Modelling temperature‐driven changes in species associations across freshwater communities

Perrin, Sam; Veen, Bert van der; Golding, Nick; Finstad, Anders G.

doi:10.1111/gcb.15888

Cited by 11 publications

(16 citation statements)

References 83 publications

(65 reference statements)

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“…However, how to disentangle the effects of biotic interactions from unmeasured environmental variables remains unclear and requires empirical tests using species with known interactions (Zurell et al, 2018). Furthermore, JSDMs have rarely been applied to freshwater communities (e.g., Burgazzi et al, 2020; Perrin et al, 2022; Wagner et al, 2019), and, to the authors' best knowledge, only once on freshwater mussels (Inoue et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Joint species distribution models unveil co‐occurrences between freshwater mussels and their fish hosts

Silva

Gonçãlves

Garcia‐Raventós

et al. 2023

Journal of Biogeography

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Aim Freshwater mussels are among the most threatened taxa in the world, partially due to the dependence on fish hosts to complete their life cycle. Knowledge about the role of environmental and biotic drivers in determining mussels' distribution is currently lacking. We aimed to assess the role of environmental and biotic drivers in determining the distribution of mussels and their fish hosts and to test if co‐occurrence patterns were able to identify mussel‐host interactions. Location Douro River basin (Iberian Peninsula). Taxon Four freshwater mussels and ten fish hosts. Methods Joint species distribution models (JSDMs) were fitted to presence‐absence records for mussel and fish assemblages. Variance partitioning among environmental variables and latent variables was conducted to determine the environmental versus biotic drivers of species distributions. Resulting matrices of pairwise species co‐occurrences were used to identify co‐occurrence patterns. Results The distribution of host generalist mussel species was mainly explained by environmental variables related to climate and topography. The distribution of the host specialist Margaritifera margaritifera was mainly explained by land use. Strong positive correlations between mussels and the most relevant fish hosts were consistently captured by JSDMs. Co‐occurrence patterns were mainly explained by residual factors, indicating the potential role of biotic interactions. Main Conclusions Biotic interactions were expected to play an important role in explaining mussels' distribution, but the contribution of this factor was only meaningful for the host specialist M. margaritifera. Correlations between mussels and suitable hosts allowed to infer important fish hosts for freshwater mussels in the Douro River basin from distributional data alone. By finding similarities between the ecological requirements of co‐occurring species, conservation measures can be oriented towards several species, which brings a more holistic perspective to the protection of biodiversity.

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Section: Introductionmentioning

confidence: 99%

Joint species distribution models unveil co‐occurrences between freshwater mussels and their fish hosts

Silva

Gonçãlves

Garcia‐Raventós

et al. 2023

Journal of Biogeography

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“…Thus, understanding how interspecific spatiotemporal associations change across human disturbance gradients provides valuable insight into the long-term implications of human impacts on ecosystem function and recovery relevant to biodiversity conservation in the Anthropocene. Species associations are not necessarily fixed as positive or negative, but can vary along environmental gradients (Davis et al, 2018;Pellissier et al, 2018;Perrin et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Species associations are not necessarily fixed as positive or negative, but can vary along environmental gradients ( Davis et al, 2018 ; Pellissier et al, 2018 ; Perrin et al, 2022 ). Associations between free-ranging species are especially context-dependent ( Chamberlain et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a key challenge in community ecology is to identify the conditions under which negative and positive species associations are more likely to occur ( Beaudrot et al, 2020 ). Understanding how species associations vary along environmental conditions is pivotal in making robust ecological predictions about biodiversity responses to changing environmental conditions ( Dangles et al, 2018 ; Perrin et al, 2022 ). For example, alpine bird communities have a higher frequency of positive interactions in grasslands with low productivity compared with in forests with high productivity ( García-Navas et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Human disturbance increases spatiotemporal associations among mountain forest terrestrial mammal species

