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Accurate mapping of species distributions is a fundamental goal of modern biogeography, both for basic and applied purposes. This is commonly done by plotting known species occurrences, expert-drawn range maps or geographical estimations derived from species distribution models. However, all three kinds of maps are implicitly subject to uncertainty, due to the quality and bias of raw distributional data, the process of map building, and the dynamic nature of species distributions themselves. Here we review the main sources of uncertainty suggesting a code of good practices in order to minimize their effects. Specifically, we claim that uncertainty should be always explicitly taken into account and we propose the creation of maps of ignorance to provide information on where the mapped distributions are reliable and where they are uncertain.

Maps and models of the distributions of animals and plants are important for assessing their current and future status. Such models rely on information on the environment and occurrence of species. While data on the environment are often easily gathered that on the occurrence of species is often tedious and expensive to collect. An easy way to gather data on species occurrences is to use online platforms such as GBIF or iNaturalist, which rely on the public. This data can be used to produce maps and develop models of the distributions of various animals, such as ants. Even though there are a few in depth studies on the distributions of ant species, knowledge of the distribution and status of many species is lacking. One such species is the widespread ant Manica rubida, which is currently not included in the international Red List. Here, data on the occurrence of M. rubida recorded in online platforms, literature and collected during a field survey were used to develop a map of its distribution and a species model, in order to evaluate its current status. A total of 611 occurrences were found and indicate that this species mainly occurs in the European Alps and other Eurasian mountain ranges. Records of most occurrences were obtained from online platforms and the number increased significantly over the last two decades and indicate this species occurs over an altitudinal range of 3000 m. The species model revealed that there are potential areas of suitable habitat for M. rubida in the Pyrenees, European Uplands, Pindus Mountains, Balkan Mountains and Pontic mountains. Currently, M. rubida does not seem to be threatened by climate change, but it is recommended that the monitoring of its distribution should be continued. This study reveals that data from online platforms can provide the information necessary for developing species models, which can be used to assess the current status and estimate the potential effect of climate change on a species and plan conservation strategies.

The dynamic, multi‐season occupancy model framework has become a popular tool for modeling open populations with occupancies that change over time through local colonizations and extinctions. However, few versions of the model relate these probabilities to the occupancies of neighboring sites or patches. We present a modeling framework that incorporates this information and is capable of describing a wide variety of spatiotemporal colonization and extinction processes. A key feature of the model is that it is based on a simple set of small‐scale rules describing how the process evolves. The result is a dynamic process that can account for complicated large‐scale features. In our model, a site is more likely to be colonized if more of its neighbors were previously occupied and if it provides more appealing environmental characteristics than its neighboring sites. Additionally, a site without occupied neighbors may also become colonized through the inclusion of a long‐distance dispersal process. Although similar model specifications have been developed for epidemiological applications, ours formally accounts for detectability using the well‐known occupancy modeling framework. After demonstrating the viability and potential of this new form of dynamic occupancy model in a simulation study, we use it to obtain inference for the ongoing Common Myna (Acridotheres tristis) invasion in South Africa. Our results suggest that the Common Myna continues to enlarge its distribution and its spread via short distance movement, rather than long‐distance dispersal. Overall, this new modeling framework provides a powerful tool for managers examining the drivers of colonization including short‐ vs. long‐distance dispersal, habitat quality, and distance from source populations.

With the internet, a massive amount of information on species abundance can be collected by citizen science programs. However, these data are often difficult to use directly in statistical inference, as their collection is generally opportunistic, and the distribution of the sampling effort is often not known. In this article, we develop a general statistical framework to combine such \"opportunistic data\" with data collected using schemes characterized by a known sampling effort. Under some structural assumptions regarding the sampling effort and detectability, our approach makes it possible to estimate the relative abundance of several species in different sites. It can be implemented through a simple generalized linear model. We illustrate the framework with typical bird datasets from the Aquitaine region in south-western France. We show that, under some assumptions, our approach provides estimates that are more precise than the ones obtained from the dataset with a known sampling effort alone. When the opportunistic data are abundant, the gain in precision may be considerable, especially for rare species. We also show that estimates can be obtained even for species recorded only in the opportunistic scheme. Opportunistic data combined with a relatively small amount of data collected with a known effort may thus provide access to accurate and precise estimates of quantitative changes in relative abundance over space and/or time.

While multi-species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single-species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross-validation techniques performing the best in terms of prediction.

Aim: Geographic variation in species richness is a well-studied phenomenon. However, the unique response of individual lineages to environmental gradients in the context of general patterns of biodiversity across broad spatial scales has received limited attention. The focus of this research is to examine relationships between species richness and climate, topographic heterogeneity and stream channel characteristics within and among families of North American freshwater fishes. Location: The United States and Canada. Methods: Distribution maps of 828 native species of freshwater fishes were used to generate species richness estimates across the United States and Canada. Variation in species richness was predicted using spatially explicit models incorporating variation in climate, topography and/or stream channel length and stream channel diversity for all 828 species as well as for the seven largest families of freshwater fishes. Results: The overall gradient of species richness in North American freshwater fishes is best predicted by a model incorporating variables describing climate and topography. However, the response of species richness to particular climate or landscape variables differed among families, with models possessing the highest predictive ability incorporating data on climate, topography and/or stream channel characteristics within a region. Main conclusions: The correlations between species richness and abiotic variables suggest a strong influence of climate and physical habitat on the structuring of regional assemblages of North American freshwater fishes. However, the relationship between these variables and species richness varies among families, suggesting the importance of phylogenetic constraints on the regulation of geographic distributions of species.

