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Aim Species distribution maps are essential for assessing extinction risk and guiding conservation efforts. However, most come sourced as expert‐drawn range maps with known issues of accuracy or are developed with overly complex modelling procedures. Thus, data‐driven alternatives that are accessible and reliable are a welcome addition to the spatial conservation toolkit. Here, we developed a geospatial workflow to refine the distribution of a species from its extent of occurrence (EOO) to area of habitat (AOH) within the species range map. The range maps are produced with an inverse distance weighted (IDW) interpolation procedure using presence and absence points derived from primary biodiversity data. Location The Americas (North, South, Central America and the Caribbean). Methods As a case study, we mapped the distribution of 723 resident forest birds in the Americas and assessed their performance in comparison with expert‐drawn range maps. We evaluated differences in accuracy, spatial overlap, range map size and derived AOH. Results The geospatial workflow generated IDW range maps with a higher overall accuracy (87% versus 62%) and fewer errors of omission (<1%) and commission (14%) than the expert range maps (28% both errors). The spatial overlap between both datasets was low (35%), but the agreement increased in areas of high probability of occurrence (68%). We did not find significant differences in range size, but the AOH derived from the expert‐drawn range maps was consistently smaller than the estimates from the IDW range maps. Main Conclusions Our geospatial workflow provides a straightforward approach to accurately map species ranges and the estimation of area of habitat (AOH) for conservation planning and decision‐making. Conversely, procedures that refine expert‐drawn range maps to obtain AOH risk producing biased estimates for local‐scale applications.

Aim: Using survey data for North American birds, we assess how well historical patterns of species richness are explained by stacked species distribution models and macroecological models. We then describe the degree to which projections of future species richness differ, employing both modelling approaches across multiple emissions scenarios. Location: USA and Canada. Methods: We use Audubon Christmas Bird Count and North American Breeding Bird Survey data to estimate current and future species richness of birds using two distinct approaches. In the first, we model richness by stacking predictions from individual species distribution models. In the second, we model richness directly, ignoring the contributions of specific taxa to richness estimates. Results: The two modelling approaches show similar accuracies when validated with historical observations, particularly winter observations, and result in similar patterns of richness when projected onto current and future climate spaces. Patterns of projected change in species richness differed markedly between winter and summer seasons regardless of modelling approach. Our models suggest that bird species richness in winter will increase or remain stable across much of North America. In contrast, species richness in summer is projected to decrease over much of North America, except part of northern Canada, suggesting that climate may constrain many breeding bird species and communities in the future. Main conclusions: Stacked species distribution models and macroecological models produce similar estimates of current and future species richness for each of two seasons despite being built on different concepts of community assembly. Our results suggest that, although the mechanisms that shape geographical variation in biodiversity remain uncertain, these limitations do not impede our ability to predict patterns of species richness at broad scales. Congruence of species richness projections across modelling approaches is encouraging for conservation planning efforts that focus on retaining biodiversity into the future.

Mapping species distributions is crucial to support effective conservation efforts, especially in the Neotropics, which are experiencing rapid and large-scale habitat conversion and degradation. Area of Habitat (AOH) maps have emerged as spatial tools for representing species distributions, indicating potential habitat suitability within a species’ range. We developed AOH maps for 713 neotropical bird species of conservation concern (listed as globally or nationally threatened, endemic, or range-restricted) using primary biodiversity data and a data-driven geospatial workflow. We employed a flagging protocol to improve the quality of approximately 2.5 million occurrence records and manually inspected and validated 50,743 of them. This unparalleled effort led to the creation of high-quality AOH maps, along with altitude-corrected Extent of Occurrence (EOO) and Inverse Distance Weighted (IDW) range maps. Our AOH maps significantly improved species distribution predictions for 94% of species over altitude-corrected EOO maps. The utility of the AOH maps generated here is broad, providing a basis for ecological and evolutionary research as well as the identification of critical areas to prioritize conservation efforts.

Georeferencing error is prevalent in datasets used to model species distributions, inducing uncertainty in covariate values associated with species occurrences that result in biased probability of occurrence estimates. Traditionally, this error has been dealt with at the data‐level by using only records with an acceptable level of error (filtering) or by summarizing covariates at sampling units by using measures of central tendency (averaging). Here we compare those previous approaches to a novel implementation of a Bayesian logistic regression with measurement error (ME), a seldom used method in species distribution modeling. We show that the ME model outperforms data‐level approaches on 1) specialist species and 2) when either sample sizes are small, the georeferencing error is large or when all georeferenced occurrences have a fixed level of error. Thus, for certain types of species and datasets the ME model is an effective method to reduce biases in probability of occurrence estimates and account for the uncertainty generated by georeferencing error. Our approach may be expanded for its use with presence‐only data as well as to include other sources of uncertainty in species distribution models.

