The identification of factors that predict trends in population abundance is critical to formulate successful conservation strategies. Here, we explore population trends of Canadian vertebrates assessed as “at-risk” by the Committee on the Status of Endangered Wildlife in Canada and the threats affecting these trends using data from the Canadian Living Planet Index. We investigate how threat profiles—the combination of threats for a given species—vary among species and taxonomic groups. We then investigate threat profile as a predictor of temporal trends—both exclusively and in combination with additional biotic and abiotic factors. Species had 5.06 (±2.77) threats listed on average, and biological resource use (BRU) was the most frequently cited. Our analysis also revealed an association between taxonomic group and population trends, as measured by the proportion of annual increases (years with a positive interannual change). By contrast, the predictive power of threat profile was poor. This analysis yielded some useful insight for conservation action, particularly the prioritization of abating BRU. However, the predictive models were not as meaningful as originally anticipated. We provide recommendations on methodological improvements to advance the understanding of factors that predict trends in population abundance for prioritizing conservation action.

To what extent are threat profiles useful for predicting trends in abundance, either alone or in combination with other biological and physical factors (e.g., taxa, generation length, and protected area)?

Here, we explore variation in population trends of Canadian at-risk species using data from the Canadian LPI and their association with a small set of predictor variables, including threat profile, to investigate the following:

The Canadian LPI ( WWF Canada 2017 ) is a recent assessment of temporal trends of abundance in 3689 monitored populations of 903 vertebrate species and reported an average decline of 8% from 1970 to 2014. The number of species experiencing positive and negative trends was equal, suggesting that the magnitude of declining trends marginally exceeded that of the increasing trends. This aggregate metric, however, masked the substantial variation in the directionality and magnitude of temporal trends of abundance ( WWF Canada 2017 ), especially for those scientifically assessed as at-risk under Canadian legislation. Importantly, advancing the understanding of factors that predict trends in species abundance is valuable for appropriately guiding conservation decisions in Canada, especially for species at greatest risk of extinction.

To date, a single study has investigated threat profiles for biodiversity in Canada ( McCune et al. 2013 ). However, in this study, threat profiles were restricted to species with published recovery strategies required under the Species at Risk Act (SARA), as opposed to the broader assemblage of scientifically assessed at-risk species. Moreover, the relationship between population trends and threat profiles has yet to be examined, and no specific analysis of large-scale associations between predictor variables and population trends exists exclusively for Canada.

Methods

Data collection We used a subset of the previously compiled data underlying the Canadian LPI. These data are also included in the LPI Data Portal, one of the largest repositories of data containing changes in vertebrate abundance over time (http://www.livingplanetindex.org/data_portal). There are strict criteria for the inclusion of population time series in the LPI (Collen et al. 2009). For instance, populations must be consistently monitored in the same location using similar methods for at least two years since 1970. Here, we restricted our analysis to native species, or where appropriate, Designatable Units (DUs) that had been assessed as Special Concern, Threatened, or Endangered by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) as of May 2018 (sararegistry.gc.ca). DUs are recognized as both discrete and significant units (species, subspecies, or geographic or genetic units) that are irreplaceable components of Canada’s biodiversity (COSEWIC 2015). The data set used also contains confidential records (3.21%) that are not publicly available online but were available to the authors as data holders of the Canadian LPI.1 The subset of national LPI data includes 591 population time series (1970–2016), encompassing 180 COSEWIC-assessed at-risk species or DUs. Population time series had broad spatial coverage across Canada (Fig. S1), though an abundance of data records for marine fish were prominent within the Atlantic Canadian Exclusive Economic Zone. In addition, 35 bird population time series (each corresponding to a single species) represented long-term, nationwide trends.

Population modelling We used the LPI methodology (Collen et al. 2009) to calculate trends of designated at-risk vertebrate population time series in Canada using the publicly available rlpi R package (Freeman et al. 2017). In modelling trends, we treated population counts of zero as missing values, resulting in conservative estimates of change (Marconi et al. in preparation). Changes in population abundance were calculated using a geometric mean of relative abundance (Collen et al. 2009) from 1970 to 2016. We employed two methods to generate index values as per Collen et al. (2009). For population time series containing ≥6 data points, we modelled trends using a Generalized Additive Model (GAM) and fitted GAM values were used to interpolate values for all years between the start and end year of the time series. Alternatively, we applied log-linear interpolation (chain method) to shorter time series or to those that resulted in a poor GAM fit (Loh et al. 2005; Collen et al. 2009). On average, 3.28 population time series contributed to a species or DU, with variation among taxa (Fig. S2). For instance, fish had on average, 4.24 more population time series contributing to an individual species or DU compared with birds, which often had complete geographic coverage within a singular nationwide trend. Averaged population time-series length (number of years between first and last data point) was 18.0 years, and fullness (number of years within a time series that had a measured (non-interpolated) value) was 14.0 years.

Threat profiles In Canada, threats to at-risk species are identified in scientific assessments conducted by COSEWIC using the best available science and Indigenous Knowledge (COSEWIC 2016). Using threat information from the most recent COSEWIC assessment reports, researchers from the University of Ottawa (Findlay and McKee 2018) constructed species-specific threat profiles, according to the International Union for the Conservation of Nature (IUCN) Threat Classification Version 3.2 (Salafsky et al. 2008). For each species or DU, the description of threats listed within the most recent COSEWIC status reports were extracted verbatim and independently evaluated by two to three University of Ottawa reviewers. Evaluators used the explicit IUCN threat terminology (e.g., “Biological resource use”) and a set of related terms (e.g., “harvest”, “fishing”, and “hunting”), to obtain a binary classification of presence or absence for each of the 11 IUCN threat categories. The analysis was restricted to those taxa—birds, mammals, fish (marine and freshwater), and herpetofauna (reptiles and amphibians)—for which there was both threat profile information and temporal abundance data. Threat profiles are specific to the COSEWIC-assessed species or DUs and often lack the geographic specificity of individual LPI population time series. However, of the population time series in our data set, 45 were obtained directly from COSEWIC Status Reports, and another 43 were derived from Canada-wide or provincial bird surveys with data covering the entirety of the species’ range. In principle, these population trends therefore match the scale of the corresponding threat profiles. In addition, half (n = 294) of the population time series were contributed by Fisheries and Oceans Canada, provincial assessments, and other sources that collectively covered the entirety of the geographic distribution for 53 species. In total, nearly two-thirds of the data set (both species threat profiles and population time series) were comparable at geographical scale. For the remaining one-third, species or DUs were based on 1–24 population time series, with an average of 2.43 time series per species, encompassing various degrees of geographic coverage. As LPI populations are currently recorded as point localities, it is difficult to measure the exact spatial overlap between LPI time series and species or DUs when the LPI doesn’t cover the whole of the species’ range. Importantly, the availability of range-wide temporal abundance data is limited, and the data set used here contains the best available Canadian data for this type of analysis.

Analyzing threat profiles Given that multiple threats are often acting in synergy (Brook et al. 2008), it is difficult and arguably inefficient to disentangle stressors to analyze each individually. Accordingly, we used logistic principal component analysis (PCA) for binary data using the logisticPCA (Landgraf and Lee 2015) package for dimensionality reduction of correlated binary threats into principal components. Analyses were conducted using the statistical software R (R Core Team 2017). Parameters were fit using a two-dimensional representation (k = 2) and optimal m, calculated as the minimum value of the negative log likelihood for m ranging from 1 to 10 (m = 4). Species threat profiles were approximated using two principal components (a two-dimensional composite threat profile).