Who will kill them first, Man or Climate? · Who will kill them first, Climate or Man? Sreenath, K....
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Who will kill them first, Climate or Man? Sreenath, K. R., Gyanaranjan Dash, Mohammed Koya, K. , Swatipriyanka Sen, Vinayakumar, V., Divu, D., Pradhan, R., Sukhadhane, K., Sonia Kumari, Makwana Nayan, P., Joshi, K. K. and Zacharia, P. U. Regional Centre of ICAR - Central Marine Fisheries Research Institute, Veraval, Gujarat-362269 1. Background It is evident that the variability of fish abundance in the sea has been increasing over time. This variability might be due to climate factors or anthropogenic factors such as fishing. Challenge of the modern fishery management is to distinguish between the two and take account of these sources of variability. As all of the marine fisheries resources of India are currently under exploitation, there is no control group available to distinguish climate effects from fishing effects. Here ,we are utilizing methods involving statistical controls for this purpose. 4. Results continued.. Fig.1. Significant Beta weights (β) of model fitted for each fish species’ abundance with SST and fishing effort. Complete dominance weights showed that fishing effort contributes more variance than SST (Fig. 2) . Fig.2. Complete dominance weights (CD 0 ) of model fitted for each fish species’ abundance with SST and fishing effort . 2. Aim The goal of this work is to distinguish impact due to fishing on fish abundance from those due to climate , with the hope that former can be controlled where as the latter can be observed and (possibly) predicted. 3. Data and Method Data Abundance – 30 years Catch (Kg) and Effort (fishing days) has been taken from the ICAR-CMFRI database and Catch per unit effort (CPUE) is utilized as a proxy to fish abundance for thirty six (36) commercially important species. Climate variable – The Sea Surface Temperature (SST) data for 2x 2 o grid (69-71 o N, 19- 21 o E) have been gathered from International Comprehensive Ocean-Atmosphere Data Set (ICOADS) for a period of 30 years (1985 to 2014). Method Data analysis has been performed using the R 3.3.2 software. Stepwise linear regression was performed to fit model for the abundance of all the 36 species with SST and fishing effort. ‘Yhat’ package was used for analyzing variable importance using the beta weights (β) and complete dominance (CD0). 5. Conclusion Results of the study confirms that the impact of fishing pressure is very high and more wide spread as compared to that of sea surface temperature on the fish abundance. However, as14% of species have shown SST also as one of the reasons for their population variability, measures to control fishing needs to be increased simultaneous with conservative management measures which can increase resilience of vulnerable species to climate variability. Acknowledgements We express our gratitude to the Director, ICAR-CMFRI for the guidance and facilities put forward for conducting this work. We also thank ICAR and National Innovation on Climate Resilient Agriculture (NICRA) for support and providing fund for this work. -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 Acetes indicus Harpadon nehereus Coilia dussumieri Sardinella longiceps Rastrelliger kanagurta Euthynnus affinis Thunnus tonggol Scomberomorus gutatus Scoliodon laticaudus Otolithes biauritus Protonibea diacanthus Metapanaeus monoceros Metapenaeus affinis Parapenaeopsis stylifera Penaeus semisulcatus Fenneropenaeus merguiensis Solenocera crassicornis Charybdis feriata Portunus sanguinolentus Panulirus polyphagus Trichurus lepturus Pampus argenteus Parastromateus niger P tenuispinis Himantura imbricata Otolithoides cuvieri Beta weights (β) Fish Species Beta weights (β) plot SST Effort 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 A.indicus H.nehereus C.dussumieri S.longiceps R.kanagurta E.affinis T.tonggol S.gutatus S.laticaudus O.biauritus P.diacanthus M.monoceros M.affinis P.stylifera P.semisulcatus P.merguiensis S.crassicornis C.feriata P.sanguinolentus P.polyphagus T.lepturus P.argenteus P.niger P.tenuispinis H.imbricata O.cuvieri Complete dominance (CD 0 ) Fish Species Complete dominance plot SST Effort 4. Results Regression has yielded linear models of all species with significant coefficient of determination. Out of 36 species studied, twenty six (26) species (mostly demersals) showed significant negative β for effort and five (5) species have showed negative β for SST (Fig. 1). Another six (6 ) species have showed positive β with SST. However, for all the 26 species, the negative influence of fishing effort was much higher as compared to that of SST. Presented by Dr. Sreenath, K. R. at 1 st International Agrobiodiversity Congress, 6-9 November , New Delhi Poster No. 1794
Transcript of Who will kill them first, Man or Climate? · Who will kill them first, Climate or Man? Sreenath, K....