To evaluate how factors acting at different spatial scales influence range limits in bird species of the Colombian Andes. Andes Mountains of Colombia. We used M axent, a climate envelope model (CEM), and environmental and geographic information to study range-filling (i.e. the extent to which a species occurs in all the areas in which it is predicted to occur) in 70 range-restricted bird species of the Colombian Andes. Environmental data were taken from the WorldClim database, and species occurrence data were taken from museum data collated by the BioMap project, an observational database, and the literature. We evaluated how climate and geographic barriers may shape range limits at two scales. At a broad extent (i.e. across the three main cordilleras within the Colombian Andes), we find that CEMs predict there to be suitable environmental conditions for particular species in regions where the species is absent, possibly as a result of dispersal limitation or biotic interactions. In contrast, at a finer scale (within a given cordillera), species generally occur across the entire area predicted to be suitable by a given CEM. Geographic discontinuities within cordilleras do not generally correspond to range limits; instead, range limits correspond to changes in environmental conditions. Our results suggest that different mechanisms influence the presence of species at different scales. Dispersal limitation, potentially combined with species interactions, may influence range limits at a broad extent (the entire Colombian Andes), while strong environmental gradients correspond to range limits at a finer scale (within a cordillera).

Despite recent advances in the compilation of primary biodiversity data, biases in the quality and the accessibility of the information difficult the inference of biodiversity spatial patterns. We present the first systematic analysis on the spatial distribution of terrestrial wild mammal records for continental Colombia. By using multiple databases, we identified the geopolitical areas and ecoregions from the country with the biggest information gaps at the order level regarding the number of species recorded vs the number of expected species. In addition, we carried out a complementarity analysis to establish priority sampling areas that maximize the recording of mammal species in the country. Most orders (70 %) show representativeness lower than 50 % in at least 40 % of the departments y 60 % of the studied ecoregions. Also, we found that the temporal coverage in grids of 50 * 50 km tends to below, with an average lower than four sampled years from 1950 to 2019. The complementary analysis shows several areas where sampling would maximize the record of new species at the national level. These areas include tropical forests of the Amazonian region in the limits between Caquetá and Amazonas, the Guyana region, as well as savanna ecosystems from Vichada, Casanare, and Arauca. We advocate for the definition of sampling prioritization schemes for both isolated and relatively unknown areas, as well as areas under high human pressure that could suffer from species losses in the short term. A pesar de los avances en la compilación de registros primarios de especies, sesgos en la calidad y accesibilidad a la información dificultan el estudio de patrones espaciales de biodiversidad. Este estudio presenta el primer análisis sistemático sobre los sesgos en la distribución temporal y espacial de registros de los mamíferos silvestres terrestres continentales de Colombia. Mediante el uso de bases de datos de acceso abierto, identificamos las áreas administrativas y las ecorregiones con los mayores vacíos de información a nivel de orden en cuanto al número de especies registradas con respecto al número de especies esperadas. Además, realizamos un análisis de complementariedad para establecer áreas prioritarias de muestreo que ayuden a disminuir sesgos en los patrones de registro de especies de mamíferos en Colombia. La mayoría de los órdenes (70 %) presentan una representatividad menor al 50 % en al menos 40 % de los departamentos y 60 % de las ecorregiones estudiadas. Además, encontramos que la cobertura temporal en celdas de 50 * 50 km tiende a ser baja, con un promedio inferior a los cuatro años desde 1950 hasta 2019. El análisis de complementariedad muestra que el registro de especies a nivel nacional se maximizaría en los bosques tropicales de la Amazonía en los límites entre el departamento del Caquetá y Amazonas, el escudo Guayanés en la región de la Orinoquia, además de los ecosistemas de sabanas del Vichada, Casanare y Arauca. Proponemos la definición de esquemas de priorización y muestreo sistemático tanto para áreas aisladas y poco conocidas, como para zonas de mayor presión antrópica que pueden sufrir pérdidas locales de especies a corto plazo.

Information on species distribution is recognized as a crucial input for biodiversity conservation and management. To that end, considerable resources have been dedicated towards increasing the quantity and availability of species occurrence data, boosting their use in species distribution modeling and online platforms for their dissemination. Currently, those platforms face the challenge of bringing biology into modeling by making informed decisions that result in meaningful models, based on limited occurrence and ecological data. Here we describe BioModelos, a modeling approach supported by an online system and a core team of modelers, whereby a network of experts contributes to the development of species distribution models by assessing the quality of occurrence data, identifying potentially limiting environmental variables, establishing species' accessible areas and validating modeling predictions qualitatively. Models developed through BioModelos become freely and publicly available once validated by experts, furthering their use in conservation applications. Our approach has been implemented in Colombia since 2013 and it currently consist of a network of nearly 500 experts that collaboratively contribute to enhance the knowledge on the distribution of a growing number of species and it has aided the development of several decision support products such as national risk assessments and biodiversity compensation manuals. BioModelos is an example of operationalization of an essential biodiversity variable at a national level through the implementation of a research infrastructure that enhances the value of open access species data.

