Consistent edge effect patterns revealed using continuous surveys across an Eastern Mediterranean no-take marine protected area

Question

1. How can UVC be improved for spatial assessment of MPAs performance?

2. What biases may impact fish differentially within and outside MPAs?

3. What spatial patterns were observed in MPA borders?

4. What are the spatial variations within and outside Marine Protected Areas (MPAs) and their impact on fish populations and non-indigenous species (NIS)?

Accepted Answer

To improve UVC for spatial assessment of MPAs performance, it is recommended to move away from a simplistic in/out sampling design and spread the sampling sites more evenly over larger areas. Additionally, analyzing the data continuously can provide better spatial coverage. This approach can help detect clear and consistent spatial patterns associated with grouper species and reveal gradients of decreasing biomass across the MPA border. Increasing the sample size can produce steeper slopes, indicating a larger edge effect and a lower spillover distance. By implementing these improvements, UVC can be more frequently used as a tool for spatial assessment of MPAs performance.

Accepted Answer

Biases that may impact fish differentially within and outside MPAs include fish avoidance behavior towards divers or boat noises. Fish in fished areas may avoid divers or boat noises to a larger degree than fish inside the MPA. Controlling for these biases is beyond the scope of this study and further research is needed to evaluate their effect on patterns of fish biomass. This differential behavior may lead to under or overestimations of biomass depending on the fish community composition. The study used the same sampling protocols inside the MPA and in fished areas, minimizing the impact of these biases on observed spatial patterns. However, it is important to consider these biases when interpreting the results and conducting further research in this area.

Accepted Answer

The study documented spatial patterns of fishes across MPA borders using three non-destructive methods. Strong edge effects were found in Yam Rosh Hankira no-take MPA, and a pattern consistent with fishing the line was observed. These patterns highlight the risks of overfishing even in well-enforced no-take MPAs, emphasizing the need for additional measures like buffer areas for long-term sustainability. Continuous spatial monitoring of MPAs and exploited fish populations is also crucial.

Accepted Answer

Spatial variations within and outside MPAs are critical for understanding MPA functioning and benefits to fisheries. The efficacy of MPAs varies across their area, and their impact on fish populations is related to the distance from MPA borders. Inside MPAs, an edge effect results in a reduction of fish biomass in the periphery due to fishing along the MPA borders. Spillover, the export of fish from within the MPAs to adjacent fished areas, contributes to fisheries. To study spatial variations, sampling over large areas with high spatial resolution is needed. Spatial variations may also elucidate interactions between MPAs and NIS. The biotic resistance hypothesis suggests that diverse communities within MPAs are more resistant to invasions by NIS. However, protection from fishing offered by MPAs may also serve as a favorable environment for NIS. Empirical studies have shown both positive and negative responses of NIS to protection. Detecting spillover, edge effects, and species invasion requires considering spatial variations within the MPA. The prevailing approach to monitoring MPAs is underwater visual censuses (UVC), but due to resource limitations, it is seldom applied on the spatial scales needed to evaluate edge effect and spillover. Acoustic methods and towed-diver surveys (TDS) can sample large areas relatively quickly and provide estimates of fish density and biomass, but they have limitations in species-level data and detection of small-sized and cryptic fish. The study of Yam Rosh Hanikra, an enforced no-take MPA in the Eastern Mediterranean Sea, aims to examine the effect of the MPA on fish communities, describe spatial patterns across the MPA borders, and investigate the impact of fish characteristics such as commercial value, body size, and species origin. The goals are to examine the effect of the MPA on fish biomass and species composition, describe spatial patterns continuously across the MPA borders, examine the impact of fish characteristics, and compare sampling effort among methods. Quantifying continuous patterns across MPA borders can provide a deeper understanding of MPA functioning.

Accepted Answer

Effective enforcement of regulations in the Yam Rosh Hanikra MPA started in 2005. Prior to this, only recreational fishing from shore was permitted within the MPA. In 2017, recreational fishing was further restricted, turning most of the MPA area into a no-take zone. The MPA was significantly expanded in 2019 to protect the deep areas of the Achziv underwater canyon. Our study refers to the MPA borders prior to the expansion, which overlaps the international border between Israel and Lebanon. Military restrictions on both sides of the border suggest that the northernmost part of the MPA is least exposed to fishing activities.

Accepted Answer

Underwater Visual Census (UVC) is a visual, non-destructive method that provides individual-level data. It involves surveys conducted by SCUBA divers who record fish species, abundance, and total length (TL) of the benthic and benthic-pelagic fish community. The data is collected through belt transects, and fish TL is converted into biomass using a length-weight formula. UVCs are used to monitor fish communities in marine protected areas (MPAs) and can be compared between different methods and habitats.