Li,

Bleisch,

et al. 2023

Preprint

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Spatial and temporal associations between sympatric species underpin biotic interactions, structure ecological assemblages, and sustain ecosystem functioning and stability. However, the resilience of interspecific spatiotemporal associations to human activity remains poorly understood, particularly in mountain forests where anthropogenic impacts are often pervasive. Here we applied context-dependent Joint Species Distribution Models to a systematic camera-trap survey dataset from a global biodiversity hotspot in eastern Himalaya to understand how prominent human activities in mountain forests influence species associations within terrestrial mammal communities. We categorized anthropogenic pressures into chronic human modification and acute direct human presence and compared their effects on spatiotemporal associations among species. We obtained 10,388 independent detections of 17 focal species (12 carnivores and five ungulates) from 322 stations over 43,163 camera days of effort. We identified a higher incidence of positive associations in habitats with higher levels of human modification (87 %) and human presence (83 %) compared to those located in habitats with lower human modification (64 %) and human presence (65 %) levels. We also detected a significant reduction of pairwise encounter time at increasing levels of human disturbance (human modification: mean = −1.07, 95% CI = −1.39 to −0.73; human presence: mean = −0.69, 95% CI = −0.92 to −0.46), corresponding to more frequent encounters between pairs of species. Our findings indicate that human activities can push mammals together into more frequent encounters and associations, which likely influences the coexistence and persistence of wildlife, with potential far-ranging ecological consequences. With increasing human presence and human modification of areas throughout the world, identifying thresholds of anthropogenic activity that shift species relationships, limiting human activity and increasing landscape connectivity across protected areas may be imperative to maintain interspecific spatiotemporal associations that underpin ecosystem resilience.

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“…These dependencies enable the borrowing of information across data that may be scarce in time or space in order to infer population characteristics. However, JSDMs have rarely been applied to inland fisheries and those that have are limited to investigating patterns and drivers of species occurrence, not abundance (Inoue et al., 2017; Perrin et al., 2022; Wagner et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Accounting for spatiotemporal sampling variation in joint species distribution models

North,

Schliep,

Hansen

et al. 2023

Journal of Applied Ecology

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Estimating relative abundance is critical for informing conservation and management efforts and for making inferences about the effects of environmental change on populations. Freshwater fisheries span large geographic regions, occupy diverse habitats and consist of varying species assemblages. Monitoring schemes used to sample these diverse populations often result in populations being sampled at different times and under different environmental conditions. Varying sampling conditions can bias estimates of abundance when compared across time, location and species, and properly accounting for these biases is critical for making inferences. We develop a joint species distribution model (JSDM) that accounts for varying sampling conditions due to the environment and time of sampling when estimating relative abundance. The novelty of our JSDM is that we explicitly model sampling effort as the product of known quantities based on time and gear type and an unknown functional relationship to capture seasonal variation in species life history. We use the model to study relative abundance of six freshwater fish species across the state of Minnesota, USA. Our model enables estimates of relative abundance to be compared both within and across species and lakes, and captures the inconsistent sampling present in the data. We discuss how gear type, water temperature and day of the year impact catchability for each species at the lake level and throughout a year. We compare our estimates of relative abundance to those obtained from a model that assumes constant catchability to highlight important differences within and across lakes and species. Synthesis and applications: Our method illustrates that assumptions relating indices of abundance to observed catch data can greatly impact model inferences derived from JSDMs. Specifically, not accounting for varying sampling conditions can bias inference of relative abundance, restricting our ability to detect responses to management interventions and environmental change. While our focus is on freshwater fisheries, this model architecture can be adopted to other systems where catchability may vary as a function of space, time and species.

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Modelling temperature‐driven changes in species associations across freshwater communities

Cited by 11 publications

References 83 publications

Joint species distribution models unveil co‐occurrences between freshwater mussels and their fish hosts

Joint species distribution models unveil co‐occurrences between freshwater mussels and their fish hosts

Human disturbance increases spatiotemporal associations among mountain forest terrestrial mammal species

Accounting for spatiotemporal sampling variation in joint species distribution models

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