Who will kill them first, Climate or Man?
Sreenath, K. R., Gyanaranjan Dash, Mohammed Koya, K. , Swatipriyanka Sen, Vinayakumar, V., Divu,
D., Pradhan, R., Sukhadhane, K., Sonia Kumari, Makwana Nayan, P., Joshi, K. K. and Zacharia, P. U.
Regional Centre of ICAR - Central Marine Fisheries Research Institute, Veraval, Gujarat-362269
1. Background
It is evident that the variability of fish abundance in the sea
has been increasing over time. This variability might be due to
climate factors or anthropogenic factors such as fishing.
Challenge of the modern fishery management is to distinguish
between the two and take account of these sources of variability.
As all of the marine fisheries resources of India are currently
under exploitation, there is no control group available to
distinguish climate effects from fishing effects. Here ,we are
utilizing methods involving statistical controls for this purpose.
4. Results continued..
Fig.1. Significant Beta weights (β) of model fitted for each fish species’ abundance with SST and fishing effort.
Complete dominance weights showed that fishing effort
contributes more variance than SST (Fig. 2) .
Fig.2. Complete dominance weights (CD0) of model fitted for each fish
species’ abundance with SST and fishing effort.
2. Aim
The goal of this work is to distinguish impact due to
fishing on fish abundance from those due to climate , with
the hope that former can be controlled where as the latter can be
observed and (possibly) predicted.
3. Data and Method
Data
Abundance – 30 years Catch (Kg) and Effort (fishing days) has
been taken from the ICAR-CMFRI database and Catch per unit
effort (CPUE) is utilized as a proxy to fish abundance for thirty
six (36) commercially important species.
Climate variable – The Sea Surface Temperature (SST) data for
2x 2 o grid (69-71o N, 19- 21o E) have been gathered from
International Comprehensive Ocean-Atmosphere Data Set
(ICOADS) for a period of 30 years (1985 to 2014).
Method
Data analysis has been performed using the R 3.3.2 software.
Stepwise linear regression was performed to fit model for the
abundance of all the 36 species with SST and fishing effort.
‘Yhat’ package was used for analyzing variable importance
using the beta weights (β) and complete dominance (CD0).
5. Conclusion
Results of the study confirms that the impact of fishing
pressure is very high and more wide spread as compared
to that of sea surface temperature on the fish abundance.
However, as14% of species have shown SST also as one of the
reasons for their population variability, measures to control
fishing needs to be increased simultaneous with conservative
management measures which can increase resilience of
vulnerable species to climate variability.
Acknowledgements
We express our gratitude to the Director, ICAR-CMFRI for the
guidance and facilities put forward for conducting this work. We
also thank ICAR and National Innovation on Climate Resilient
Agriculture (NICRA) for support and providing fund for this work.
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4. Results
Regression has yielded linear models of all species with
significant coefficient of determination. Out of 36 species studied,
twenty six (26) species (mostly demersals) showed significant
negative β for effort and five (5) species have showed negative β
for SST (Fig. 1). Another six (6 ) species have showed positive β
with SST. However, for all the 26 species, the negative influence
of fishing effort was much higher as compared to that of SST.
Presented by Dr. Sreenath, K. R. at 1 st International Agrobiodiversity Congress, 6-9 November , New Delhi Poster No. 1794
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