Species distribution modeling (SDM) is a booming area of research that has had an exponential increase in use and development in recent years. We performed a search of scientific literature and found 5,533 documents published from 1993 to 2018 using SDM, representing a global network of 4,329 collaborating institutions from 155 countries, with Brazil and Mexico being in the top 10 of the most prolific countries globally. National Autonomous University of Mexico, Chinese Academy of Sciences, University of Kansas, and U.S. Geological Survey are the most prolific institutions worldwide. Latin American institutions (n = 556) participated in 1,000 (18% of global productivity) documents published in collaboration with 591 institutions outside Latin American countries, from which the National Autonomous University of Mexico, Federal University of Goiás, Institute of Ecology A.C., National Scientific and Technical Research Council in Argentina, University of São Paulo, and University of Brasilia were the most productive. From this body of literature, the most frequently modeled taxonomic groups were Chordata and Insecta, and the most common realms of application were conservation planning and management, climate change, species conservation, epidemiology, evolutionary biology, and biological invasions. From the 36 modeling methods identified to generate SDMs, MaxEnt is used in 73.5% of the papers, followed by Genetic Algorithm for Rule-Set Prediction (GARP) with 18.7%, and just 7.4% of the papers compared between 3 and 10 modeling methods. In Latin American countries, productivity in SDM research could be improved as the network of collaborations diversifies and connects with other productive countries (such as United Kingdom, China, Spain, Germany, Australia, and France). The scientific collaboration between Latin American countries should be increased, as the most prolific countries (Brazil, Mexico, Argentina, and Colombia) share less than 10% of its productivity. Some of the main challenges for SDM development in Latin America include bridging the gaps from (a) software use to research productivity and (b) translation to decision-making. To address these challenges, we propose to strengthen communities of practice where modelers, species experts, and decision-makers come together to discuss and develop SDM to shift and enhance current paradigms on how science and decision-making are linked.

Estimates of species’ ranges can inform many aspects of biodiversity research and conservation-management decisions. Many practical applications need high-precision range estimates that are sufficiently reliable to use as input data in downstream applications. One solution has involved expert-generated maps that reflect on-the-ground field information and implicitly capture various processes that may limit a species’ geographic distribution. However, expert maps are often subjective and rarely reproducible. In contrast, species distribution models (SDMs) typically have finer resolution and are reproducible because of explicit links to data. Yet, SDMs can have higher uncertainty when data are sparse, which is an issue for most species. Also, SDMs often capture only a subset of the factors that determine species distributions (e.g., climate) and hence can require significant post-processing to better estimate species’ current realized distributions. Here, we demonstrate how expert knowledge, diverse data types, and SDMs can be used together in a transparent and reproducible modeling workflow. Specifically, we show how expert knowledge regarding species’ habitat use, elevation, biotic interactions, and environmental tolerances can be used to make and refine range estimates using SDMs and various data sources, including high-resolution remotely sensed products. This range-refinement approach is primed to use various data sources, including many with continuously improving spatial or temporal resolution. To facilitate such analyses, we compile a comprehensive suite of tools in a new R package, maskRangeR, and provide worked examples. These tools can facilitate a wide variety of basic and applied research that requires high-resolution maps of species’ current ranges, including quantifications of biodiversity and its change over time.

The conservation of migratory birds poses a fundamental challenge, their conservation requires coordinated action across the hemisphere, but those actions must be designed and implemented locally. To address this challenge, we describe a multilevel framework for linking broad‐scale, full annual cycle prioritizations to local conservation actions for migratory birds. We developed hemisphere‐scale spatial prioritizations for the full annual cycle of migratory birds that breed in six different ecosystems in North America. The full annual cycle prioritizations provide a hemispheric context within which regional priorities can be identified. Finer resolution, regional prioritizations can then inform local conservation actions more effectively. We describe the importance of local conservation practitioner contributions at each level of the process and provide two examples of regional spatial prioritizations that were developed to guide local action. The first example focused on coastal North and South Carolina, USA, and used information on marsh birds, shorebirds, ecological integrity, and co‐benefits for people to identify Cape Romain, South Carolina as a high‐priority site for conservation action. The second example in Colombia used information on migrant and resident birds to identify the Cauca Valley as a high‐priority site. The multilevel conceptual framework we describe is one pathway for identifying sites for implementation of local conservation actions that are guided by conservation priorities for migratory birds across their full annual cycle. A fundamental challenge to migratory bird conservation is translating global scale processes to localized conservation actions. Here we describe a multilevel framework for using hemisphere‐scale, full annual cycle spatial prioritizations to inform on‐the‐ground conservation actions. Our framework illustrates how multi‐scale conservation planning can bring conservation practitioners together to develop locally relevant conservation plans that are in the context of hemispheric perspectives.