Accepted Answer

Acoustic surveys are a non-intrusive and non-destructive method to study fish populations in their natural habitat using active sound waves. They provide valuable information about fish distribution, density, and biomass. The surveys are conducted using echosounders, which emit sound waves and analyze the echoes produced by fish. By analyzing the echoes, researchers can estimate the size, abundance, and distribution of fish populations. Acoustic surveys are particularly useful in areas where traditional sampling methods may be challenging or disruptive, such as in rocky reefs or protected marine areas. The data obtained from acoustic surveys can be used to inform management decisions, assess the effectiveness of conservation measures, and monitor changes in fish populations over time.

Accepted Answer

In TDS, fish biomass is estimated by visually documenting fish schools and large-bodied fish, focusing on species, total length (TL), and number of individuals. Two invasive herbivorous fish are assigned to the genus level, while small-bodied local reef fish are excluded. The habitat surveyor evaluates habitat complexity using three metrics: habitat type, habitat complexity on a scale from 0 to 5, and presence of special rock formations. Habitat type is converted into numerical values, and the index of total habitat complexity is calculated. The visual records of fish are converted into biomass using the same procedure as in UVC. Data is then integrated into 50 m intervals, based on the boat's distance traveled every minute during the survey, providing an estimation of fish biomass in 250 m^2.

Accepted Answer

Two complexity indices were calculated based on a high-resolution bathymetric map. The first index, slope-calculated, was based on each pixel's eight neighboring pixels. The second index, Bathymetric Position Index (BPI), quantifies depth relative to the overall seascape. BPI values were tested in models, but original values were used due to no difference in results. Both indices provide valuable insights into the environmental data's complexity and depth variations.

Accepted Answer

Spatial patterns of fish were modeled using general additive models (GAMs) from the 'mgcv' R package. The models included distance from the MPA border as a continuous predictor and relevant response variables such as NASC in the acoustic dataset and biomass in the visual datasets. Random effects were included for UVC-campaign, observer ID, acoustic surveys-survey ID, and TDS-observer ID. Thin plate spline (TPS) was used for smoothing the main predictor, and the number of basis functions was adjusted for minimal wiggliness. The models used a gaussian distribution family and were evaluated based on AIC and deviance explained. Environmental factors like water depth and habitat complexity were also included as fixed effects in the models. The effect of fish characteristics on the spatial pattern of fish biomass was tested using the 'by' argument in the GAMs.

Accepted Answer

Edge effect distance is measured as the distance from where the point of highest biomass within the MPA starts to decrease to the MPA border. This measurement helps estimate the impact of the MPA on the surrounding biomass. The distance is crucial in understanding the spatial patterns and the extent of edge effects in marine protected areas (MPAs). By quantifying this distance, researchers can assess the effectiveness of MPAs in preserving marine biodiversity and ecosystem health. The methodology involves analyzing the biomass data collected from the MPA and its surroundings, identifying the point of maximum biomass within the MPA, and measuring the distance to the MPA border where the biomass starts to decline. This approach provides valuable insights into the spatial dynamics of marine ecosystems and aids in the development of effective conservation strategies.

Accepted Answer

Biomass estimates inside and outside the MPA vary based on the method used. The traditional dichotomous approach showed strongest differences in UVC data and weakest in acoustic data. However, these differences were generally non-significant for all methods. Mean biomass estimates using visual methods, UVC, and TDS were high in the MPA core, decreased close to the border, and were lowest in the fished areas close to the MPA border. Biomass in the MPA core was approximately 200% higher than in adjacent fished areas. In the acoustic data, biomass in the MPA core was about 100% higher than in the border area and adjacent fished areas. Biomass estimates in distant fished areas exceeded those in the MPA core. UVC surveys had 3-4 times higher estimates than TDS, while acoustic methods had an order of magnitude lower estimates. Species composition also varied, with Siganus spp. and indigenous groupers showing higher biomass inside the MPA compared to fished areas, while Chromis chromis had higher biomass in fished areas. Overall, 61% and 81% of the total fish biomass in the UVC data and 85% and 94% in the TDS data were inside the MPA compared to fished areas.

Accepted Answer

Inside the MPA, an almost linear decrease in fish biomass/NASC in log 10 scale was observed in all three methods. The highest values were recorded in the northernmost part of the MPA, decreasing across the MPA border. This pattern is consistent with an edge effect that extends to the innermost part of the MPA. Modelling based on acoustic density and biomass produced similar patterns to NASC. The spatial patterns of fish outside the MPA were also similar among methods, with slight changes observed in biomass distribution.

