (LR) on the mainland, and Yeo-Coldwell (YC), Cunningham-Chatfield (CC), and Denny-Campbell (DC) islands. Note that the label for DC (green colour) is overlapped by UR (red colour). c) A subsample of individual profiles (n?=?18) with duplicated genotypes based on???5 loci labelled according to mainland and island sample locations. d) Individual profiles (n?=?18) with duplicated genotypes based on???5 loci labelled according to membership in four wolf family groups LR, YC, CC, and DC (none from UR).

Additional file 4. Colour plot of wolf profiles from the central coast of British Columbia, Canada. a) Individual profiles (n?=?116) based on???10 microsatellite loci. The first axis represents 6.1% of the variation, the second axis 5.1%. b) A subsample of individual profiles (n?=?18) based on???5 duplicated loci. Genetic diversity is represented by distance and colour; individuals further apart and/or labelled with more dissimilar colours have more divergent genotypes. The first axis represents 18.8% of the variation, the second axis 13.9%.

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Additional file 5. Principal component analysis (PCA) of male wolves from the central coast of British Columbia, Canada, showing island (IS, n?=?19) and mainland (MA, n?=?9) individuals. The first axis represents 11.9% of the variation, the second axis 9.7%. PCA is based on the 10 loci retained for final analyses (Table 1 and Additional file 2).

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Additional file 6. Eigenvalues from a spatial principal component analysis (sPCA) on 10 microsatellite loci from 116 wolves from the central coast of British Columbia, Canada. Positive values (left side) represent global structures and negative values (right side) show local patterns. Tests for local and global structure revealed the presence of one global structure, which was subsequently interpreted.

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wolves2Figure 2. Spatial principal component analysis of wolves (Canis lupus) from the central coast of British Columbia, Canada, showing the first global structure mapped across the study area. Individual profiles (n?=?116) are based on???10 microsatellite loci and originate from five wolf family groups: Upper Roscoe (UR) and Lower Roscoe (LR) on the mainland, and Yeo-Coldwell (YC), Cunningham-Chatfield (CC), and Denny-Campbell (DC) islands.

Additional file 7. Spatial autocorrelation analysis of wolf samples (n?=?116) from the central coast of British Columbia, Canada, using 5 km distance classes. The Y axis shows the kinship coefficient (r), and U and L are the upper and lower limits for the 95% confidence interval of no spatial structure occurring in the data set after permutation (n?=?999). Error bars show the 95% confidence interval around r as determined by bootstrap resampling (n?=?999).

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Genetic variation

Allelic diversity and expected heterozygosity for island wolves (6.8, 0.690) and mainland wolves (5.8, 0.63) were relatively high and comparable to values reported for island populations of wolves on the Pacific Coast in southeast Alaska (5, 0.52; Weckworth et al. [10]) and coastal island populations in Arctic Canada (4.2, 0.61; Carmichael et al. [43]). Allelic diversity and expected heterozygosity were somewhat lower for wolves on the mainland portion of our study area, although this might, at least in part, reflect chance effects of our relatively small sample sizes. Comparison with FIS values from southeast Alaska islands (0.05) and coastal islands in Arctic Canada (0.181) suggest a higher degree of mating among relatives in mainland (0.264) and island (0.211) wolves from coastal BC. However, Carmichael et al. [43] also observed high FIS values on Victoria Island (0.427, n?=?52) and on islands in the High Arctic (0.629, n?=?11). Based on the findings from Alaska wolves, continental wolves appear to have higher genetic diversity. We would also expect a similar situation for our study area, as mainland wolves have a wider surrounding area from which to receive immigrants. However, there are known wolf groups on neighbouring islands not included in this study and we cannot exclude the possibility that immigration from these areas may have augmented the diversity in our sample of island wolves.