Background Reliable information on the distribution of target species and influencing environmental factors is essential for effective conservation management. However, ecologists have often derived data from costly field surveys. The Swan Goose ( Anser cygnoides ), a vulnerable Anatidae species, winters almost exclusively in China’s Yangtze River floodplain, but wintering numbers have been steadily decreasing. To better safeguard this unique species, modern modeling approaches can be used to quantify and predict its suitable wintering habitat. Specifically, a potential wintering distribution map of this species is critically important. Methods This study used the maximum entropy approach to model a distribution map of this species. In total, data from 97 up-to-date sites were extracted from 1263 survey sites (excluding duplicate data). After eliminating spatial autocorrelation, 11 environmental variables, including factors related to climate, land structure, vegetation, and anthropogenic activities, were used for model prediction. Results The prediction distribution map shows that the population has concentrated mainly in the boundary area of Anhui, Hubei, and Jiangxi provinces, especially along the Yangtze River. Modeling results suggest that areas within the middle and lower Yangtze River floodplain, such as those in Hunan and Hubei provinces and the eastern coastal area of Zhejiang Province, demonstrate a potential level of “medium” suitability for this species to winter. Conclusions Results from this study provide fundamental information for the restoration and management of the Swan Goose. Our “visualized” potential distribution map can assist in planning optimal conservation strategies, and consequently may help to increase the number of wintering populations in China.

Here, we produce Ambrosia pollen source inventories for Italy that focuses on the periods before and after the accidental introduction of the Ophraella communa beetle. The inventory uses the top–down approach that combines the annual Ambrosia pollen index from a number of monitoring stations in the source region as well as Ambrosia ecology, local knowledge of Ambrosia infestation and detailed land cover information. The final inventory is gridded to a 5 × 5-km resolution using a stereographic projection. The sites with the highest European Infection levels were recorded in the north of Italy at Busto Arsizio (VA3) (European Infection level 2003–2014 = 52.1) and Magenta (MI7) (European Infection level 2003–2014 = 51.3), whereas the sites with the lowest (i.e. around 0.0) were generally located to the south of the country. Analysis showed that the European Infection level in all of Italy was significantly lower in 2013–2014 compared to 2003–2012, and this decrease was even more pronounced at the sites in the area where Ophraella communa was distributed. Cross-validations show that the sensitivity to the inclusion of stations is typically below 1% (for two thirds of the stations) and that the station Magenta (MI7) had the largest impact compared to all other stations. This is the first time that pollen source inventories from different temporal periods have been compared in this way and has implications for simulating interannual variations in pollen emission as well as evaluating the management of anemophilous plants like Ambrosia artemisiifolia.

Habitat modifications driven by human impact and climate change may influence species distribution, particularly in aquatic environments. Niche-based models are commonly used to evaluate the availability and suitability of habitat and assess the consequences of future climate scenarios on a species range and shifting edges of its distribution. Together with knowledge on biology and ecology, niche models also allow evaluating the potential of species to react to expected changes. The availability of projections of future climate scenarios allows comparing current and future niche distributions, assessing a species’ habitat suitability modification and shift, and consequently estimating potential species’ reaction. In this study, differences between the distribution maps of 406 marine species, which were produced by the AquaMaps niche models on current and future (year 2050) scenarios, were estimated and evaluated. Discrepancy measurements were used to identify a discrete number of categories, which represent different responses to climate change. Clustering analysis was then used to automatically detect these categories, demonstrating their reliability compared to human supervised classification. Finally, the distribution of characteristics like extinction risk (based on IUCN categories), taxonomic groups, population trends and habitat suitability change over the clustering categories was evaluated. In this assessment, direct human impact was neglected, in order to focus only on the consequences of environmental changes. Furthermore, in the comparison between two climate snapshots, the intermediate phases were assumed to be implicitly included into the model of the 2050 climate scenario.

The piscivorous chub (Opsariichthys uncirostris uncirostris) has widely invaded Kyushu Island in Japan, and its presence in irrigation ditches known as creeks around Ariake Bay has caused particular concern because various native freshwater fishes are also known to exist in the region. In order to examine the habitat characteristics that are related to its occurrence, we developed a species distribution model for piscivorous chub that inhabits creeks in the Kase river catchment by using geographic and habitat variables that were both biotic and abiotic. We then evaluated the model by using a different data set from the adjacent Chikugo river catchment. The resulting multiple logistic regression model, whose performance was supported by a high value of 0.881 for the area under the receiver operating characteristics curve (AUC), indicated that the occurrence of piscivorous chub was strongly affected by the watercourse distance from the source populations in the Kase river. The model's performance was still high (AUC = 0.792) when tested with the data set from the Chikugo river catchment. We also produced a GIS map that projects the predicted distribution of piscivorous chub across all creeks within the Kase river catchment. The result is likely to reflect the connectivity between static and lentic habitat and is not merely a question of the simple distance from the source populations. We also discuss how the potential distribution map can be applied to the management of piscivorous chub.