Accepted Answer

Fish characteristics, such as species composition and commercial value, significantly impact the spatial patterns observed in Marine Protected Areas (MPAs). The study found that the dominant pattern of high biomass in the MPA core, decreasing towards the border and fished areas, is mainly associated with species of commercial value. Models including commercial value showed a stronger MPA effect compared to models with only distance, depth, and habitat complexity. The spatial patterns based on the TDS data largely followed the pattern obtained for Siganus spp. alone, while the patterns based on the UVC data resembled the spatial pattern obtained for groupers alone. The UVC method recorded large and commercially valuable grouper species better than the TDS method. However, the relative proportions of species recorded by each method differed, with groupers being favored by spatial protection and Siganus spp. having lower commercial demand. The study also found no clear evidence for biotic resistance, as the biomass of Siganus spp. did not decrease with increasing distance from the MPA border. Instead, a gradual increase in biomass was observed, potentially due to decreased fishing pressure and lower predation by groupers.

Accepted Answer

In the study, the acoustic method sampled an order of magnitude larger area and volume compared to the UVC method, with fewer sampling days and people-days. The TDS method covered twice the area of UVC transects, requiring fewer days and people. UVC was found to be the most costly per area surveyed, followed by the acoustic method, while TDS was the least costly. This comparison highlights the efficiency and cost-effectiveness of different sampling methods, providing valuable insights for researchers in selecting the most suitable approach for their studies.

Accepted Answer

Three different methods were used to examine the effect of Yam Rosh Hanikra MPA on fishes. These methods included a dichotomous approach (in/out), and a continuous approach, examining spatial patterns across the MPA into fished areas as far as 6 km from the MPA border. The findings from these methods supported the prevalence of strong edge effects and minor spillover even in a well-enforced no-take MPA. Continuous sampling in the spatial domain was highlighted as necessary to reveal such patterns. The traditional in/out approach showed higher biomass values in the MPA core relative to adjacent fished areas, although these differences were non-significant due to large spatial variability in biomass estimates. Spatial sampling was suggested as a potential solution to overcome this challenge and reveal areas of high and low biomass both within and outside the MPA. The increase of commercial grouper species was noted as a benefit of MPA protection in terms of species composition.

Accepted Answer

The spatial pattern of fish biomass in the Yam Rosh Hanikra MPA shows a general trend of high biomass in the northernmost part of the MPA, decreasing towards and across the MPA southern border, and lowest in the adjacent fished areas. This indicates that the real core of the MPA, with the highest biomass, is not in the center but in the northernmost area. The MPA may be experiencing edge effects due to intensive fishing along the border, leading to a reduction of approximately 50% in biomass along 4 km from the northernmost part to the southern border. Outside the MPA, the biomass pattern continues to decrease up to 2-3 km outside the border, with no significant pattern of spillover detected. However, a surprising pattern of high biomass 6 km from the MPA border was observed using acoustic surveys and TDS data, which could be due to increased biomass unrelated to fishing, higher fishing activity closer to the MPA border, or differential response of species to protection and fishing. Habitat complexity plays an important role in shaping fish distribution and abundances, with species like C. chromis and Siganus spp. being better associated with small-scale rock features. The effect of habitat complexity on the spatial pattern of biomass varies depending on the survey method used.

Consistent edge effect patterns revealed using continuous surveys across an Eastern Mediterranean no-take marine protected area

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Most frequently asked questions

1. How can UVC be improved for spatial assessment of MPAs performance?

2. What biases may impact fish differentially within and outside MPAs?

3. What spatial patterns were observed in MPA borders?

4. What are the spatial variations within and outside Marine Protected Areas (MPAs) and their impact on fish populations and non-indigenous species (NIS)?

5. When was effective enforcement of MPA regulations started?

6. What is Underwater Visual Census (UVC) method?

7. What is the purpose of using acoustic surveys in studying fish populations?

8. How is fish biomass estimated in TDS?

9. What complexity indices were calculated for the environmental data?

10. How were spatial patterns of fish modeled?

11. How is edge effect distance measured?

12. How do biomass estimates differ inside and outside the MPA?

13. What spatial patterns of fish biomass/NASC were observed inside the MPA?

14. How do fish characteristics affect MPA patterns?

15. How do sampling methods compare in terms of effort and cost?

16. What methods were used to examine the effect of Yam Rosh Hanikra MPA on fishes?

17. What is the spatial pattern of fish biomass in the Yam Rosh Hanikra MPA?

Citations

Evaluating the long term effectiveness of a Mediterranean marine protected area to tackle the effects of invasive and range expanding herbivorous fish on rocky reefs.

References

R: A language and environment for statistical computing.

Fast stable direct fitting and smoothness selection for generalized additive models

Generalized Additive Models

Cube law, condition factor and weight-length relationships: history, meta-analysis and recommendations

The Ecology of Invasions by Animals and Plants

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