Non-invasive sampling and genotyping

Allelic dropout in non-invasive sampling (Santini et al. [44]) could, at least in part, explain the lower values for observed heterozygosity, the high number of loci not in Hardy-Weinberg equilibrium, and the positive FIS values. Our results could also have been influenced by the presence of null alleles. When most of the loci indicate null alleles, however, the MICRO-CHECKER program warns there may not be random mating in the population (panmixia). The PCA and sPCA findings of island-mainland differentiation suggest absence of panmixia in our study area. We therefore believe that island-mainland structure contributed to the frequent reports of null alleles. The study area is difficult to access, and many samples may have been several weeks old and thus affected by exposure to the humid climate (Santini et al. [44]; Navid [23]). Our results are based on analyses of faecal material, where duplicated genotypes were obtained for 15% (18 of 116) individuals. Error rates were high, but we do not expect any consistent bias between areas. Results from the duplicated genotypes accord with the larger dataset, although further sampling and multiple-tube analyses (e.g. Santini et al. [44]) would be necessary for accurate identification of individual wolves and to confirm dispersal events in our study area.

Evolutionary ecology and genetic differentiation between mainland and island wolves

The partial Mantel test showed no significant relationship between genetic distance and island-mainland habitat type when accounting for geographic distance. However, there was a significant correlation between geographic distance and island-mainland habitat type, suggesting that the two matrices are collinear and their effects cannot be differentiated. The spatial autocorrelation indicated negative autocorrelation from approximately 17–45 km. These results appear to contrast with those of Muñoz-Fuentes et al. [13] who reported that geographic distance was unlikely to explain the spatial structure of wolf mtDNA haplotypes in a broader study of coastal and central BC. Wolves are highly capable dispersers able to travel?>?70 km/day (Mech and Boitani [45]), and it seems unlikely that geographic distance alone can explain the island-mainland structure suggested by the sPCA. In such a situation, we would expect the spatial autocorrelation results to show consistent (and increasing) negative kinship-values with geographic distance. In contrast, the 45–50 distance class that represents wolves in the northern- and southernmost parts of our study, which are farthest apart in geographic distance, showed positive values (or, more likely, no autocorrelation). Multiple interacting factors, including distance, water, terrain ruggedness etc., may affect genetic structure in our study area. Although it is essential to evaluate the possible influence of physiography on the differences observed between island and mainland wolves, the observed correlation between geographic distance and habitat type combined with the physical complexity of the landscape make it problematic and potentially misleading to use linear distances for estimating wolf movement.

Water barriers between the mainland and islands might restrict dispersal and gene flow. For example, captive wolves released on Coronation Island in Alaska did not swim 900 m to nearby habitat with abundant food (Klein [46]). In our study, we reject this hypothesis because we commonly observe wolves swimming among landmasses and distances among islands (including the multiple landmasses used by some groups) are often larger than the distances between islands and the mainland (Darimont et al., unpublished data). Immigrants from outside the study area could also influence the observed east–west gradient in genetic profiles. The differentiation seen in YC profiles, for example, may be explained by gene flow from unsampled wolves on the outer islands father west. Similarly, profiles from the UR group, which showed considerable overlap with island wolves, might result from immigration by one or more island wolves with high reproductive success. Furthermore, the presence of intermediate profiles in the north and south of our study area implies an island-mainland gradient. A strict island-mainland dichotomy may thus be simplistic and should be evaluated on a broader geographic scale. Without genetic data from a larger spatial extent, however, we cannot evaluate these hypotheses.

Family group structure might also have influenced our results, especially for long-lived animals for which the genetic influence of one successful breeder can be detected for many generations. Difficulties with amplification of MS41B likely reduced our ability to identify male wolves. A possible higher prevalence of females in the sample might nevertheless exacerbate genetic structuring in species where males are more likely to disperse. However, male wolf profiles we assessed showed a similar island-mainland gradient. Observational and tracking data suggest that wolf group size in the study area was???10 individuals (Darimont [24]), and it appears unlikely that the observed gradient in genetic profiles could be explained by social structure (i.e., wolf pack membership) alone. The identification of 116 individuals in the study area appears reasonable for a sampling period that included 2 litters, winter pup mortality that may exceed 50%, and the likelihood that 20% of individuals would be solitary or extra-territorial dispersers (Mech and Boitani [47]).

