Article impact statement: Combining native and non‐native species to evaluate biodiversity is overly simplistic and may undermine the conservation of ecosystems.

Summary The conservation of wetlands, many threatened by human activities, is paramount to sustaining global biodiversity. Yet the protection of targeted wetlands may not be sufficient to protect the species they host because some species may also be impacted by alterations to the surrounding landscape. Some black‐headed gull (Chroicocephalus ridibundus: Laridae) populations have experienced a sharp decline in population size and number of colonies. Here, we investigated the relative contributions of wetland and its surrounding landscape to two major determinants of population dynamics, i.e. habitat selection and reproductive success, using 37 years of monitoring data. Our analyses revealed that large areas of cultivated land surrounding ponds and high vegetation cover of helophytes promote longer occupation of a pond by gull colonies, probably because they allow better reproductive success. We also found that both agricultural practices in surrounding landscapes and pond vegetation cover have sharply changed over the past 30 years in the study area, with an intensification of the former and a general decrease of the latter. These alterations are likely to have led to the observed decrease in the number of breeding gulls. The decrease in helophyte cover may have reduced their ability to construct nests in vegetation that protects them against flooding, and agricultural intensification may have decreased food availability during the crucial period of reproduction. Our study provides additional evidence, from long‐term changes in habitats and reproductive success, that in order to be effective, waterbird conservation plans should consider the terrestrial landscape surrounding wetlands, in addition to the quality of the wetland.

71 Abstract 8 1. Species distribution modelling, which allows users to predict the spatial distribution of species with the 9 use of environmental covariates, has become increasingly popular, with many software platforms providing 10 tools to fit species distribution models. However, the species observations used in species distribution 11 models can have varying levels of quality and can have incomplete information, such as uncertain species 12 identity. 13 2. In this paper, we develop two algorithms to reclassify observations with unknown species identities 14 which simultaneously predict different species distributions using spatial point processes. We compare the 15 performance of the different algorithms using different initializations and parameters with models fitted 16 using only the observations with known species identity through simulations. 17 3. We show that performance varies with differences in correlation among species distributions, species 18 abundance, and the proportion of observations with unknown species identities. Additionally, some of the 19 methods developed here outperformed the models that didn't use the misspecified data. 20 4. These models represent an helpful and promising tool for opportunistic surveys where misidentification 21 happens or for the distribution of species newly separated in their taxonomy. 22 23

Species distribution modeling has been a popular topic in ecological statistics over the past decade. Many tools and methods have been developed to provide a means to explore the distributions of species through mapping of suitable environments (Inoue et al., 2017;

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