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1. Using temporal occupancy to predict avian species distributions

   https://doi.org/10.1111/ddi.13296  

   Snell Taylor, Sara ; Di Cecco, Grace ; Hurlbert, Allen H. ; Liu, ed., Xuan ( May 2021 , Diversity and Distributions)

   Species distribution models (SDMs) are ubiquitous in ecology to predict species occurrence throughout their range. Typically, SDMs are created using presence‐only or presence–absence data. We hypothesize that the continuous metric of temporal occupancy, the proportion of time a species is observed at a given site, provides more detail about species occurrence than binary presence‐based SDMs.

   North America.

   We compared SDMs for 189 focal species using four modelling methods to determine whether North American avian species distributions are better predicted using temporal occupancy over presence–absence. We used the North American Breeding Bird Survey and built SDMs based on all sites sampled consecutively between 2001 and 2015, as well as on a subset of only five time points within the 15‐year sampling window. Each model used the same environmental inputs to predict species range. Each SDM was cross‐validated temporally and spatially.

   Species distributions were generally better predicted using temporal occupancy rather than presence–absence when using either a five‐year or fifteen‐year sampling window. Species that occurred in a smaller proportion of their predicted range were particularly better predicted with SDMs using temporal occupancy. Temporal occupancy SDMs had lower false discovery and false‐positive rates but higher false‐negative rates than presence–absence models.

   Temporal occupancy is a valuable metric that can improve predictions of species occurrence for birds and may improve conservation planning and design efforts.

    

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2. Integrated species distribution models to account for sampling biases and improve range‐wide occurrence predictions

   https://doi.org/10.1111/geb.13792  

   Mäkinen, Jussi ; Merow, Cory ; Jetz, Walter ( November 2023 , Global Ecology and Biogeography)

   Species distribution models (SDMs) that integrate presence‐only and presence–absence data offer a promising avenue to improve information on species' geographic distributions. The use of such ‘integrated SDMs’ on a species range‐wide extent has been constrained by the often limited presence–absence data and by the heterogeneous sampling of the presence‐only data. Here, we evaluate integrated SDMs for studying species ranges with a novel expert range map‐based evaluation. We build new understanding about how integrated SDMs address issues of estimation accuracy and data deficiency and thereby offer advantages over traditional SDMs.

   South and Central America.

   1979–2017.

   Hummingbirds.

   We build integrated SDMs by linking two observation models – one for each data type – to the same underlying spatial process. We validate SDMs with two schemes: (i) cross‐validation with presence–absence data and (ii) comparison with respect to the species' whole range as defined with IUCN range maps. We also compare models relative to the estimated response curves and compute the association between the benefit of the data integration and the number of presence records in each data set.

   The integrated SDM accounting for the spatially varying sampling intensity of the presence‐only data was one of the top performing models in both model validation schemes. Presence‐only data alleviated overly large niche estimates, and data integration was beneficial compared to modelling solely presence‐only data for species which had few presence points when predicting the species' whole range. On the community level, integrated models improved the species richness prediction.

   Integrated SDMs combining presence‐only and presence–absence data are successfully able to borrow strengths from both data types and offer improved predictions of species' ranges. Integrated SDMs can potentially alleviate the impacts of taxonomically and geographically uneven sampling and to leverage the detailed sampling information in presence–absence data.

    

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3. The influence of scale-dependent geodiversity on species distribution models in a biodiversity hotspot

   https://doi.org/10.1098/rsta.2023.0057  

   Gerstner, Beth E ; Blair, Mary E ; Bills, Patrick ; Cruz-Rodriguez, Cristian A ; Zarnetske, Phoebe L ( April 2024 , Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)

   Dainton, John (Ed.)

   Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity.

   This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’.

    

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4. Species distributions models may predict accurately future distributions but poorly how distributions change: A critical perspective on model validation

   https://doi.org/10.1111/ddi.13687  

   Piirainen, Sirke ; Lehikoinen, Aleksi ; Husby, Magne ; Kålås, John Atle ; Lindström, Åke ; Ovaskainen, Otso ( February 2023 , Diversity and Distributions)

   Species distribution models (SDMs) are widely used to make predictions on how species distributions may change as a response to climatic change. To assess the reliability of those predictions, they need to be critically validated with respect to what they are used for. While ecologists are typically interested in how and where distributions will change, we argue that SDMs have seldom been evaluated in terms of their capacity to predict such change. Instead, typical retrospective validation methods estimate model's ability to predict to only one static time in future. Here, we apply two validation methods, one that predicts and evaluates a static pattern, while the other measures change and compare their estimates of predictive performance.

   Fennoscandia.

   We applied a joint SDM to model the distributions of 120 bird species in four model validation settings. We trained models with a dataset from 1975 to 1999 and predicted species' future occurrence and abundance in two ways: for one static time period (2013–2016, ‘static validation’) and for a change between two time periods (difference between 1996–1999 and 2013–2016, ‘change validation’). We then measured predictive performance using correlation between predicted and observed values. We also related predictive performance to species traits.

   Even though static validation method evaluated predictive performance as good, change method indicated very poor performance. Predictive performance was not strongly related to any trait.

   Static validation method might overestimate predictive performance by not revealing the model's inability to predict change events. If species' distributions remain mostly stable, then even an unfit model can predict the near future well due to temporal autocorrelation. We urge caution when working with forecasts of changes in spatial patterns of species occupancy or abundance, even for SDMs that are based on time series datasets unless they are critically validated for forecasting such change.

    

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5. Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

   https://doi.org/10.1111/gcb.14728  

   Requena‐Mullor, Juan M. ; Maguire, Kaitlin C. ; Shinneman, Douglas J. ; Caughlin, Timothy Trevor ( July 2019 , Global Change Biology)

   Species distribution models (SDMs) that rely on regional‐scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local‐scale anthropogenic variables, including wildfire history, land‐use change, invasive species, and ecological restoration practices can override regional‐scale variables to drive patterns of species distribution. Incorporating these human‐induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentataNutt.) as a model species to explore whether including human‐induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field‐sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: −6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: −47.9%, −41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land‐use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.

    

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