Despite the above-mentioned uncertainties, we offer the working hypothesis that the sharp ecological gradient between island and mainland locations, as revealed by the landscape characteristics and the dietary and parasitic data from wolves in our study area, can influence population genetic structure. Although our study must be interpreted with caution, and should be repeated with genetic profiles of higher quality, the results appear consistent with an increasing body of literature reporting genetic differentiation in wolves and other highly mobile species (see Introduction) influenced by ecological and environmental factors. Dispersal rates and gene flow might differ substantially between island and mainland sub-populations, and the extent to which populations are demographically independent could help define management units (Palsbøll et al. [48]) along the Pacific coast.

Associated morphological or other characteristics observed over time might have allowed TEK knowledge holders to recognize these dissimilar wolf forms. Such intra-specific nomenclature is common among indigenous knowledge holders (Turner et al. [49]). Indeed, in adjacent southeast Alaska, the frequency of the black colour phase among wolves killed by trappers is ~50% on the mainland and only ~20% on the islands (Person et al. [50]). Additional morphological differences among wolves of coastal BC might have led to mainland-island classification by local people.

The evolutionary influence of marine resources, which are pronounced on islands in our study area, can be dramatic for terrestrial wildlife. For example, polar bears (U. maritimus Phipps, 1774) are thought to have evolved from grizzly bears in peripheral areas where marine resources were abundant (Shields et al. [51]). Moreover, wolves of coastal BC (mainland and island populations) were thought to have diverged from interior populations in part because of marine resource availability in coastal zones (Muñoz-Fuentes et al. [13]). Individuals born in this distinct environment are likely better able to survive and reproduce within, compared to beyond, these conditions.


Our results indicate the presence of a genetic cline between island and mainland wolves. Although overlap was extensive, the results suggest an east–west gradient in profiles across?<?30 km. We hypothesize that adaptive responses to heterogeneity in food resources can influence genetic differentiation. Accordingly, this line of inquiry presents an exciting avenue for future research where marine resources or other components of ecological heterogeneity are present.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

ELN carried out laboratory work, and contributed to fieldwork, statistical analyses, manuscript writing and revisions. AVS performed statistical analyses and manuscript writing. MSQ supervised the study and helped revise the manuscript. CTD participated in study design, fieldwork, and manuscript writing. PCP contributed to study design, field work and manuscript revisions. HMB created the maps and helped revise the manuscript. All authors read and approved the final manuscript.


We thank the Heiltsuk First Nation for their permission to study an important cultural animal in their traditional territory. We also thank J. Carpenter, J. Gordon-Walker, B. Mann, M. Musiani, M. Niedzia?kowska, G. Pflueger, Y. Plante, and C. Service for field, laboratory and/or intellectual contributions. We are especially grateful to Chester Starr (Lone Wolf) of the Heiltsuk Nation for the knowledge he shared with us, which inspired this work. Raincoast Conservation Foundation, National Geographic Society, Wilburforce Foundation, and the Tula Foundation supported this work. ELN was supported by a Natural Science and Engineering Research Council Graduate Scholarship. CTD was supported by Natural Science and Engineering Research Council Discovery Grant no. 435683. J. Olano-Marin and B. Martinez-Cruz offered constructive comments on an earlier version of the text. We thank the editor and two anonymous reviewers for their assistance in improving our manuscript.


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Spirit of the Coast

056Spirit of the Coast is a three-month canoe paddle that began this past Sunday in Fort Langley and will end in Alaska. The trip, led by wilderness guide Chris Cooper, will encompass 1,300km up through BC in order to raise awareness of the beautiful coast. The trip is not about a protest, but about celebration. According to Cooper:

Spirit of the Coast is about awareness, education, culture, environment and most of all bringing attention to our beautiful BC Coastline and to share with Canadians what an amazing place we have, it is not a protest, but about educating all that have never seen it.

The trip is also to bring awareness of the First Nations communities of BC.

People from various countries, including South Africa, England, Scotland, the United States, Ontario, and Canada will be paddling.

Kwantlen Cultural Centre
Kwantlen Cultural Centre
Kwantlen Cultural Centre